system

Abstract

The system according to the embodiment aims to efficiently display appropriate information and advertisements according to the location. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a selection unit, a display unit, a linking unit, and a connection unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The selection unit selects appropriate information or advertisements based on the information analyzed by the analysis unit. The display unit displays the information or advertisements selected by the selection unit. The linking unit links the client's commercials to the information or advertisements displayed by the display unit. The connection unit includes an automatic connection system for communication devices.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult to efficiently display appropriate information and advertisements according to the location in digital signage devices.

[0005] The system according to the embodiment aims to efficiently display appropriate information and advertisements according to the location. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a selection unit, a display unit, a linking unit, and a connection unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The selection unit selects appropriate information or advertisements based on the information analyzed by the analysis unit. The display unit displays the information or advertisements selected by the selection unit. The linking unit links the client's commercials to the information or advertisements displayed by the display unit. The connection unit includes an automatic connection system for communication devices. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently display appropriate information and advertisements according to the location. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A digital signage system according to an embodiment of the present invention directly provides desired information to people in a specific location. This digital signage system collects and analyzes information, selects and displays the most appropriate information and advertisements, and is also equipped with a function for linking client commercials and an automatic connection system for communication devices. This allows the digital signage system to directly provide desired information to people in a specific location and maximize the effectiveness of advertisements. For example, the digital signage system uses smartphone location information and Wi-Fi connection information to identify the attributes and interests of people in that location. The collected information is then analyzed to select the most appropriate information and advertisements. The selected information and advertisements are then displayed on the digital signage device. Furthermore, when displaying an advertisement for a specific product, a commercial from the product's vendor is simultaneously displayed. The digital signage device automatically connects to the Internet and updates information in real time, allowing the system to always provide the latest information.

[0029] A digital signage system according to an embodiment includes a collection unit, an analysis unit, a selection unit, a display unit, a linking unit, and a connection unit. The collection unit collects information about people in a specific location. For example, the collection unit can identify the attributes and interests of people in the location using smartphone location information and Wi-Fi connection information. The collection unit can also detect people's movements in real time using a camera or sensor. The analysis unit analyzes the collected information and selects optimal information or advertisements. For example, the analysis unit can use AI to analyze the collected data and select optimal information or advertisements based on a user's interests and attributes. The selection unit selects appropriate information or advertisements based on the information analyzed by the analysis unit. For example, the selection unit can use AI to select optimal information or advertisements based on a user's interests and attributes. The display unit displays the selected information or advertisements on a digital signage device. For example, the display unit can provide information visually using an electronic signage or an interactive display. The linking unit simultaneously displays a commercial from a vendor of a specific product when displaying an advertisement for that product. For example, the linking unit can use AI to associate and display product advertisements with vendor commercials. The connection unit automatically connects the digital signage device to the Internet and updates information in real time. For example, the connection unit can connect the digital signage device to the Internet using a Wi-Fi connection or a Bluetooth (registered trademark) connection, allowing the digital signage system according to the embodiment to provide the latest information at all times. This allows the digital signage system according to the embodiment to directly provide people in specific locations with the information they want, maximizing the effectiveness of advertising.

[0030] The collection unit can identify the attributes or interests of people in a location using smartphone location information or Wi-Fi connection information. The collection unit can identify the attributes and interests of people in a location using, for example, smartphone location information. For example, the collection unit can acquire GPS information to identify the user's current location. The collection unit can also identify the attributes and interests of people in a location using Wi-Fi connection information. For example, the collection unit can identify the user's location based on the connected SSID and signal strength. This can identify the attributes and interests of people in a specific location, thereby providing more appropriate information. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input the smartphone's location information into AI and have the AI ​​identify the user's attributes and interests.

[0031] The analysis unit can analyze the collected information and select appropriate information or advertisements. The analysis unit, for example, analyzes the collected information and selects appropriate information or advertisements. For example, the analysis unit can use AI to analyze the collected data and select optimal information or advertisements based on the user's interests and attributes. The analysis unit can also analyze the user's behavioral patterns based on the collected information and select optimal information or advertisements. For example, the analysis unit can analyze the user's past behavioral history and select the most relevant information or advertisements. This allows optimal information or advertisements to be selected by analyzing the collected information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​select optimal information or advertisements.

[0032] The display unit can display the selected information or advertisement on a digital signage device. The display unit, for example, displays the selected information or advertisement on the digital signage device. For example, the display unit can provide information visually using an electronic signage or an interactive display. The display unit can also update the selected information or advertisement in real time to always provide the latest information. For example, the display unit is connected to the Internet and can automatically obtain and display the latest information. This allows the selected information or advertisement to be displayed on the digital signage device, thereby providing the information visually. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the selected information or advertisement into AI and have the AI ​​generate the display content.

[0033] The linking unit can simultaneously display a commercial from a seller of a specific product when displaying an advertisement for the specific product. For example, the linking unit can simultaneously display a commercial from a seller of the specific product when displaying an advertisement for the specific product. For example, the linking unit can use AI to associate and display an advertisement for the product with a commercial from the seller. The linking unit can also display an advertisement for the product and a commercial from the seller in a consistent manner, thereby achieving effective marketing. For example, the linking unit can simultaneously display an advertisement for the product and a commercial from the seller to maintain consistency in the advertisements. As a result, consistency in the advertisements is maintained by simultaneously displaying an advertisement for the specific product and a commercial from the seller. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input an advertisement for the product and a commercial from the seller into AI and have the AI ​​perform the association processing.

[0034] The connection unit can automatically connect the digital signage device to the Internet and update information in real time. For example, the connection unit can automatically connect the digital signage device to the Internet and update information in real time. For example, the connection unit can connect the digital signage device to the Internet using a Wi-Fi connection or a Bluetooth connection to always provide the latest information. The connection unit can also ensure stability of the Internet connection and prevent interruptions in information updates. For example, the connection unit can ensure redundancy of the Internet connection by using multiple connection means. This allows the digital signage device to automatically connect to the Internet and always provide the latest information. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input Internet connection settings into AI and have AI manage the connection.

[0035] The collection unit can analyze the user's past behavioral history and select the optimal collection method when collecting data. For example, the collection unit analyzes the user's past behavioral history and selects the optimal collection method when collecting data. For example, the collection unit selects the optimal collection method based on information about places the user has visited in the past. The collection unit can also analyze the user's past behavioral patterns and suggest the most efficient collection method. The collection unit can also prioritize the use of information collection means that the user has used in the past. In this way, the optimal collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history into AI and have the AI ​​select the optimal collection method.

