system

Abstract

The system according to this embodiment aims to improve awareness and satisfaction in the wider Shibuya area by providing optimal suggestions tailored to the interests and purposes of visitors. [Solution] The system according to the embodiment comprises an analysis unit, a collection unit, a proposal unit, and a dialogue unit. The analysis unit analyzes the interests and purposes of visitors. The collection unit collects information on the wider Shibuya area based on the information analyzed by the analysis unit. The proposal unit makes optimal suggestions based on the information collected by the collection unit. The dialogue unit provides the information proposed by the proposal unit through a character.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there is a problem that the recognition and satisfaction of information other than the tourist attractions in the greater Shibuya area are low, and it is difficult to promote the behavioral changes of visitors.

[0005] The system according to the embodiment aims to make an optimal proposal according to the interests and purposes of visitors and improve the recognition and satisfaction of the greater Shibuya area.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a data collection unit, a proposal unit, and a dialogue unit. The analysis unit analyzes the interests and purposes of visitors. The data collection unit collects information about the wider Shibuya area based on the information analyzed by the analysis unit. The proposal unit makes optimal suggestions based on the information collected by the data collection unit. The dialogue unit provides the information proposed by the proposal unit through a character. [Effects of the Invention]

[0007] The system according to this embodiment can make optimal suggestions tailored to the interests and purposes of visitors, thereby improving awareness and satisfaction in the wider Shibuya area. [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 a plurality of 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 Shibuya AI concierge system, according to an embodiment of the present invention, proposes a region-specific generative AI, "Shibuya AI concierge," to solve the problem that while tourist attractions in the greater Shibuya area are well-known, awareness and satisfaction with dining and shopping are low. Shibuya AI concierge analyzes diverse information such as event information, store information, and weather information for the greater Shibuya area and makes optimal suggestions according to the visitor's interests and purposes. Shibuya AI concierge provides information through characters with different personalities, such as "a woman working in Shibuya" and "a local elderly man who has lived in Shibuya for many years." Visitors can obtain real-time local information and recommended spots through interaction with the characters, enjoying an experience as if they were talking to a local. This can encourage behavioral changes among visitors, improve awareness and satisfaction, and enhance the attractiveness and walkability of the area. For example, with Shibuya AI concierge, visitors input their interests and purposes. Next, the generative AI analyzes event information, store information, weather information, etc. for the greater Shibuya area and makes optimal suggestions according to the visitor's interests and purposes. The suggestions are made through characters such as "a woman working in Shibuya" and "a local elderly man who has lived in Shibuya for many years." Visitors can obtain real-time local information and recommended spots through interaction with these characters. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," Shibuya AI concierge will analyze information on ramen restaurants in the greater Shibuya area and suggest the best ramen restaurant. The suggestions are made through characters such as "a woman working in Shibuya" and "a local elderly man who has lived in Shibuya for many years." Visitors can obtain real-time local information and recommended spots through interaction with these characters. This encourages behavioral changes among visitors, improves awareness and satisfaction, and enhances the attractiveness and walkability of the area. Shibuya AI concierge analyzes diverse information such as event information, store information, and weather information in the greater Shibuya area in order to make optimal suggestions tailored to the interests and purposes of visitors.For example, if a visitor enters "I want to eat delicious ramen in Shibuya," Shibuya AI concierge analyzes information on ramen restaurants in the greater Shibuya area and suggests the best ramen restaurant. The suggestions are made through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." Through interaction with these characters, visitors can obtain real-time local information and recommended spots. This encourages behavioral change among visitors, improves awareness and satisfaction, and enhances the area's appeal and encourages exploration. Shibuya AI concierge analyzes diverse information such as event information, store information, and weather information for the greater Shibuya area to provide optimal suggestions tailored to visitors' interests and purposes. For example, if a visitor enters "I want to eat delicious ramen in Shibuya," Shibuya AI concierge analyzes information on ramen restaurants in the greater Shibuya area and suggests the best ramen restaurant. The suggestions are made through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." Through interaction with these characters, visitors can obtain real-time local information and recommended spots. This will encourage behavioral changes among visitors, improve awareness and satisfaction, and enhance the area's appeal and encourage people to explore. As a result, the Shibuya AI concierge system will be able to provide optimal suggestions tailored to the interests and purposes of visitors.

[0029] The Shibuya AI concierge system according to this embodiment comprises an analysis unit, a data collection unit, a proposal unit, and a dialogue unit. The analysis unit analyzes the interests and purposes of visitors. For example, the analysis unit analyzes text data entered by visitors to identify their interests and purposes. The analysis unit can use generative AI to analyze visitors' input data and extract their interests and purposes. For example, if a visitor enters "I want to eat delicious ramen in Shibuya," the analysis unit will prioritize analyzing information related to ramen. The data collection unit collects information about the wider Shibuya area based on the information analyzed by the analysis unit. For example, the data collection unit collects event information, store information, weather information, etc., for the wider Shibuya area. The data collection unit can use generative AI to automatically collect information about the wider Shibuya area. For example, the data collection unit collects publicly available data on the internet and information from affiliated stores and event organizers. The proposal unit makes optimal suggestions based on the information collected by the data collection unit. For example, the proposal unit suggests the most suitable stores and events according to the visitor's interests and purposes. The suggestion unit can use generative AI to analyze collected information and generate optimal suggestions. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," the suggestion unit will suggest highly-rated ramen restaurants from among those in the greater Shibuya area. The dialogue unit provides the information suggested by the suggestion unit through characters. The dialogue unit provides information to visitors through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." The dialogue unit can use generative AI to provide information through dialogue with characters. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," the dialogue unit will have "a woman working in Shibuya" suggest "Here's a ramen restaurant I recommend." As a result, the Shibuya AI concierge system according to this embodiment can make optimal suggestions tailored to the visitor's interests and objectives.

[0030] The analysis unit analyzes visitors' interests and purposes. For example, the analysis unit analyzes text data entered by visitors to identify their interests and purposes. The analysis unit can use generative AI to analyze visitors' input data and extract their interests and purposes. Specifically, the generative AI utilizes natural language processing technology to grammatically and semantically analyze the visitors' input text. For example, if a visitor enters "I want to eat delicious ramen in Shibuya," the generative AI extracts the keyword "delicious ramen," identifying that the visitor's purpose is to find information about ramen restaurants. Furthermore, the generative AI can understand the context of the input text and infer the visitor's potential needs and preferences. For example, it can respond to specific requests such as "a place to have fun with friends" or "I want to read in a quiet cafe." Based on this information, the analysis unit gains a detailed understanding of visitors' interests and purposes and uses this information to inform the next steps of information gathering and making suggestions.

[0031] The data collection unit collects information about the Greater Shibuya area based on the information analyzed by the analysis unit. For example, the data collection unit collects event information, store information, weather information, and more within the Greater Shibuya area. The data collection unit can automatically collect information about the Greater Shibuya area using a generation AI. Specifically, the generation AI crawls the latest information from publicly available databases on the internet, social media, and official websites, and extracts the necessary data. For example, the data collection unit collects event schedules held in the Greater Shibuya area, store opening hours, and special promotion information. It also acquires information from partner stores and event organizers in real time and stores it in the database. The data collection unit centrally manages this information, making it easily accessible to the analysis and proposal units. Furthermore, the data collection unit is equipped with a filtering function to verify the reliability and timeliness of the collected information, preventing incorrect or outdated information from being reflected in the system. This allows the data collection unit to efficiently collect the latest and most accurate information about the Greater Shibuya area, improving the overall information accuracy of the system.

[0032] The suggestion department makes optimal suggestions based on the information collected by the data collection department. For example, the suggestion department suggests the most suitable shops and events according to the visitor's interests and purpose. The suggestion department can use generative AI to analyze the collected information and generate optimal suggestions. Specifically, the generative AI prioritizes analyzing information related to the visitor's interests and purpose and selects highly-rated shops and events. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," the suggestion department will suggest the most suitable ramen shop from among ramen shops in the greater Shibuya area, taking into account factors such as customer reviews, popularity, and accessibility. The suggestion department can also learn from the visitor's past usage history and preferences to make personalized suggestions. For example, it can make new suggestions tailored to the visitor's preferences based on information about shops they have previously visited and events they have attended. Furthermore, the suggestion department can also make suggestions that are appropriate to the visitor's real-time situation and environment. For example, it can make optimal suggestions considering the weather, time of day, and crowd levels. In this way, the suggestion department can provide visitors with optimal information and a highly satisfying experience.

[0033] The Dialogue Department provides information proposed by the Proposal Department through characters. For example, the Dialogue Department provides information to visitors through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." The Dialogue Department can provide information through dialogue with characters using generative AI. Specifically, the generative AI uses natural language generation technology to generate text that allows the characters to have natural conversations with visitors. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," "a woman working in Shibuya" will suggest, "Here's a ramen shop I recommend." The Dialogue Department also makes the dialogue more approachable by changing the characters' facial expressions and tone of voice. For example, a character can create a sense of familiarity with visitors by speaking with a smile or conveying recommendations in an excited voice. Furthermore, the Dialogue Department can analyze visitors' reactions and feedback in real time and adjust the dialogue content as needed. For example, if a visitor shows no interest in a suggestion, it can flexibly respond by making a different suggestion. This allows the dialogue department to provide visitors with approachable and useful information, resulting in a highly satisfying experience.

[0034] The data collection unit can collect event information, store information, weather information, etc., for the greater Shibuya area. For example, the data collection unit can collect event information for the greater Shibuya area. The data collection unit can collect information such as the type of event, date and time, and location. The data collection unit can also collect store information for the greater Shibuya area. The data collection unit can collect information such as the type of business, business hours, and location of the stores. Furthermore, the data collection unit can also collect weather information for the greater Shibuya area. The data collection unit can collect information such as weather forecasts, temperature, and probability of precipitation. In this way, a wide variety of information can be collected for the greater Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input publicly available data from the internet into a generation AI, and the generation AI can automatically collect the information.

[0035] The analysis unit can analyze visitors' past behavioral history and optimize the analysis method for their interests and purposes. For example, the analysis unit can prioritize analyzing spots of interest based on places visitors have visited in the past. The analysis unit can analyze visitors' past behavioral patterns and suggest events of interest. The analysis unit can also analyze information of interest by referring to visitors' past search history. This allows the analysis method to be optimized based on visitors' past behavioral history. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input visitor location data into a generative AI, which can analyze past behavioral history and optimize the analysis method based on the results.

[0036] The analysis unit can filter information based on the visitor's current situation and areas of interest during analysis. For example, the analysis unit can prioritize analyzing nearby spots based on the visitor's current location. The analysis unit can filter relevant information based on the visitor's areas of interest. The analysis unit can also provide optimal information based on the visitor's current situation (weather, time of day, etc.). This allows information to be filtered based on the visitor's current situation and areas of interest. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the visitor's real-time location information into a generation AI, which can then filter the information based on the current situation.

[0037] The analysis unit can prioritize analyzing highly relevant information by considering the visitor's geographical location during the analysis process. For example, the analysis unit can prioritize analyzing nearby spots based on the visitor's current location. The analysis unit can also prioritize analyzing relevant information based on the visitor's travel route. Furthermore, the analysis unit can prioritize analyzing optimal information based on the visitor's destination. This allows for the prioritization of highly relevant information based on the visitor's geographical location. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the visitor's GPS data into a generating AI, which can then prioritize analyzing highly relevant information based on geographical location information.

[0038] The analysis unit can analyze visitors' social media activity and extract relevant information during the analysis process. For example, the analysis unit can analyze the content of visitors' social media posts and identify spots of interest. The analysis unit can also analyze relevant information by referring to the content of posts by visitors' followers and friends. Furthermore, the analysis unit can analyze optimal information based on visitors' social media activity. This allows for the analysis of relevant information based on visitors' social media activity. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input visitors' social media data into a generative AI, which can then analyze social media activity and extract relevant information based on the results.

[0039] The data collection unit can analyze past event information in the greater Shibuya area and optimize the data collection method. For example, the data collection unit can prioritize the collection of relevant information based on past event information. The data collection unit can propose the optimal information collection method by referring to successful past events. Furthermore, the data collection unit can analyze feedback from past event participants and improve the information collection method. This allows the data collection method to be optimized based on past event information in the greater Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past event data into a generation AI, which can analyze the past event information and optimize the data collection method based on the results.

[0040] The data collection unit can filter information based on current weather and event information during data collection. For example, the data collection unit can prioritize collecting information on indoor events based on the current weather. The data collection unit can also prioritize collecting information on events currently underway. Furthermore, the data collection unit can filter information to be optimal based on current weather conditions. This allows information to be filtered based on current weather and event information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input current weather data into a generation AI, and the generation AI can filter the information based on the weather information.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information within the wider Shibuya area during data collection. For example, the data collection unit can prioritize the collection of information about nearby spots based on the current location. The data collection unit can prioritize the collection of relevant information based on the travel route. Furthermore, the data collection unit can prioritize the collection of optimal information based on the destination. This allows for the priority collection of highly relevant information based on geographical location information within the wider Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input GPS data for the wider Shibuya area into a generation AI, which can then prioritize the collection of highly relevant information based on geographical location information.