[0036] The collection unit can perform filtering based on the user's current activity status and areas of interest at the time of collection. The collection unit, for example, performs filtering based on the user's current activity status and areas of interest at the time of collection. For example, the collection unit prioritizes collecting information related to events in which the user is currently participating. The collection unit can also collect only relevant information based on the user's areas of interest. The collection unit can also filter unnecessary information taking into account the user's current activity status. In this way, highly relevant information can be collected by filtering based on the user's current activity status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current activity status and areas of interest into AI and have the AI ​​perform the filtering process.

[0037] The collection unit can select the optimal collection means according to the user's device information at the time of collection. For example, the collection unit selects the optimal collection means according to the user's device information (smartphone, tablet, etc.) at the time of collection. For example, if the user is using a smartphone, the collection unit collects information based on location information. Furthermore, if the user is using a tablet, the collection unit can collect information optimized for a large screen. Furthermore, if the user is using a smartwatch, the collection unit can collect concise and highly visible information. This allows information to be collected efficiently by selecting the optimal collection means according to the user's device information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's device information into AI and have the AI ​​select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting store information near the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about tourist spots. Furthermore, if the user is in a specific area, the collection unit can prioritize collecting news and event information related to that area. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0039] The collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can collect information related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's social media activities into AI and have the AI ​​collect related information.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit optimizes the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use preferred information collection means based on the user's past feedback. The collection unit can also analyze the user's past feedback and adjust the type of information to be collected. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into AI and have the AI ​​customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to AI and have the AI ​​adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm based on purchase history to product information. The analysis unit can also apply an analysis algorithm based on trend analysis to news information. The analysis unit can also apply an analysis algorithm based on participation history to event information. In this way, applying different analysis algorithms depending on the category of information enables highly accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into AI and have the AI ​​apply different analysis algorithms.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also select a highly accurate analysis method from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time when the information was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the information was collected during analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also lower the analysis priority of older information. The analysis unit can also adjust the order of analysis depending on the time when the information was collected. This enables efficient analysis by determining the analysis priority based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information collection time data into AI and have the AI ​​determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone the order of analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data into AI and have the AI ​​adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, the analysis unit can avoid technical terms if the user does not have technical expertise. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms.

[0047] The selection unit can improve the accuracy of selection by taking into account the interrelationships of information when making a selection. The selection unit can improve the accuracy of selection by taking into account the interrelationships of information when making a selection, for example. For example, the selection unit preferentially selects highly related information. The selection unit can also analyze the interrelationships of information and select optimal information. The selection unit can also improve the accuracy of selection based on the interrelationships of information. In this way, the accuracy of selection can be improved by taking the interrelationships of information into consideration. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input data on the interrelationships of information into AI and cause the AI ​​to improve the accuracy of selection.

[0048] The selection unit can make a selection while taking into consideration the attribute information of the information submitter. For example, the selection unit makes a selection while taking into consideration the attribute information of the information submitter. For example, the selection unit selects information based on the reliability of the submitter. The selection unit can also select information while taking into consideration the submitter's level of expertise. The selection unit can also select information based on the submitter's past performance. In this way, by taking into consideration the attribute information of the information submitter, highly reliable information can be selected. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the attribute information of the information submitter into AI and have the AI ​​perform the selection processing.

[0049] The selection unit can weight the selection based on the frequency of information submission at the time of selection. The selection unit, for example, weights the selection based on the frequency of information submission at the time of selection. For example, the selection unit preferentially selects information that is submitted frequently. The selection unit can also lower the weight of the selection for information that is submitted less frequently. The selection unit can also weight the selection according to the frequency of information submission. In this way, weighting the selection based on the frequency of information submission enables efficient information selection. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input information submission frequency data into AI and have the AI ​​perform the weighting of the selection.

[0050] The selection unit can make a selection taking into consideration the geographical distribution of information. For example, the selection unit makes a selection taking into consideration the geographical distribution of information. For example, the selection unit preferentially selects information near the user's current location. Furthermore, if the user is traveling, the selection unit can preferentially select information about the travel destination. Furthermore, if the user is in a specific area, the selection unit can preferentially select information related to that area. In this way, by taking the geographical distribution of information into consideration, highly relevant information can be selected. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographical distribution data of information to AI and have the AI ​​perform the selection processing.

[0051] The selection unit can improve the accuracy of the selection by referring to literature related to the information when making a selection. The selection unit can improve the accuracy of the selection by referring to literature related to the information when making a selection, for example. For example, the selection unit evaluates the reliability of the information based on the related literature. The selection unit can also improve the accuracy of the information selection by referring to the related literature. The selection unit can also analyze the related literature and select optimal information. In this way, the accuracy of the selection can be improved by referring to literature related to the information. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input literature data related to the information into AI and cause the AI ​​to improve the accuracy of the selection.

[0052] The selection unit can make a selection taking into consideration the market value of the information. For example, the selection unit makes a selection taking into consideration the market value of the information. For example, the selection unit preferentially selects information with high market value. The selection unit can also lower the selection priority for information with low market value. The selection unit can also weight the selection based on the market value of the information. In this way, it is possible to select information with high value by taking into consideration the market value of the information. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input market value data of the information into AI and have the AI ​​perform the selection processing.

[0053] The display unit can adjust the level of detail of the display based on the importance of the information when displaying the information. The display unit, for example, adjusts the level of detail of the display based on the importance of the information when displaying the information. For example, the display unit displays information of high importance in detail. The display unit can also display information of low importance in a concise manner. The display unit can also adjust the level of detail of the display according to the importance of the information. This enables efficient information provision by adjusting the level of detail of the display based on the importance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information importance data to AI and have the AI ​​adjust the level of detail of the display.

[0054] The display unit can apply different display algorithms depending on the category of information when displaying the information. For example, the display unit applies different display algorithms depending on the category of information when displaying the information. For example, the display unit applies a display algorithm based on purchase history to product information. The display unit can also apply a display algorithm based on trend analysis to news information. The display unit can also apply a display algorithm based on participation history to event information. In this way, by applying different display algorithms depending on the category of information, it is possible to provide highly accurate information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information category data into AI and cause the AI ​​to apply different display algorithms.

[0055] The display unit can improve the accuracy of the display by referring to the user's past display results when displaying. For example, the display unit can improve the accuracy of the display by referring to the user's past display results when displaying. For example, the display unit can optimize a display algorithm based on the user's past display results. The display unit can also select a highly accurate display method from the user's past display results. The display unit can also analyze the user's past display results and improve the accuracy of the display. In this way, the accuracy of the display can be improved by referring to the user's past display results. Some or all of the above-mentioned processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the user's past display result data into AI and have the AI ​​improve the accuracy of the display.

[0056] The display unit can determine the display priority based on the time of submission of the information when displaying the information. The display unit, for example, determines the display priority based on the time of submission of the information when displaying the information. For example, the display unit prioritizes display of the latest information. The display unit can also lower the display priority of older information. The display unit can also adjust the display order according to the time of submission of the information. This enables efficient information provision by determining the display priority based on the time of submission of the information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information submission time data into AI and have the AI ​​determine the display priority.