[0042] The data collection unit can analyze social media activity in the greater Shibuya area and collect relevant information during data collection. For example, the data collection unit can analyze social media posts and collect relevant event information. The data collection unit can collect relevant information by referring to posts from followers and friends. The data collection unit can also collect optimal information based on social media activity. This allows for the collection of relevant information based on social media activity in the greater Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input social media data from the greater Shibuya area into a generative AI, which will analyze social media activity and collect relevant information based on the results.

[0043] The proposal unit can adjust the level of detail of a proposal based on the importance of the information at the time of proposal creation. For example, the proposal unit can increase the level of detail of a proposal to prioritize the provision of important information. The proposal unit can adjust the level of detail of a proposal to provide general information. The proposal unit can also optimize the level of detail of a proposal to quickly provide highly urgent information. This allows the level of detail of a proposal to be adjusted according to the importance of the information. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not using a generative AI. For example, the proposal unit can input data to evaluate the importance of information into a generative AI, and the generative AI can adjust the level of detail of the proposal based on the importance of the information.

[0044] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, when providing restaurant information, the proposal unit can apply a proposal algorithm specifically for restaurants. When providing shopping information, the proposal unit can apply a proposal algorithm specifically for shopping. Furthermore, when providing event information, the proposal unit can apply a proposal algorithm specifically for events. This allows for the application of different proposal algorithms depending on the category of information. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input restaurant information into a generative AI, and the generative AI can apply a proposal algorithm specifically for restaurants to make a proposal.

[0045] The proposal department can determine the priority of proposals based on the timing of information submission at the time of proposal submission. For example, the proposal department may prioritize proposals to provide urgent information first. The proposal department may adjust the priority of proposals to provide general information first. The proposal department may also optimize the priority of proposals to provide important information first. This allows the priority of proposals to be determined according to the timing of information submission. Some or all of the above processing in the proposal department may be performed using or without a generative AI. For example, the proposal department may input data to evaluate the timing of information submission into a generative AI, and the generative AI may determine the priority of proposals based on the timing of information submission.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal unit can adjust the order of proposals to prioritize the provision of highly relevant information. The proposal unit can optimize the order of proposals to provide general information. The proposal unit can also determine the order of proposals to prioritize the provision of important information. This allows the order of proposals to be adjusted based on the relevance of the information. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not. For example, the proposal unit can input data to evaluate the relevance of information into a generative AI, and the generative AI can adjust the order of proposals based on the relevance of the information.

[0047] The dialogue unit can select the optimal dialogue method by referring to the visitor's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the visitor's preferred dialogue style in the past. The dialogue unit can also prioritize providing topics of interest to the visitor based on their past dialogue history. Furthermore, the dialogue unit can analyze the visitor's past dialogue history and select the most effective dialogue method. This allows the dialogue unit to select the optimal dialogue method based on the visitor's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the visitor's past dialogue data into a generative AI, which can then analyze the past dialogue history and select the optimal dialogue method based on the results.

[0048] The dialogue unit can customize the content of the conversation based on the visitor's current situation during the conversation. For example, the dialogue unit can provide relevant information based on the visitor's current location. The dialogue unit can provide optimal information based on the visitor's current situation (such as weather and time of day). The dialogue unit can also customize the content of the conversation based on the visitor's current areas of interest. This allows the content of the conversation to be customized based on the visitor's current situation. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the visitor's real-time location information into a generative AI, which can then customize the content of the conversation based on the current situation.

[0049] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the visitor's geographical location information. For example, the dialogue unit can provide relevant information based on the visitor's current location. The dialogue unit can select the optimal dialogue method based on the visitor's travel route. The dialogue unit can also provide optimal information based on the visitor's destination. This allows the dialogue unit to select the optimal dialogue method based on the visitor's geographical location information. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the visitor's GPS data into a generative AI, which can then select the optimal dialogue method based on the geographical location information.

[0050] The dialogue unit can analyze visitors' social media activity during a dialogue and suggest dialogue content. For example, the dialogue unit can analyze the content of visitors' social media posts and provide topics of interest. The dialogue unit can also refer to the content of posts by visitors' followers and friends to provide relevant information. Furthermore, the dialogue unit can suggest optimal dialogue content based on visitors' social media activity. This allows the dialogue content to be suggested based on visitors' social media activity. Some or all of the above processing in the dialogue unit may be performed using generative AI, or not. For example, the dialogue unit can input visitors' social media data into a generative AI, which can analyze the social media activity and suggest dialogue content based on the results.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The analytics department can consider visitors' past purchase history when analyzing their interests and purposes. For example, it can prioritize analyzing stores and events of interest based on data of products and services previously purchased by visitors. Furthermore, it can identify preferences for specific brands or categories from visitors' purchase history and provide optimal recommendations based on that. In addition, the analytics department can analyze visitors' purchase history and prioritize recommending stores and events that visitors have previously given high ratings to. This allows for more personalized recommendations based on visitors' past purchase history.

[0053] The data collection unit can consider the real-time location information of visitors when collecting information in the wider Shibuya area. For example, the unit can prioritize collecting information on nearby events and shops based on the visitor's current location. It can also predict the visitor's travel route and collect information on points of interest along that route. Furthermore, the unit can collect information about the area around the visitor's destination based on their destination. This allows for the collection of more relevant information based on the visitor's real-time location information.

[0054] The analytics unit can analyze visitors' past behavioral history and optimize the analysis method for their interests and purposes. For example, it can prioritize analyzing spots of interest based on places visitors have visited in the past. It can also analyze visitors' past behavioral patterns and suggest events of interest. Furthermore, it can analyze information of interest by referring to visitors' past search history. This allows for the optimization of analysis methods based on visitors' past behavioral history.

[0055] The analysis unit can filter information based on the visitor's current situation and areas of interest during analysis. For example, it can prioritize analyzing nearby spots based on the visitor's current location. It can also filter relevant information based on the visitor's areas of interest. Furthermore, it can provide optimal information based on the visitor's current situation (such as weather and time of day). This allows for information filtering based on the visitor's current situation and areas of interest.

[0056] The analysis unit can prioritize analyzing highly relevant information by considering the visitor's geographical location during the analysis process. For example, the analysis unit can prioritize analyzing nearby spots based on the visitor's current location. It can also prioritize analyzing relevant information based on the visitor's travel route. Furthermore, the analysis unit can prioritize analyzing optimal information based on the visitor's destination. This allows for the prioritization of highly relevant information based on the visitor's geographical location.

[0057] The analysis unit can analyze visitors' social media activity during analysis and extract relevant information. For example, the analysis unit can analyze the content of visitors' social media posts and identify spots of interest. It can also analyze relevant information by referencing posts from visitors' followers and friends. Furthermore, the analysis unit can analyze optimal information based on visitors' social media activity. This allows for the analysis of relevant information based on visitors' social media activity.

[0058] The data collection unit can analyze past event information in the greater Shibuya area and optimize its collection methods. For example, it can prioritize the collection of relevant information based on past event data. Furthermore, it can propose optimal information collection methods by referencing successful past events. In addition, it can analyze feedback from past event participants to improve its information collection methods. This allows for the optimization of data collection methods based on past event information in the greater Shibuya area.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The analysis unit analyzes the interests and purposes of visitors. For example, it analyzes text data entered by visitors to identify their interests and purposes. The analysis unit can use generation AI to analyze visitors' input data and extract their interests and purposes. Step 2: The collection unit collects information about the wider Shibuya area based on the information analyzed by the analysis unit. For example, it collects event information, store information, weather information, etc., for the wider Shibuya area. The collection unit can automatically collect information about the wider Shibuya area using generation AI. Step 3: The proposal department makes optimal suggestions based on the information collected by the data collection department. For example, it suggests the most suitable stores and events according to the interests and purposes of visitors. The proposal department can use generation AI to analyze the collected information and generate optimal suggestions. Step 4: The dialogue unit provides information proposed by the proposal unit through characters. For example, it provides information to visitors through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." The dialogue unit can provide information through dialogue with characters using generative AI.

[0061] (Example of form 2) The Shibuya AI concierge system, according to an embodiment of the present invention, proposes a region-specific generative AI, "Shibuya AI concierge," to solve the problem that while tourist attractions in the greater Shibuya area are well-known, awareness and satisfaction with dining and shopping are low. Shibuya AI concierge analyzes diverse information such as event information, store information, and weather information for the greater Shibuya area and makes optimal suggestions according to the visitor's interests and purposes. Shibuya AI concierge provides information through characters with different personalities, such as "a woman working in Shibuya" and "a local elderly man who has lived in Shibuya for many years." Visitors can obtain real-time local information and recommended spots through interaction with the characters, enjoying an experience as if they were talking to a local. This can encourage behavioral changes among visitors, improve awareness and satisfaction, and enhance the attractiveness and walkability of the area. For example, with Shibuya AI concierge, visitors input their interests and purposes. Next, the generative AI analyzes event information, store information, weather information, etc. for the greater Shibuya area and makes optimal suggestions according to the visitor's interests and purposes. The suggestions are made through characters such as "a woman working in Shibuya" and "a local elderly man who has lived in Shibuya for many years." Visitors can obtain real-time local information and recommended spots through interaction with these characters. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," Shibuya AI concierge will analyze information on ramen restaurants in the greater Shibuya area and suggest the best ramen restaurant. The suggestions are made through characters such as "a woman working in Shibuya" and "a local elderly man who has lived in Shibuya for many years." Visitors can obtain real-time local information and recommended spots through interaction with these characters. This encourages behavioral changes among visitors, improves awareness and satisfaction, and enhances the attractiveness and walkability of the area. Shibuya AI concierge analyzes diverse information such as event information, store information, and weather information in the greater Shibuya area in order to make optimal suggestions tailored to the interests and purposes of visitors.For example, if a visitor enters "I want to eat delicious ramen in Shibuya," Shibuya AI concierge analyzes information on ramen restaurants in the greater Shibuya area and suggests the best ramen restaurant. The suggestions are made through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." Through interaction with these characters, visitors can obtain real-time local information and recommended spots. This encourages behavioral change among visitors, improves awareness and satisfaction, and enhances the area's appeal and encourages exploration. Shibuya AI concierge analyzes diverse information such as event information, store information, and weather information for the greater Shibuya area to provide optimal suggestions tailored to visitors' interests and purposes. For example, if a visitor enters "I want to eat delicious ramen in Shibuya," Shibuya AI concierge analyzes information on ramen restaurants in the greater Shibuya area and suggests the best ramen restaurant. The suggestions are made through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." Through interaction with these characters, visitors can obtain real-time local information and recommended spots. This will encourage behavioral changes among visitors, improve awareness and satisfaction, and enhance the area's appeal and encourage people to explore. As a result, the Shibuya AI concierge system will be able to provide optimal suggestions tailored to the interests and purposes of visitors.

[0062] The Shibuya AI concierge system according to this embodiment comprises an analysis unit, a data collection unit, a proposal unit, and a dialogue unit. The analysis unit analyzes the interests and purposes of visitors. For example, the analysis unit analyzes text data entered by visitors to identify their interests and purposes. The analysis unit can use generative AI to analyze visitors' input data and extract their interests and purposes. For example, if a visitor enters "I want to eat delicious ramen in Shibuya," the analysis unit will prioritize analyzing information related to ramen. The data collection unit collects information about the wider Shibuya area based on the information analyzed by the analysis unit. For example, the data collection unit collects event information, store information, weather information, etc., for the wider Shibuya area. The data collection unit can use generative AI to automatically collect information about the wider Shibuya area. For example, the data collection unit collects publicly available data on the internet and information from affiliated stores and event organizers. The proposal unit makes optimal suggestions based on the information collected by the data collection unit. For example, the proposal unit suggests the most suitable stores and events according to the visitor's interests and purposes. The suggestion unit can use generative AI to analyze collected information and generate optimal suggestions. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," the suggestion unit will suggest highly-rated ramen restaurants from among those in the greater Shibuya area. The dialogue unit provides the information suggested by the suggestion unit through characters. The dialogue unit provides information to visitors through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." The dialogue unit can use generative AI to provide information through dialogue with characters. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," the dialogue unit will have "a woman working in Shibuya" suggest "Here's a ramen restaurant I recommend." As a result, the Shibuya AI concierge system according to this embodiment can make optimal suggestions tailored to the visitor's interests and objectives.