[0057] The display unit can adjust the display order based on the relevance of the information when displaying the information. The display unit, for example, adjusts the display order based on the relevance of the information when displaying the information. For example, the display unit prioritizes display of highly relevant information. The display unit can also postpone the display order of less relevant information. The display unit can also adjust the display order according to the relevance of the information. This enables efficient information provision by adjusting the display order based on the relevance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input relevance data of the information to AI and have the AI ​​adjust the display order.

[0058] The display unit can adjust the use of technical terms displayed in accordance with the user's level of expertise during display. For example, the display unit can adjust the use of technical terms displayed in accordance with the user's level of expertise during display. For example, if the user has technical expertise, the display unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the display unit can avoid technical terms. Furthermore, the display unit can adjust the use of technical terms displayed in accordance with the user's level of expertise. By adjusting the use of technical terms displayed in accordance with the user's level of expertise, more understandable information can be provided. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without AI. For example, the display unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms.

[0059] The linking unit can improve the accuracy of linking by taking into account the interrelationships of information when linking. For example, the linking unit can improve the accuracy of linking by taking into account the interrelationships of information when linking. For example, the linking unit preferentially links highly related information. The linking unit can also analyze the interrelationships of information and link optimal information. The linking unit can also improve the accuracy of linking based on the interrelationships of information. In this way, the accuracy of linking can be improved by taking the interrelationships of information into consideration. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input interrelationship data of information into AI and cause the AI ​​to improve the accuracy of linking.

[0060] The linking unit can perform linking by taking into consideration attribute information of the information submitter. For example, the linking unit performs linking by taking into consideration attribute information of the information submitter. For example, the linking unit links information based on the reliability of the submitter. The linking unit can also link information by taking into consideration the submitter's level of expertise. The linking unit can also link information based on the submitter's past performance. In this way, by taking into consideration the attribute information of the information submitter, highly reliable information can be linked. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input attribute information of the information submitter into AI and have the AI ​​perform the linking processing.

[0061] The linking unit can weight the links based on the frequency of information submission when linking. For example, the linking unit weights the links based on the frequency of information submission when linking. For example, the linking unit preferentially links information that is submitted more frequently. The linking unit can also lower the weight of the links for information that is submitted less frequently. The linking unit can also weight the links according to the frequency of information submission. In this way, efficient information linking is possible by weighting the links based on the frequency of information submission. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input information submission frequency data into AI and have the AI ​​perform the link weighting.

[0062] The linking unit can perform linking taking into account the geographical distribution of information when linking. For example, the linking unit can perform linking taking into account the geographical distribution of information when linking. For example, the linking unit prioritizes linking information near the user's current location. Furthermore, if the user is traveling, the linking unit can prioritize linking information about the travel destination. Furthermore, if the user is in a specific area, the linking unit can prioritize linking information related to that area. In this way, by taking the geographical distribution of information into account, highly relevant information can be linked. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input geographical distribution data of information into AI and have the AI ​​perform the linking processing.

[0063] The linking unit can improve the accuracy of linking by referring to literature related to the information when linking. For example, the linking unit can improve the accuracy of linking by referring to literature related to the information when linking. For example, the linking unit evaluates the reliability of information based on the literature related to the information. The linking unit can also improve the accuracy of linking information by referring to the literature related to the information. The linking unit can also analyze the literature related to the information and link optimal information. In this way, the accuracy of linking can be improved by referring to literature related to the information. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input literature data related to the information into AI and cause the AI ​​to improve the accuracy of linking.

[0064] The linking unit can perform linking taking into account the market value of the information when linking. For example, the linking unit can perform linking taking into account the market value of the information when linking. For example, the linking unit prioritizes linking information with high market value. The linking unit can also lower the priority of linking for information with low market value. The linking unit can also weight the linking based on the market value of the information. In this way, by taking the market value of the information into account, information with high value can be linked. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input market value data of the information into AI and have the AI ​​perform the linking processing.

[0065] The connection unit can optimize the connection algorithm by referring to past connection history when connecting. The connection unit, for example, optimizes the connection algorithm by referring to past connection history when connecting. For example, the connection unit selects an optimal connection algorithm based on the user's past connection history. The connection unit can also apply an algorithm that improves the connection success rate from the user's past connection history. The connection unit can also analyze the user's past connection history and optimize the connection algorithm. In this way, by referring to the past connection history, the connection algorithm can be optimized and the connection success rate can be improved. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input past connection history data into AI and cause the AI ​​to optimize the connection algorithm.

[0066] The connection unit can update the connection method by reflecting user feedback at the time of connection. For example, the connection unit updates the connection method by reflecting user feedback at the time of connection. For example, the connection unit improves the connection procedure based on user feedback. The connection unit can also introduce a method for improving the success rate of connection based on user feedback. The connection unit can also analyze user feedback and optimize the connection method. In this way, the connection method can be updated by reflecting user feedback, thereby improving the success rate of connection. Some or all of the above-mentioned processing in the connection unit may be performed using AI, for example, or may be performed without using AI. For example, the connection unit can input user feedback data into AI and have the AI ​​execute the update of the connection method.

[0067] The connection unit can weight the connection data based on the time of information submission at the time of connection. For example, the connection unit weights the connection data based on the time of information submission at the time of connection. For example, the connection unit prioritizes connection of the most recent information. The connection unit can also lower the connection weight for older information. The connection unit can also adjust the weighting of the connection data according to the time of information submission. As a result, efficient connection is possible by weighting the connection data based on the time of information submission. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input information submission time data to AI and have the AI ​​perform weighting of the connection data.

[0068] The connection unit can integrate information from different data sources to enrich the connection data when connecting. For example, the connection unit integrates information from different data sources to enrich the connection data when connecting. For example, the connection unit integrates information from different data sources to enrich the connection data. The connection unit can also analyze information from different data sources and select optimal connection data. The connection unit can also improve the accuracy of the connection data based on information from different data sources. In this way, by integrating information from different data sources, the connection data can be enriched and the accuracy of the connection can be improved. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input information from different data sources into AI and have the AI ​​integrate the connection data.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The collection unit can analyze the user's past behavioral history and select the optimal collection method when collecting data. For example, the collection unit selects the optimal collection method based on information about places the user has visited in the past. The collection unit can also analyze the user's past behavioral patterns and suggest the most efficient collection method. The collection unit can also prioritize the use of information collection means that the user has used in the past. In this way, the optimal collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history into AI and have the AI ​​select the optimal collection method.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. The analysis unit can also adjust the depth of the analysis depending on the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input information importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0072] The selection unit can improve the accuracy of the selection by taking into account the interrelationships of information when making a selection. For example, the selection unit preferentially selects highly related information. The selection unit can also analyze the interrelationships of information and select optimal information. The selection unit can also improve the accuracy of the selection based on the interrelationships of information. In this way, the accuracy of the selection can be improved by taking the interrelationships of information into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input data on the interrelationships of information into AI and cause the AI ​​to improve the accuracy of the selection.