[0063] The analysis unit analyzes visitors' interests and purposes. For example, the analysis unit analyzes text data entered by visitors to identify their interests and purposes. The analysis unit can use generative AI to analyze visitors' input data and extract their interests and purposes. Specifically, the generative AI utilizes natural language processing technology to grammatically and semantically analyze the visitors' input text. For example, if a visitor enters "I want to eat delicious ramen in Shibuya," the generative AI extracts the keyword "delicious ramen," identifying that the visitor's purpose is to find information about ramen restaurants. Furthermore, the generative AI can understand the context of the input text and infer the visitor's potential needs and preferences. For example, it can respond to specific requests such as "a place to have fun with friends" or "I want to read in a quiet cafe." Based on this information, the analysis unit gains a detailed understanding of visitors' interests and purposes and uses this information to inform the next steps of information gathering and making suggestions.

[0064] The data collection unit collects information about the Greater Shibuya area based on the information analyzed by the analysis unit. For example, the data collection unit collects event information, store information, weather information, and more within the Greater Shibuya area. The data collection unit can automatically collect information about the Greater Shibuya area using a generation AI. Specifically, the generation AI crawls the latest information from publicly available databases on the internet, social media, and official websites, and extracts the necessary data. For example, the data collection unit collects event schedules held in the Greater Shibuya area, store opening hours, and special promotion information. It also acquires information from partner stores and event organizers in real time and stores it in the database. The data collection unit centrally manages this information, making it easily accessible to the analysis and proposal units. Furthermore, the data collection unit is equipped with a filtering function to verify the reliability and timeliness of the collected information, preventing incorrect or outdated information from being reflected in the system. This allows the data collection unit to efficiently collect the latest and most accurate information about the Greater Shibuya area, improving the overall information accuracy of the system.

[0065] The suggestion department makes optimal suggestions based on the information collected by the data collection department. For example, the suggestion department suggests the most suitable shops and events according to the visitor's interests and purpose. The suggestion department can use generative AI to analyze the collected information and generate optimal suggestions. Specifically, the generative AI prioritizes analyzing information related to the visitor's interests and purpose and selects highly-rated shops and events. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," the suggestion department will suggest the most suitable ramen shop from among ramen shops in the greater Shibuya area, taking into account factors such as customer reviews, popularity, and accessibility. The suggestion department can also learn from the visitor's past usage history and preferences to make personalized suggestions. For example, it can make new suggestions tailored to the visitor's preferences based on information about shops they have previously visited and events they have attended. Furthermore, the suggestion department can also make suggestions that are appropriate to the visitor's real-time situation and environment. For example, it can make optimal suggestions considering the weather, time of day, and crowd levels. In this way, the suggestion department can provide visitors with optimal information and a highly satisfying experience.

[0066] The Dialogue Department provides information proposed by the Proposal Department through characters. For example, the Dialogue Department provides information to visitors through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." The Dialogue Department can provide information through dialogue with characters using generative AI. Specifically, the generative AI uses natural language generation technology to generate text that allows the characters to have natural conversations with visitors. For example, if a visitor inputs "I want to eat delicious ramen in Shibuya," "a woman working in Shibuya" will suggest, "Here's a ramen shop I recommend." The Dialogue Department also makes the dialogue more approachable by changing the characters' facial expressions and tone of voice. For example, a character can create a sense of familiarity with visitors by speaking with a smile or conveying recommendations in an excited voice. Furthermore, the Dialogue Department can analyze visitors' reactions and feedback in real time and adjust the dialogue content as needed. For example, if a visitor shows no interest in a suggestion, it can flexibly respond by making a different suggestion. This allows the dialogue department to provide visitors with approachable and useful information, resulting in a highly satisfying experience.

[0067] The data collection unit can collect event information, store information, weather information, etc., for the greater Shibuya area. For example, the data collection unit can collect event information for the greater Shibuya area. The data collection unit can collect information such as the type of event, date and time, and location. The data collection unit can also collect store information for the greater Shibuya area. The data collection unit can collect information such as the type of business, business hours, and location of the stores. Furthermore, the data collection unit can also collect weather information for the greater Shibuya area. The data collection unit can collect information such as weather forecasts, temperature, and probability of precipitation. In this way, a wide variety of information can be collected for the greater Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input publicly available data from the internet into a generation AI, and the generation AI can automatically collect the information.

[0068] The analysis unit can estimate the emotions of visitors and adjust the accuracy of the analysis based on the estimated emotions. For example, if a visitor is excited, the analysis unit can increase the accuracy of the analysis to provide detailed information. If a visitor is relaxed, the analysis unit can adjust the accuracy of the analysis to provide general information. Furthermore, if a visitor is in a hurry, the analysis unit can optimize the accuracy of the analysis to provide information quickly. In this way, the accuracy of the analysis can be adjusted according to the emotions of the visitors. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input visitor facial expression data into a generative AI, the generative AI can estimate emotions, and the accuracy of the analysis can be adjusted based on the result.

[0069] The analysis unit can analyze visitors' past behavioral history and optimize the analysis method for their interests and purposes. For example, the analysis unit can prioritize analyzing spots of interest based on places visitors have visited in the past. The analysis unit can analyze visitors' past behavioral patterns and suggest events of interest. The analysis unit can also analyze information of interest by referring to visitors' past search history. This allows the analysis method to be optimized based on visitors' past behavioral history. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input visitor location data into a generative AI, which can analyze past behavioral history and optimize the analysis method based on the results.

[0070] The analysis unit can filter information based on the visitor's current situation and areas of interest during analysis. For example, the analysis unit can prioritize analyzing nearby spots based on the visitor's current location. The analysis unit can filter relevant information based on the visitor's areas of interest. The analysis unit can also provide optimal information based on the visitor's current situation (weather, time of day, etc.). This allows information to be filtered based on the visitor's current situation and areas of interest. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the visitor's real-time location information into a generation AI, which can then filter the information based on the current situation.

[0071] The analysis unit can estimate the emotions of visitors and determine the priority of analysis results based on the estimated emotions. For example, if a visitor is excited, the analysis unit can prioritize providing detailed information. If a visitor is relaxed, the analysis unit can prioritize providing general information. Furthermore, if a visitor is in a hurry, the analysis unit can prioritize providing information that can be delivered quickly. This allows the priority of analysis results to be determined according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit may be performed using the generative AI or not. For example, the analysis unit can input visitor voice data into the generative AI, the generative AI can estimate emotions, and the priority of analysis results can be determined based on the result.

[0072] The analysis unit can prioritize analyzing highly relevant information by considering the visitor's geographical location during the analysis process. For example, the analysis unit can prioritize analyzing nearby spots based on the visitor's current location. The analysis unit can also prioritize analyzing relevant information based on the visitor's travel route. Furthermore, the analysis unit can prioritize analyzing optimal information based on the visitor's destination. This allows for the prioritization of highly relevant information based on the visitor's geographical location. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the visitor's GPS data into a generating AI, which can then prioritize analyzing highly relevant information based on geographical location information.