[0073] The display unit can adjust the level of detail of the display based on the importance of the information when displaying the information. For example, the display unit displays information of high importance in detail. The display unit can also display information of low importance in a concise manner. The display unit can also adjust the level of detail of the display according to the importance of the information. This enables efficient information provision by adjusting the level of detail of the display based on the importance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information importance data into AI and have the AI ​​adjust the level of detail of the display.

[0074] The connection unit can optimize the connection algorithm by referring to past connection history when connecting. For example, the connection unit selects an optimal connection algorithm based on the user's past connection history. The connection unit can also apply an algorithm that improves the connection success rate from the user's past connection history. The connection unit can also analyze the user's past connection history and optimize the connection algorithm. In this way, by referring to the past connection history, the connection algorithm can be optimized and the connection success rate can be improved. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input past connection history data into AI and cause the AI ​​to optimize the connection algorithm.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects information about people in a specific location. For example, the collection unit can use smartphone location information and Wi-Fi connection information to identify the attributes and interests of people in that location. The collection unit can also use cameras and sensors to detect people's movements in real time. Step 2: The analysis unit analyzes the collected information and selects the most appropriate information and advertisements. For example, the analysis unit can use AI to analyze the collected data and select the most appropriate information and advertisements based on the user's interests and attributes. Step 3: The selection unit selects appropriate information or advertisements based on the information analyzed by the analysis unit. For example, the selection unit can use AI to select optimal information or advertisements based on the user's interests and attributes. Step 4: The display unit displays the selected information or advertisement on a digital signage device. For example, the display unit can provide information visually using an electronic sign or an interactive display. Step 5: When displaying an advertisement for a specific product, the linking unit simultaneously displays a commercial from the product's distributor. For example, the linking unit can use AI to associate the product advertisement with the distributor's commercial and display it. Step 6: The connection unit automatically connects the digital signage device to the Internet and updates information in real time. For example, the connection unit can connect the digital signage device to the Internet using a Wi-Fi connection or Bluetooth connection, allowing it to always provide the latest information.

[0077] (Example 2) A digital signage system according to an embodiment of the present invention directly provides desired information to people in a specific location. This digital signage system collects and analyzes information, selects and displays the most appropriate information and advertisements, and is also equipped with a function for linking client commercials and an automatic connection system for communication devices. This allows the digital signage system to directly provide desired information to people in a specific location and maximize the effectiveness of advertisements. For example, the digital signage system uses smartphone location information and Wi-Fi connection information to identify the attributes and interests of people in that location. The collected information is then analyzed to select the most appropriate information and advertisements. The selected information and advertisements are then displayed on the digital signage device. Furthermore, when displaying an advertisement for a specific product, a commercial from the product's vendor is simultaneously displayed. The digital signage device automatically connects to the Internet and updates information in real time, allowing the system to always provide the latest information.

[0078] A digital signage system according to an embodiment includes a collection unit, an analysis unit, a selection unit, a display unit, a linking unit, and a connection unit. The collection unit collects information about people in a specific location. For example, the collection unit can identify the attributes and interests of people in the location using smartphone location information and Wi-Fi connection information. The collection unit can also detect people's movements in real time using a camera or sensor. The analysis unit analyzes the collected information and selects optimal information or advertisements. For example, the analysis unit can use AI to analyze the collected data and select optimal information or advertisements based on a user's interests and attributes. The selection unit selects appropriate information or advertisements based on the information analyzed by the analysis unit. For example, the selection unit can use AI to select optimal information or advertisements based on a user's interests and attributes. The display unit displays the selected information or advertisements on a digital signage device. For example, the display unit can provide information visually using an electronic signage or an interactive display. The linking unit simultaneously displays a commercial from a vendor of a specific product when displaying an advertisement for that product. For example, the linking unit can use AI to associate and display product advertisements with vendor commercials. The connection unit automatically connects the digital signage device to the Internet and updates information in real time. For example, the connection unit can connect the digital signage device to the Internet using a Wi-Fi connection or Bluetooth connection to always provide the latest information. As a result, the digital signage system according to the embodiment can directly provide people in specific locations with the information they want, maximizing the effectiveness of advertising.

[0079] The collection unit can identify the attributes or interests of people in a location using smartphone location information or Wi-Fi connection information. The collection unit can identify the attributes and interests of people in a location using, for example, smartphone location information. For example, the collection unit can acquire GPS information to identify the user's current location. The collection unit can also identify the attributes and interests of people in a location using Wi-Fi connection information. For example, the collection unit can identify the user's location based on the connected SSID and signal strength. This can identify the attributes and interests of people in a specific location, thereby providing more appropriate information. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input the smartphone's location information into AI and have the AI ​​identify the user's attributes and interests.

[0080] The analysis unit can analyze the collected information and select appropriate information or advertisements. The analysis unit, for example, analyzes the collected information and selects appropriate information or advertisements. For example, the analysis unit can use AI to analyze the collected data and select optimal information or advertisements based on the user's interests and attributes. The analysis unit can also analyze the user's behavioral patterns based on the collected information and select optimal information or advertisements. For example, the analysis unit can analyze the user's past behavioral history and select the most relevant information or advertisements. This allows optimal information or advertisements to be selected by analyzing the collected information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​select optimal information or advertisements.

[0081] The display unit can display the selected information or advertisement on a digital signage device. The display unit, for example, displays the selected information or advertisement on the digital signage device. For example, the display unit can provide information visually using an electronic signage or an interactive display. The display unit can also update the selected information or advertisement in real time to always provide the latest information. For example, the display unit is connected to the Internet and can automatically obtain and display the latest information. This allows the selected information or advertisement to be displayed on the digital signage device, thereby providing the information visually. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the selected information or advertisement into AI and have the AI ​​generate the display content.

[0082] The linking unit can simultaneously display a commercial from a seller of a specific product when displaying an advertisement for the specific product. For example, the linking unit can simultaneously display a commercial from a seller of the specific product when displaying an advertisement for the specific product. For example, the linking unit can use AI to associate and display an advertisement for the product with a commercial from the seller. The linking unit can also display an advertisement for the product and a commercial from the seller in a consistent manner, thereby achieving effective marketing. For example, the linking unit can simultaneously display an advertisement for the product and a commercial from the seller to maintain consistency in the advertisements. As a result, consistency in the advertisements is maintained by simultaneously displaying an advertisement for the specific product and a commercial from the seller. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input an advertisement for the product and a commercial from the seller into AI and have the AI ​​perform the association processing.

[0083] The connection unit can automatically connect the digital signage device to the Internet and update information in real time. For example, the connection unit can automatically connect the digital signage device to the Internet and update information in real time. For example, the connection unit can connect the digital signage device to the Internet using a Wi-Fi connection or a Bluetooth connection to always provide the latest information. The connection unit can also ensure stability of the Internet connection and prevent interruptions in information updates. For example, the connection unit can ensure redundancy of the Internet connection by using multiple connection means. This allows the digital signage device to automatically connect to the Internet and always provide the latest information. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input Internet connection settings into AI and have AI manage the connection.

[0084] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit reduces the frequency of information collection and collects only necessary information. Furthermore, when the user is excited, the collection unit can increase the frequency of information collection and collect information in real time. Furthermore, when the user is stressed, the collection unit can delay the timing of information collection to reduce the burden on the user. This allows more appropriate information to be collected by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and cause the AI ​​to adjust the timing of information collection.

[0085] The collection unit can analyze the user's past behavioral history and select the optimal collection method when collecting data. For example, the collection unit analyzes the user's past behavioral history and selects the optimal collection method when collecting data. For example, the collection unit selects the optimal collection method based on information about places the user has visited in the past. The collection unit can also analyze the user's past behavioral patterns and suggest the most efficient collection method. The collection unit can also prioritize the use of information collection means that the user has used in the past. In this way, the optimal collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history into AI and have the AI ​​select the optimal collection method.

[0086] The collection unit can perform filtering based on the user's current activity status and areas of interest at the time of collection. The collection unit, for example, performs filtering based on the user's current activity status and areas of interest at the time of collection. For example, the collection unit prioritizes collecting information related to events in which the user is currently participating. The collection unit can also collect only relevant information based on the user's areas of interest. The collection unit can also filter unnecessary information taking into account the user's current activity status. In this way, highly relevant information can be collected by filtering based on the user's current activity status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current activity status and areas of interest into AI and have the AI ​​perform the filtering process.

[0087] The collection unit can select the optimal collection means according to the user's device information at the time of collection. For example, the collection unit selects the optimal collection means according to the user's device information (smartphone, tablet, etc.) at the time of collection. For example, if the user is using a smartphone, the collection unit collects information based on location information. Furthermore, if the user is using a tablet, the collection unit can collect information optimized for a large screen. Furthermore, if the user is using a smartwatch, the collection unit can collect concise and highly visible information. This allows information to be collected efficiently by selecting the optimal collection means according to the user's device information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's device information into AI and have the AI ​​select the optimal collection means.

[0088] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting less important information. Furthermore, when the user is excited, the collection unit can also prioritize collecting more important information. Furthermore, when the user is stressed, the collection unit can collect only the minimum amount of information necessary. This allows more appropriate information to be collected by determining the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of information.

[0089] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting store information near the user's current location. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about tourist spots. Furthermore, if the user is in a specific area, the collection unit can prioritize collecting news and event information related to that area. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0090] The collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can collect information related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the user's social media activities into AI and have the AI ​​collect related information.

[0091] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit optimizes the collection method based on feedback provided by the user in the past. The collection unit can also preferentially use preferred information collection means based on the user's past feedback. The collection unit can also analyze the user's past feedback and adjust the type of information to be collected. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into AI and have the AI ​​customize the collection method.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. Furthermore, the analysis unit can provide concise and concise analysis results when the user is stressed. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the presentation method of the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to AI and have the AI ​​adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an analysis algorithm based on purchase history to product information. The analysis unit can also apply an analysis algorithm based on trend analysis to news information. The analysis unit can also apply an analysis algorithm based on participation history to event information. In this way, applying different analysis algorithms depending on the category of information enables highly accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data into AI and have the AI ​​apply different analysis algorithms.

[0095] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also select a highly accurate analysis method from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a short, concise analysis when the user is excited. The analysis unit can also perform a concise analysis when the user is stressed. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using an AI, or may be performed without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0097] The analysis unit can determine the analysis priority based on the time when the information was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the information was collected during analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also lower the analysis priority of older information. The analysis unit can also adjust the order of analysis depending on the time when the information was collected. This enables efficient analysis by determining the analysis priority based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information collection time data into AI and have the AI ​​determine the analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone the order of analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data into AI and have the AI ​​adjust the order of analysis.

[0099] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Furthermore, the analysis unit can avoid technical terms if the user does not have technical expertise. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms.

[0100] The selection unit can estimate the user's emotion and adjust the selection criteria based on the estimated user's emotion. For example, the selection unit can estimate the user's emotion and adjust the selection criteria based on the estimated user's emotion. For example, the selection unit can select detailed information when the user is relaxed. Furthermore, the selection unit can select visually stimulating information when the user is excited. Furthermore, the selection unit can select concise and to-the-point information when the user is stressed. This allows for adjusting the selection criteria according to the user's emotion, thereby selecting more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into an AI and have the AI ​​adjust the selection criteria.

[0101] The selection unit can improve the accuracy of selection by taking into account the interrelationships of information when making a selection. The selection unit can improve the accuracy of selection by taking into account the interrelationships of information when making a selection, for example. For example, the selection unit preferentially selects highly related information. The selection unit can also analyze the interrelationships of information and select optimal information. The selection unit can also improve the accuracy of selection based on the interrelationships of information. In this way, the accuracy of selection can be improved by taking the interrelationships of information into consideration. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input data on the interrelationships of information into AI and cause the AI ​​to improve the accuracy of selection.

[0102] The selection unit can make a selection while taking into consideration the attribute information of the information submitter. For example, the selection unit makes a selection while taking into consideration the attribute information of the information submitter. For example, the selection unit selects information based on the reliability of the submitter. The selection unit can also select information while taking into consideration the submitter's level of expertise. The selection unit can also select information based on the submitter's past performance. In this way, by taking into consideration the attribute information of the information submitter, highly reliable information can be selected. Some or all of the above-mentioned processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the attribute information of the information submitter into AI and have the AI ​​perform the selection processing.

[0103] The selection unit can weight the selection based on the frequency of information submission at the time of selection. The selection unit, for example, weights the selection based on the frequency of information submission at the time of selection. For example, the selection unit preferentially selects information that is submitted frequently. The selection unit can also lower the weight of the selection for information that is submitted less frequently. The selection unit can also weight the selection according to the frequency of information submission. In this way, weighting the selection based on the frequency of information submission enables efficient information selection. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input information submission frequency data into AI and have the AI ​​perform the weighting of the selection.

[0104] The selection unit can estimate the user's emotion and adjust the order in which the selection results are displayed based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the order in which the selection results are displayed based on the estimated user's emotion. For example, the selection unit can display detailed information first if the user is relaxed. Furthermore, the selection unit can also display visually stimulating information first if the user is excited. Furthermore, the selection unit can also display concise, to-the-point information first if the user is stressed. This allows for more appropriate information to be provided by adjusting the order in which the selection results are displayed based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's emotion data into an AI and have the AI ​​adjust the display order.