[0073] The analysis unit can analyze visitors' social media activity and extract relevant information during the analysis process. For example, the analysis unit can analyze the content of visitors' social media posts and identify spots of interest. The analysis unit can also analyze relevant information by referring to the content of posts by visitors' followers and friends. Furthermore, the analysis unit can analyze optimal information based on visitors' social media activity. This allows for the analysis of relevant information based on visitors' social media activity. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input visitors' social media data into a generative AI, which can then analyze social media activity and extract relevant information based on the results.

[0074] The data collection unit can estimate the emotions of visitors and adjust the timing of information collection based on the estimated emotions. For example, if a visitor is excited, the data collection unit can collect information in real time. If a visitor is relaxed, the data collection unit can collect information periodically. Also, if a visitor is in a hurry, the data collection unit can collect information quickly. This allows the timing of information collection to be adjusted according to the emotions of the visitors. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using a generative AI or not. For example, the data collection unit can input visitor facial expression data into a generative AI, the generative AI can estimate emotions, and the timing of information collection can be adjusted based on the result.

[0075] The data collection unit can analyze past event information in the greater Shibuya area and optimize the data collection method. For example, the data collection unit can prioritize the collection of relevant information based on past event information. The data collection unit can propose the optimal information collection method by referring to successful past events. Furthermore, the data collection unit can analyze feedback from past event participants and improve the information collection method. This allows the data collection method to be optimized based on past event information in the greater Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input past event data into a generation AI, which can analyze the past event information and optimize the data collection method based on the results.

[0076] The data collection unit can filter information based on current weather and event information during data collection. For example, the data collection unit can prioritize collecting information on indoor events based on the current weather. The data collection unit can also prioritize collecting information on events currently underway. Furthermore, the data collection unit can filter information to be optimal based on current weather conditions. This allows information to be filtered based on current weather and event information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input current weather data into a generation AI, and the generation AI can filter the information based on the weather information.

[0077] The data collection unit can estimate the emotions of visitors and determine the priority of information to collect based on the estimated emotions. For example, if a visitor is excited, the data collection unit may prioritize collecting detailed information. If a visitor is relaxed, the data collection unit may prioritize collecting general information. Furthermore, if a visitor is in a hurry, the data collection unit may prioritize collecting information that can be quickly gathered. This allows the data collection unit to determine the priority of information to collect according to the visitor'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 or without a generative AI. For example, the data collection unit can input visitor voice data into a generative AI, the generative AI can estimate emotions, and the data collection unit can determine the priority of information to collect based on the result.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information within the wider Shibuya area during data collection. For example, the data collection unit can prioritize the collection of information about nearby spots based on the current location. The data collection unit can prioritize the collection of relevant information based on the travel route. Furthermore, the data collection unit can prioritize the collection of optimal information based on the destination. This allows for the priority collection of highly relevant information based on geographical location information within the wider Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input GPS data for the wider Shibuya area into a generation AI, which can then prioritize the collection of highly relevant information based on geographical location information.

[0079] The data collection unit can analyze social media activity in the greater Shibuya area and collect relevant information during data collection. For example, the data collection unit can analyze social media posts and collect relevant event information. The data collection unit can collect relevant information by referring to posts from followers and friends. The data collection unit can also collect optimal information based on social media activity. This allows for the collection of relevant information based on social media activity in the greater Shibuya area. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input social media data from the greater Shibuya area into a generative AI, which will analyze social media activity and collect relevant information based on the results.

[0080] The suggestion unit can estimate the visitor's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the visitor is excited, the suggestion unit can adjust the way the suggestion is presented to provide detailed information. If the visitor is relaxed, the suggestion unit can adjust the way the suggestion is presented to provide general information. Furthermore, if the visitor is in a hurry, the suggestion unit can adjust the way the suggestion is presented to provide information quickly. This allows the suggestion to be presented according to the visitor'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 suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input visitor facial expression data into a generative AI, the generative AI can estimate emotions, and the suggestion can be presented based on the result.

[0081] The proposal unit can adjust the level of detail of a proposal based on the importance of the information at the time of proposal creation. For example, the proposal unit can increase the level of detail of a proposal to prioritize the provision of important information. The proposal unit can adjust the level of detail of a proposal to provide general information. The proposal unit can also optimize the level of detail of a proposal to quickly provide highly urgent information. This allows the level of detail of a proposal to be adjusted according to the importance of the information. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not using a generative AI. For example, the proposal unit can input data to evaluate the importance of information into a generative AI, and the generative AI can adjust the level of detail of the proposal based on the importance of the information.

[0082] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, when providing restaurant information, the proposal unit can apply a proposal algorithm specifically for restaurants. When providing shopping information, the proposal unit can apply a proposal algorithm specifically for shopping. Furthermore, when providing event information, the proposal unit can apply a proposal algorithm specifically for events. This allows for the application of different proposal algorithms depending on the category of information. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input restaurant information into a generative AI, and the generative AI can apply a proposal algorithm specifically for restaurants to make a proposal.

[0083] The suggestion unit can estimate the visitor's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the visitor is excited, the suggestion unit can adjust the length of the suggestion to provide detailed information. If the visitor is relaxed, the suggestion unit can adjust the length of the suggestion to provide general information. The suggestion unit can also adjust the length of the suggestion to provide information quickly if the visitor is in a hurry. This allows the length of the suggestion to be adjusted according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input the visitor's voice data into a generative AI, the generative AI can estimate the emotions, and the length of the suggestion can be adjusted based on the result.

[0084] The proposal department can determine the priority of proposals based on the timing of information submission at the time of proposal submission. For example, the proposal department may prioritize proposals to provide urgent information first. The proposal department may adjust the priority of proposals to provide general information first. The proposal department may also optimize the priority of proposals to provide important information first. This allows the priority of proposals to be determined according to the timing of information submission. Some or all of the above processing in the proposal department may be performed using or without a generative AI. For example, the proposal department may input data to evaluate the timing of information submission into a generative AI, and the generative AI may determine the priority of proposals based on the timing of information submission.

[0085] The proposal unit can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal unit can adjust the order of proposals to prioritize the provision of highly relevant information. The proposal unit can optimize the order of proposals to provide general information. The proposal unit can also determine the order of proposals to prioritize the provision of important information. This allows the order of proposals to be adjusted based on the relevance of the information. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not. For example, the proposal unit can input data to evaluate the relevance of information into a generative AI, and the generative AI can adjust the order of proposals based on the relevance of the information.

[0086] The dialogue unit can estimate the visitor's emotions and adjust the way the dialogue is presented based on the estimated emotions. For example, if the visitor is excited, the dialogue unit can adjust the way the dialogue is presented to provide detailed information. If the visitor is relaxed, the dialogue unit can adjust the way the dialogue is presented to provide general information. Furthermore, if the visitor is in a hurry, the dialogue unit can adjust the way the dialogue is presented to provide information quickly. In this way, the way the dialogue is presented can be adjusted according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 dialogue unit may be performed using a generative AI or not. For example, the dialogue unit can input the visitor's facial expression data into a generative AI, the generative AI can estimate the emotions, and the way the dialogue is presented can be adjusted based on the result.

[0087] The dialogue unit can select the optimal dialogue method by referring to the visitor's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the visitor's preferred dialogue style in the past. The dialogue unit can also prioritize providing topics of interest to the visitor based on their past dialogue history. Furthermore, the dialogue unit can analyze the visitor's past dialogue history and select the most effective dialogue method. This allows the dialogue unit to select the optimal dialogue method based on the visitor's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the visitor's past dialogue data into a generative AI, which can then analyze the past dialogue history and select the optimal dialogue method based on the results.

[0088] The dialogue unit can customize the content of the conversation based on the visitor's current situation during the conversation. For example, the dialogue unit can provide relevant information based on the visitor's current location. The dialogue unit can provide optimal information based on the visitor's current situation (such as weather and time of day). The dialogue unit can also customize the content of the conversation based on the visitor's current areas of interest. This allows the content of the conversation to be customized based on the visitor's current situation. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the visitor's real-time location information into a generative AI, which can then customize the content of the conversation based on the current situation.

[0089] The dialogue unit can estimate the visitor's emotions and determine the priority of the conversation based on the estimated emotions. For example, if the visitor is excited, the dialogue unit can prioritize providing detailed information. If the visitor is relaxed, the dialogue unit can prioritize providing general information. Furthermore, if the visitor is in a hurry, the dialogue unit can prioritize providing information that can be delivered quickly. This allows the dialogue to be prioritized according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using generative AI or not. For example, the dialogue unit can input visitor facial expression data into a generative AI, the generative AI can estimate emotions, and the dialogue priority can be determined based on the result.

[0090] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the visitor's geographical location information. For example, the dialogue unit can provide relevant information based on the visitor's current location. The dialogue unit can select the optimal dialogue method based on the visitor's travel route. The dialogue unit can also provide optimal information based on the visitor's destination. This allows the dialogue unit to select the optimal dialogue method based on the visitor's geographical location information. Some or all of the above processing in the dialogue unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input the visitor's GPS data into a generative AI, which can then select the optimal dialogue method based on the geographical location information.

[0091] The dialogue unit can analyze visitors' social media activity during a dialogue and suggest dialogue content. For example, the dialogue unit can analyze the content of visitors' social media posts and provide topics of interest. The dialogue unit can also refer to the content of posts by visitors' followers and friends to provide relevant information. Furthermore, the dialogue unit can suggest optimal dialogue content based on visitors' social media activity. This allows the dialogue content to be suggested based on visitors' social media activity. Some or all of the above processing in the dialogue unit may be performed using generative AI, or not. For example, the dialogue unit can input visitors' social media data into a generative AI, which can analyze the social media activity and suggest dialogue content based on the results.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The analytics department can consider visitors' past purchase history when analyzing their interests and purposes. For example, it can prioritize analyzing stores and events of interest based on data of products and services previously purchased by visitors. Furthermore, it can identify preferences for specific brands or categories from visitors' purchase history and provide optimal recommendations based on that. In addition, the analytics department can analyze visitors' purchase history and prioritize recommending stores and events that visitors have previously given high ratings to. This allows for more personalized recommendations based on visitors' past purchase history.

[0094] The data collection unit can consider the real-time location information of visitors when collecting information in the wider Shibuya area. For example, the unit can prioritize collecting information on nearby events and shops based on the visitor's current location. It can also predict the visitor's travel route and collect information on points of interest along that route. Furthermore, the unit can collect information about the area around the visitor's destination based on their destination. This allows for the collection of more relevant information based on the visitor's real-time location information.

[0095] The analysis unit can estimate the emotions of visitors and determine the priority of the analysis based on the estimated emotions. For example, if a visitor is excited, the analysis unit can increase the accuracy of the analysis to prioritize providing detailed information. If a visitor is relaxed, it can adjust the accuracy of the analysis to prioritize providing general information. Furthermore, if a visitor is in a hurry, it can optimize the accuracy of the analysis to provide information quickly. In this way, the priority of the analysis can be determined according to the emotions of the visitors.

[0096] The analytics unit can analyze visitors' past behavioral history and optimize the analysis method for their interests and purposes. For example, it can prioritize analyzing spots of interest based on places visitors have visited in the past. It can also analyze visitors' past behavioral patterns and suggest events of interest. Furthermore, it can analyze information of interest by referring to visitors' past search history. This allows for the optimization of analysis methods based on visitors' past behavioral history.

[0097] The analysis unit can filter information based on the visitor's current situation and areas of interest during analysis. For example, it can prioritize analyzing nearby spots based on the visitor's current location. It can also filter relevant information based on the visitor's areas of interest. Furthermore, it can provide optimal information based on the visitor's current situation (such as weather and time of day). This allows for information filtering based on the visitor's current situation and areas of interest.

[0098] The analysis unit can estimate the emotions of visitors and prioritize the analysis results based on those estimated emotions. For example, if a visitor is excited, the analysis unit can prioritize providing detailed information. If a visitor is relaxed, it can prioritize providing general information. Furthermore, if a visitor is in a hurry, it can prioritize providing information that can be delivered quickly. In this way, the priority of analysis results can be determined according to the emotions of the visitors.

[0099] The analysis unit can prioritize analyzing highly relevant information by considering the visitor's geographical location during the analysis process. For example, the analysis unit can prioritize analyzing nearby spots based on the visitor's current location. It can also prioritize analyzing relevant information based on the visitor's travel route. Furthermore, the analysis unit can prioritize analyzing optimal information based on the visitor's destination. This allows for the prioritization of highly relevant information based on the visitor's geographical location.