[0105] The selection unit can make a selection taking into consideration the geographical distribution of information. For example, the selection unit makes a selection taking into consideration the geographical distribution of information. For example, the selection unit preferentially selects information near the user's current location. Furthermore, if the user is traveling, the selection unit can preferentially select information about the travel destination. Furthermore, if the user is in a specific area, the selection unit can preferentially select information related to that area. In this way, by taking the geographical distribution of information into consideration, highly relevant information can be selected. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographical distribution data of information to AI and have the AI ​​perform the selection processing.

[0106] The selection unit can improve the accuracy of the selection by referring to literature related to the information when making a selection. The selection unit can improve the accuracy of the selection by referring to literature related to the information when making a selection, for example. For example, the selection unit evaluates the reliability of the information based on the related literature. The selection unit can also improve the accuracy of the information selection by referring to the related literature. The selection unit can also analyze the related literature and select optimal information. In this way, the accuracy of the selection can be improved by referring to literature related to the information. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input literature data related to the information into AI and cause the AI ​​to improve the accuracy of the selection.

[0107] The selection unit can make a selection taking into consideration the market value of the information. For example, the selection unit makes a selection taking into consideration the market value of the information. For example, the selection unit preferentially selects information with high market value. The selection unit can also lower the selection priority for information with low market value. The selection unit can also weight the selection based on the market value of the information. In this way, it is possible to select information with high value by taking into consideration the market value of the information. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input market value data of the information into AI and have the AI ​​perform the selection processing.

[0108] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, the display unit can display detailed information when the user is relaxed. Furthermore, the display unit can display visually stimulating information when the user is excited. Furthermore, the display unit can display concise, to-the-point information when the user is stressed. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or without an AI. For example, the display unit can input the user's emotion data into an AI and have the AI ​​adjust the display method.

[0109] The display unit can adjust the level of detail of the display based on the importance of the information when displaying the information. The display unit, for example, adjusts the level of detail of the display based on the importance of the information when displaying the information. For example, the display unit displays information of high importance in detail. The display unit can also display information of low importance in a concise manner. The display unit can also adjust the level of detail of the display according to the importance of the information. This enables efficient information provision by adjusting the level of detail of the display based on the importance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information importance data to AI and have the AI ​​adjust the level of detail of the display.

[0110] The display unit can apply different display algorithms depending on the category of information when displaying the information. For example, the display unit applies different display algorithms depending on the category of information when displaying the information. For example, the display unit applies a display algorithm based on purchase history to product information. The display unit can also apply a display algorithm based on trend analysis to news information. The display unit can also apply a display algorithm based on participation history to event information. In this way, by applying different display algorithms depending on the category of information, it is possible to provide highly accurate information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information category data into AI and cause the AI ​​to apply different display algorithms.

[0111] The display unit can improve the accuracy of the display by referring to the user's past display results when displaying. For example, the display unit can improve the accuracy of the display by referring to the user's past display results when displaying. For example, the display unit can optimize a display algorithm based on the user's past display results. The display unit can also select a highly accurate display method from the user's past display results. The display unit can also analyze the user's past display results and improve the accuracy of the display. In this way, the accuracy of the display can be improved by referring to the user's past display results. Some or all of the above-mentioned processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the user's past display result data into AI and have the AI ​​improve the accuracy of the display.

[0112] The display unit can estimate the user's emotion and adjust the display length based on the estimated user's emotion. For example, the display unit estimates the user's emotion and adjusts the display length based on the estimated user's emotion. For example, when the user is relaxed, the display unit displays detailed information for a long time. When the user is excited, the display unit can also display short, concise information for a short time. When the user is stressed, the display unit can also display concise information for a short time. This allows for more appropriate information to be provided by adjusting the display length according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit may input the user's emotion data into an AI and have the AI ​​adjust the display length.

[0113] The display unit can determine the display priority based on the time of submission of the information when displaying the information. The display unit, for example, determines the display priority based on the time of submission of the information when displaying the information. For example, the display unit prioritizes display of the latest information. The display unit can also lower the display priority of older information. The display unit can also adjust the display order according to the time of submission of the information. This enables efficient information provision by determining the display priority based on the time of submission of the information. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information submission time data into AI and have the AI ​​determine the display priority.

[0114] The display unit can adjust the display order based on the relevance of the information when displaying the information. The display unit, for example, adjusts the display order based on the relevance of the information when displaying the information. For example, the display unit prioritizes display of highly relevant information. The display unit can also postpone the display order of less relevant information. The display unit can also adjust the display order according to the relevance of the information. This enables efficient information provision by adjusting the display order based on the relevance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input relevance data of the information to AI and have the AI ​​adjust the display order.

[0115] The display unit can adjust the use of technical terms displayed in accordance with the user's level of expertise during display. For example, the display unit can adjust the use of technical terms displayed in accordance with the user's level of expertise during display. For example, if the user has technical expertise, the display unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the display unit can avoid technical terms. Furthermore, the display unit can adjust the use of technical terms displayed in accordance with the user's level of expertise. By adjusting the use of technical terms displayed in accordance with the user's level of expertise, more understandable information can be provided. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without AI. For example, the display unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terms.

[0116] The linking unit can estimate the user's emotion and adjust the linking method based on the estimated user's emotion. For example, the linking unit can estimate the user's emotion and adjust the linking method based on the estimated user's emotion. For example, if the user is relaxed, the linking unit can link detailed information. If the user is excited, the linking unit can link visually stimulating information. If the user is stressed, the linking unit can link concise, to-the-point information. This allows for adjusting the linking method according to the user's emotion to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the linking unit can be performed using an AI, for example, or without an AI. For example, the linking unit can input the user's emotion data into an AI and have the AI ​​adjust the linking method.

[0117] The linking unit can improve the accuracy of linking by taking into account the interrelationships of information when linking. For example, the linking unit can improve the accuracy of linking by taking into account the interrelationships of information when linking. For example, the linking unit preferentially links highly related information. The linking unit can also analyze the interrelationships of information and link optimal information. The linking unit can also improve the accuracy of linking based on the interrelationships of information. In this way, the accuracy of linking can be improved by taking the interrelationships of information into consideration. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input interrelationship data of information into AI and cause the AI ​​to improve the accuracy of linking.

[0118] The linking unit can perform linking by taking into consideration attribute information of the information submitter. For example, the linking unit performs linking by taking into consideration attribute information of the information submitter. For example, the linking unit links information based on the reliability of the submitter. The linking unit can also link information by taking into consideration the submitter's level of expertise. The linking unit can also link information based on the submitter's past performance. In this way, by taking into consideration the attribute information of the information submitter, highly reliable information can be linked. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input attribute information of the information submitter into AI and have the AI ​​perform the linking processing.

[0119] The linking unit can weight the links based on the frequency of information submission when linking. For example, the linking unit weights the links based on the frequency of information submission when linking. For example, the linking unit preferentially links information that is submitted more frequently. The linking unit can also lower the weight of the links for information that is submitted less frequently. The linking unit can also weight the links according to the frequency of information submission. In this way, efficient information linking is possible by weighting the links based on the frequency of information submission. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input information submission frequency data into AI and have the AI ​​perform the link weighting.

[0120] The linking unit can estimate the user's emotion and adjust the order in which the linking results are displayed based on the estimated user's emotion. The linking unit, for example, estimates the user's emotion and adjusts the order in which the linking results are displayed based on the estimated user's emotion. For example, if the user is relaxed, the linking unit can display detailed information first. Also, if the user is excited, the linking unit can display visually stimulating information first. Also, if the user is stressed, the linking unit can display concise, to-the-point information first. This allows for more appropriate information to be provided by adjusting the order in which the linking results are displayed based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit can be performed using, for example, an AI. For example, the linking unit can input the user's emotion data into an AI and have the AI ​​adjust the display order.

[0121] The linking unit can perform linking taking into account the geographical distribution of information when linking. For example, the linking unit can perform linking taking into account the geographical distribution of information when linking. For example, the linking unit prioritizes linking information near the user's current location. Furthermore, if the user is traveling, the linking unit can prioritize linking information about the travel destination. Furthermore, if the user is in a specific area, the linking unit can prioritize linking information related to that area. In this way, by taking the geographical distribution of information into account, highly relevant information can be linked. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input geographical distribution data of information into AI and have the AI ​​perform the linking processing.

[0122] The linking unit can improve the accuracy of linking by referring to literature related to the information when linking. For example, the linking unit can improve the accuracy of linking by referring to literature related to the information when linking. For example, the linking unit evaluates the reliability of information based on the literature related to the information. The linking unit can also improve the accuracy of linking information by referring to the literature related to the information. The linking unit can also analyze the literature related to the information and link optimal information. In this way, the accuracy of linking can be improved by referring to literature related to the information. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input literature data related to the information into AI and cause the AI ​​to improve the accuracy of linking.

[0123] The linking unit can perform linking taking into account the market value of the information when linking. For example, the linking unit can perform linking taking into account the market value of the information when linking. For example, the linking unit prioritizes linking information with high market value. The linking unit can also lower the priority of linking for information with low market value. The linking unit can also weight the linking based on the market value of the information. In this way, by taking the market value of the information into account, information with high value can be linked. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, AI, or can be performed without using AI. For example, the linking unit can input market value data of the information into AI and have the AI ​​perform the linking processing.

[0124] The connection unit can estimate the user's emotion and adjust the connection method based on the estimated user's emotion. For example, the connection unit can estimate the user's emotion and adjust the connection method based on the estimated user's emotion. For example, the connection unit can provide a detailed connection procedure when the user is relaxed. The connection unit can also provide a concise and quick connection procedure when the user is excited. The connection unit can also provide a simple and intuitive connection procedure when the user is stressed. This allows the connection method to be adjusted according to the user's emotion, thereby providing a more appropriate connection procedure. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the connection unit can be performed using an AI, for example, or without an AI. For example, the connection unit can input the user's emotion data into an AI and have the AI ​​adjust the connection method.

[0125] The connection unit can optimize the connection algorithm by referring to past connection history when connecting. The connection unit, for example, optimizes the connection algorithm by referring to past connection history when connecting. For example, the connection unit selects an optimal connection algorithm based on the user's past connection history. The connection unit can also apply an algorithm that improves the connection success rate from the user's past connection history. The connection unit can also analyze the user's past connection history and optimize the connection algorithm. In this way, by referring to the past connection history, the connection algorithm can be optimized and the connection success rate can be improved. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input past connection history data into AI and cause the AI ​​to optimize the connection algorithm.

[0126] The connection unit can update the connection method by reflecting user feedback at the time of connection. For example, the connection unit updates the connection method by reflecting user feedback at the time of connection. For example, the connection unit improves the connection procedure based on user feedback. The connection unit can also introduce a method for improving the success rate of connection based on user feedback. The connection unit can also analyze user feedback and optimize the connection method. In this way, the connection method can be updated by reflecting user feedback, thereby improving the success rate of connection. Some or all of the above-mentioned processing in the connection unit may be performed using AI, for example, or may be performed without using AI. For example, the connection unit can input user feedback data into AI and have the AI ​​execute the update of the connection method.

[0127] The connection unit can estimate the user's emotion and adjust the frequency of connections based on the estimated user's emotion. The connection unit, for example, estimates the user's emotion and adjusts the frequency of connections based on the estimated user's emotion. For example, the connection unit can decrease the frequency of connections when the user is relaxed. The connection unit can also increase the frequency of connections when the user is excited. The connection unit can also adjust the frequency of connections to reduce the burden on the user when the user is stressed. This allows for a more appropriate connection frequency to be provided by adjusting the frequency of connections according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the connection unit can be performed using an AI, for example, or without an AI. For example, the connection unit can input the user's emotion data into an AI and have the AI ​​adjust the connection frequency.

[0128] The connection unit can weight the connection data based on the time of information submission at the time of connection. For example, the connection unit weights the connection data based on the time of information submission at the time of connection. For example, the connection unit prioritizes connection of the most recent information. The connection unit can also lower the connection weight for older information. The connection unit can also adjust the weighting of the connection data according to the time of information submission. As a result, efficient connection is possible by weighting the connection data based on the time of information submission. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input information submission time data to AI and have the AI ​​perform weighting of the connection data.