[0100] The analysis unit can analyze visitors' social media activity during analysis and extract relevant information. For example, the analysis unit can analyze the content of visitors' social media posts and identify spots of interest. It can also analyze relevant information by referencing posts from visitors' followers and friends. Furthermore, the analysis unit can analyze optimal information based on visitors' social media activity. This allows for the analysis of relevant information based on visitors' social media activity.

[0101] The data collection unit can estimate the emotions of visitors and adjust the timing of information collection based on those estimated emotions. For example, if a visitor is excited, the unit can collect information in real time. If a visitor is relaxed, it can collect information periodically. Furthermore, if a visitor is in a hurry, it can collect information quickly. This allows the timing of information collection to be adjusted according to the visitor's emotions.

[0102] The data collection unit can analyze past event information in the greater Shibuya area and optimize its collection methods. For example, it can prioritize the collection of relevant information based on past event data. Furthermore, it can propose optimal information collection methods by referencing successful past events. In addition, it can analyze feedback from past event participants to improve its information collection methods. This allows for the optimization of data collection methods based on past event information in the greater Shibuya area.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The analysis unit analyzes the interests and purposes of visitors. For example, it analyzes text data entered by visitors to identify their interests and purposes. The analysis unit can use generation AI to analyze visitors' input data and extract their interests and purposes. Step 2: The collection unit collects information about the wider Shibuya area based on the information analyzed by the analysis unit. For example, it collects event information, store information, weather information, etc., for the wider Shibuya area. The collection unit can automatically collect information about the wider Shibuya area using generation AI. Step 3: The proposal department makes optimal suggestions based on the information collected by the data collection department. For example, it suggests the most suitable stores and events according to the interests and purposes of visitors. The proposal department can use generation AI to analyze the collected information and generate optimal suggestions. Step 4: The dialogue unit provides information proposed by the proposal unit through characters. For example, it provides information to visitors through characters such as "a woman working in Shibuya" or "a local elderly man who has lived in Shibuya for many years." The dialogue unit can provide information through dialogue with characters using generative AI.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the analysis unit, collection unit, proposal unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input data of visitors and extracts their interests and purposes. The collection unit is implemented by the control unit 46A of the smart device 14, which collects event information, store information, weather information, etc., for the wider Shibuya area. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates optimal suggestions based on the collected information. The dialogue unit is implemented by the control unit 46A of the smart device 14, which provides information to visitors through a character. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the analysis unit, collection unit, suggestion unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input data of visitors and extracts their interests and purposes. The collection unit is implemented by the control unit 46A of the smart glasses 214, which collects event information, store information, weather information, etc., for the wider Shibuya area. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates optimal suggestions based on the collected information. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, which provides information to visitors through a character. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0140] Each of the multiple elements described above, including the analysis unit, collection unit, suggestion unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input data of visitors and extracts their interests and purposes. The collection unit is implemented by the control unit 46A of the headset terminal 314, which collects event information, store information, weather information, etc., for the wider Shibuya area. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates optimal suggestions based on the collected information. The dialogue unit is implemented by the control unit 46A of the headset terminal 314, which provides information to visitors through a character. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[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 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.

[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 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).

[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] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the analysis unit, collection unit, proposal unit, and dialogue unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input data of visitors and extracts their interests and purposes. The collection unit is implemented by the control unit 46A of the robot 414, which collects event information, store information, weather information, etc., for the wider Shibuya area. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates optimal suggestions based on the collected information. The dialogue unit is implemented by the control unit 46A of the robot 414, which provides information to visitors through a character. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) The analysis unit analyzes the interests and purposes of visitors, A collection unit collects information on the wider Shibuya area based on the information analyzed by the aforementioned analysis unit, A proposal unit that makes optimal suggestions based on the information collected by the aforementioned collection unit, The system comprises: an interactive unit that provides information proposed by the proposal unit through a character; A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects event information, store information, weather information, etc. for the greater Shibuya area. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system estimates the emotions of visitors and adjusts the accuracy of the analysis based on the estimated emotions of the visitors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We analyze visitors' past behavioral history and optimize the methods for analyzing their interests and purposes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, During the analysis, filtering is performed based on the current situation and areas of interest of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates the emotions of visitors and prioritizes the analysis results based on the estimated emotions of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, During analysis, the system prioritizes analyzing highly relevant information, taking into account the geographical location of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During the analysis, the social media activity of visitors is analyzed, and relevant information is examined. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the emotions of visitors and adjusts the timing of information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We analyze past event information in the greater Shibuya area and optimize the collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, filtering is performed based on current weather and event information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is The system estimates the emotions of visitors and prioritizes the information to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we prioritize collecting highly relevant information, taking into account the geographical location information of the wider Shibuya area. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, we analyze social media activity in the greater Shibuya area and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We estimate the emotions of visitors and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, The system estimates the emotions of visitors and adjusts the length of the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned dialogue unit, The system estimates the emotions of visitors and adjusts the way dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned dialogue unit, During the interaction, the system selects the most appropriate method of communication by referring to the visitor's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, During the conversation, the content of the dialogue is customized based on the visitor's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, The system estimates the emotions of visitors and determines the priority of conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, During the dialogue, the optimal dialogue method is selected considering the visitor's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During the dialogue, we analyze visitors' social media activity and propose dialogue content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 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 analysis unit analyzes the interests and purposes of visitors, A collection unit collects information on the wider Shibuya area based on the information analyzed by the aforementioned analysis unit, A proposal unit that makes optimal suggestions based on the information collected by the aforementioned collection unit, The system comprises: an interactive unit that provides information proposed by the proposal unit through a character; A system characterized by the following features.

2. The aforementioned collection unit is Collects event information, store information, weather information, etc. for the greater Shibuya area. The system according to feature 1.

3. The aforementioned analysis unit, The system estimates the emotions of visitors and adjusts the accuracy of the analysis based on the estimated emotions of the visitors. The system according to feature 1.

4. The aforementioned analysis unit, We analyze visitors' past behavioral history and optimize the methods for analyzing their interests and purposes. The system according to feature 1.

5. The aforementioned analysis unit, During the analysis, filtering is performed based on the current situation and areas of interest of visitors. The system according to feature 1.

6. The aforementioned analysis unit, The system estimates the emotions of visitors and prioritizes the analysis results based on the estimated emotions of visitors. The system according to feature 1.

7. The aforementioned analysis unit, During analysis, the system prioritizes analyzing highly relevant information, taking into account the geographical location of visitors. The system according to feature 1.

8. The aforementioned analysis unit, During the analysis, the social media activity of visitors is analyzed, and relevant information is examined. The system according to feature 1.

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