[0129] The connection unit can integrate information from different data sources to enrich the connection data when connecting. For example, the connection unit integrates information from different data sources to enrich the connection data when connecting. For example, the connection unit integrates information from different data sources to enrich the connection data. The connection unit can also analyze information from different data sources and select optimal connection data. The connection unit can also improve the accuracy of the connection data based on information from different data sources. In this way, by integrating information from different data sources, the connection data can be enriched and the accuracy of the connection can be improved. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input information from different data sources into AI and have the AI ​​integrate the connection data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, selection unit, display unit, linking unit, and connection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal information and advertisements based on the analysis results. The display unit is realized by the output device 40 of the smart device 14 and displays the selected information and advertisements. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and displays product advertisements in association with vendor commercials. The connection unit connects to the Internet using the communication I / F 44 of the smart device 14 and updates information in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, selection unit, display unit, linking unit, and connection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the smart glasses 214, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal information and advertisements based on the analysis results. The display unit is realized by the speaker 240 of the smart glasses 214 and displays the selected information and advertisements. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and displays product advertisements in association with vendor commercials. The connection unit connects to the Internet using the communication I / F 44 of the smart glasses 214 and updates information in real time. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, selection unit, display unit, linking unit, and connection unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the headset type terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal information and advertisements based on the analysis results. The display unit is realized by the display 343 of the headset type terminal 314 and displays the selected information and advertisements. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and displays product advertisements in association with vendor commercials. The connection unit connects to the Internet using the communication I / F 44 of the headset type terminal 314 and updates information in real time. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, selection unit, display unit, linking unit, and connection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information using the camera 42 and communication I / F 44 of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects optimal information and advertisements based on the analysis results. The display unit is realized by the speaker 240 of the robot 414 and displays the selected information and advertisements. The linking unit is realized by the specific processing unit 290 of the data processing device 12 and displays product advertisements in association with vendor commercials. The connection unit connects to the Internet using the communication I / F 44 of the robot 414 and updates information in real time.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, when the user is relaxed, the collection unit reduces the frequency of information collection and collects only necessary information. Furthermore, when the user is excited, the collection unit can increase the frequency of information collection and collect information in real time. Furthermore, when the user is stressed, the collection unit can delay the timing of information collection to reduce the user's burden. This allows for more appropriate information to be collected by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of information collection.

[0132] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide visually stimulating analysis results when the user is excited. The analysis unit can also provide concise and concise analysis results when the user is stressed. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the presentation method of the analysis.

[0133] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated user emotions. For example, the selection unit can select detailed information when the user is relaxed. The selection unit can also select visually stimulating information when the user is excited. The selection unit can also select concise, to-the-point information when the user is stressed. By adjusting the selection criteria according to the user's emotions, more appropriate information can be selected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into an AI and have the AI ​​adjust the selection criteria.

[0134] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. For example, the display unit can display detailed information when the user is relaxed. The display unit can also display visually stimulating information when the user is excited. The display unit can also display concise, to-the-point information when the user is stressed. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, an AI, or without an AI. For example, the display unit can input the user's emotion data into an AI and have the AI ​​adjust the display method.

[0135] The connection unit can estimate the user's emotion and adjust the connection method based on the estimated user's emotion. For example, the connection unit can provide detailed connection procedures when the user is relaxed. The connection unit can also provide concise and quick connection procedures when the user is excited. The connection unit can also provide simple and intuitive connection procedures when the user is stressed. This allows the connection method to be adjusted according to the user's emotion, thereby providing a more appropriate connection procedure. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the connection unit can be performed using, for example, an AI, or without an AI. For example, the connection unit can input the user's emotion data into an AI and have the AI ​​adjust the connection method.

[0136] The collection unit can analyze the user's past behavioral history and select the optimal collection method when collecting data. For example, the collection unit selects the optimal collection method based on information about places the user has visited in the past. The collection unit can also analyze the user's past behavioral patterns and suggest the most efficient collection method. The collection unit can also prioritize the use of information collection means that the user has used in the past. In this way, the optimal collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history into AI and have the AI ​​select the optimal collection method.

[0137] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. The analysis unit can also adjust the depth of the analysis depending on the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input information importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0138] The selection unit can improve the accuracy of the selection by taking into account the interrelationships of information when making a selection. For example, the selection unit preferentially selects highly related information. The selection unit can also analyze the interrelationships of information and select optimal information. The selection unit can also improve the accuracy of the selection based on the interrelationships of information. In this way, the accuracy of the selection can be improved by taking the interrelationships of information into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input data on the interrelationships of information into AI and cause the AI ​​to improve the accuracy of the selection.

[0139] The display unit can adjust the level of detail of the display based on the importance of the information when displaying the information. For example, the display unit displays information of high importance in detail. The display unit can also display information of low importance in a concise manner. The display unit can also adjust the level of detail of the display according to the importance of the information. This enables efficient information provision by adjusting the level of detail of the display based on the importance of the information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input information importance data into AI and have the AI ​​adjust the level of detail of the display.

[0140] The connection unit can optimize the connection algorithm by referring to past connection history when connecting. For example, the connection unit selects an optimal connection algorithm based on the user's past connection history. The connection unit can also apply an algorithm that improves the connection success rate from the user's past connection history. The connection unit can also analyze the user's past connection history and optimize the connection algorithm. In this way, by referring to the past connection history, the connection algorithm can be optimized and the connection success rate can be improved. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, AI, or may be performed without using AI. For example, the connection unit can input past connection history data into AI and cause the AI ​​to optimize the connection algorithm.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The collection unit collects information about people in a specific location. For example, the collection unit can use smartphone location information and Wi-Fi connection information to identify the attributes and interests of people in that location. The collection unit can also use cameras and sensors to detect people's movements in real time. Step 2: The analysis unit analyzes the collected information and selects the most appropriate information and advertisements. For example, the analysis unit can use AI to analyze the collected data and select the most appropriate information and advertisements based on the user's interests and attributes. Step 3: The selection unit selects appropriate information or advertisements based on the information analyzed by the analysis unit. For example, the selection unit can use AI to select optimal information or advertisements based on the user's interests and attributes. Step 4: The display unit displays the selected information or advertisement on a digital signage device. For example, the display unit can provide information visually using an electronic sign or an interactive display. Step 5: When displaying an advertisement for a specific product, the linking unit simultaneously displays a commercial from the product's distributor. For example, the linking unit can use AI to associate the product advertisement with the distributor's commercial and display it. Step 6: The connection unit automatically connects the digital signage device to the Internet and updates information in real time. For example, the connection unit can connect the digital signage device to the Internet using a Wi-Fi connection or Bluetooth connection, allowing it to always provide the latest information.

[0143] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0165] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0186] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0187] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0189] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0193] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0198] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0199] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0200] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0204] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0205] 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.

[0206] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0207] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0208] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0209] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0211] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0212] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0213] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0214] [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a selection unit that selects appropriate information or advertisements based on the information analyzed by the analysis unit; a display unit that displays the information or advertisement selected by the selection unit; a linking unit that links a client's commercial to the information or advertisement displayed by the display unit; a connection unit having an automatic connection system for communication devices; A system characterized by:

2. The collecting unit Use smartphone location or Wi-Fi connection information to identify the demographics or interests of people in that location 2. The system of claim 1.

3. The analysis unit Analyzing collected information and selecting appropriate information or advertisements 2. The system of claim 1.

4. The display unit Display selected information and advertisements on digital signage devices 2. The system of claim 1.

5. The binding portion is When displaying an advertisement for a specific product, simultaneously display a commercial from the seller of that product.

2. The system of claim 1.

6. The connection portion is Digital signage devices automatically connect to the internet and update information in real time 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting data, analyze the user's past behavioral history and select the optimal collection method.

2. The system of claim 1.

Images