system

Abstract

The system according to the embodiment aims to efficiently connect data providers and data users and provide rewards. [Solution] A system according to an embodiment includes a data providing unit, a data searching unit, an action executing unit, and a reward providing unit. The data providing unit provides data. The data searching unit searches for data provided by the data providing unit. The action executing unit executes an action based on the data searched by the data searching unit. The reward providing unit provides a reward based on the action executed by the action executing unit.

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 do not have sufficient mechanisms for efficiently connecting data providers and data users and providing rewards, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently connect data providers and data users and provide rewards. [Means for solving the problem]

[0006] The system according to the embodiment includes a data providing unit, a data searching unit, an action executing unit, and a reward providing unit. The data providing unit provides data. The data searching unit searches for data provided by the data providing unit. The action executing unit executes an action based on the data searched by the data searching unit. The reward providing unit provides a reward based on the action executed by the action executing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently connect data providers and data users and provide rewards. [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) The Generative AI Connect system according to an embodiment of the present invention is a platform that connects data providers and data users, ranging from individuals to large corporations. This system allows data providers to receive rewards for providing real-time data and actionable information (e.g., reservations and edits). Data users can search the provided data, obtain necessary information, and directly take action. This creates a new style of information sharing. For example, a data provider provides data to Generative AI Connect. For example, the data provider can provide real-time updated data, such as restaurant reservation status and inventory information. The provided data is analyzed by Generative AI and provided to users. Next, data users search the provided data through Generative AI Connect. For example, they can check the reservation status of a specific restaurant or check inventory information. Users can not only obtain necessary information but also directly take action. For example, they can make restaurant reservations or edit inventory. This system allows data providers to receive rewards and data users to efficiently obtain and utilize necessary information. This creates a new style of information sharing and realizes a platform that can be used by a wide range of users, from individuals to large corporations. This allows the Generative AI Connect System to efficiently connect data providers and data users.

[0029] The generative AI connect system according to the embodiment includes a data providing unit, a data search unit, an action execution unit, and a reward providing unit. The data providing unit provides data. The data providing unit provides, for example, real-time data and actionable information. The data providing unit can provide, for example, data that is updated in real time, such as restaurant reservation status and inventory information. The data providing unit provides data to the generative AI connect. The data search unit searches for data provided by the data providing unit. The data search unit can, for example, check the reservation status of a specific restaurant or check inventory information. The data search unit can efficiently search the provided data. The action execution unit executes an action based on the data searched by the data search unit. The action execution unit can, for example, make a restaurant reservation or edit inventory. The action execution unit executes an action based on the searched data, thereby allowing a user to directly perform an action. The reward providing unit provides a reward based on an action executed by the action execution unit. The reward providing unit, for example, allows a data provider to receive a reward. The reward providing unit provides a reward based on an executed action, thereby allowing the data provider to receive a reward. As a result, the generation AI connect system according to the embodiment can efficiently connect data providers and data users.

[0030] The generative AI connect system includes a data analysis unit that analyzes provided data. The data analysis unit analyzes the provided data. The data analysis unit analyzes the data by applying, for example, statistical analysis or a machine learning algorithm. The data analysis unit can analyze patterns and trends in the data to increase the usefulness of the provided data. For example, the data analysis unit can build a predictive model based on the provided data and predict future trends. The data analysis unit can also cluster the provided data and identify data groups. This allows the data analysis unit to increase the usefulness of the provided data. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input the provided data into an AI model and have the AI ​​analyze the data. This allows the usefulness of the data to be increased by analyzing the provided data.

[0031] The generation AI connect system includes a security protection unit that protects the security of data. The security protection unit protects the security of data. The security protection unit protects the data using, for example, encryption technology. The security protection unit can perform access control of data to ensure that only authorized users can access the data. For example, the security protection unit can periodically back up data to prevent data loss. The security protection unit can also introduce a data monitoring system to detect unauthorized access. In this way, the security protection unit can ensure the security of data. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the security protection unit can have an AI model encrypt the data. In this way, the security of data can be ensured.

[0032] The generative AI connect system includes a privacy protection unit that protects the privacy of data. The privacy protection unit protects the privacy of data. The privacy protection unit protects the privacy of data, for example, by using data anonymization technology. The privacy protection unit can mask data to protect personal information. For example, the privacy protection unit can restrict access to data so that only specific users can access the data. The privacy protection unit can also record an audit log of data and monitor data usage. In this way, the privacy protection unit can protect the privacy of data. Some or all of the above-mentioned processing in the privacy protection unit may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection unit can cause an AI model to anonymize data. In this way, the privacy of data can be protected.

[0033] The data providing unit can provide real-time data or actionable information. The data providing unit provides, for example, real-time data. Real-time data includes, for example, restaurant reservation status and inventory information, but is not limited to these examples. The data providing unit can provide data that is updated in real time. For example, the data providing unit updates restaurant reservation status in real time and provides it to the user. The data providing unit can also update inventory information in real time and provide it to the user. This allows the data providing unit to enable the user to utilize the latest information. Some or all of the above-described processing in the data providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data providing unit can input real-time data into an AI model and have the AI ​​update the data. This allows the user to utilize the latest information by providing real-time data and actionable information.

[0034] The data search unit can search the provided data. For example, the data search unit performs a keyword search on the provided data. The data search unit can filter the provided data to enable the user to efficiently search for the information they need. For example, the data search unit performs a keyword search for the reservation status of a specific restaurant and provides the result to the user. The data search unit can also filter inventory information to enable the user to quickly obtain the information they need. This allows the data search unit to efficiently search the provided data. Some or all of the above-mentioned processing in the data search unit may be performed using AI, for example, or may be performed without using AI. For example, the data search unit can input the provided data into an AI model and have the AI ​​perform a data search. This allows the provided data to be efficiently searched.

[0035] The action execution unit can execute an action based on the searched data. For example, the action execution unit can make a restaurant reservation based on the searched data. The action execution unit can edit inventory based on the searched data. For example, the action execution unit can check the reservation status of a specific restaurant and execute the reservation. The action execution unit can also edit inventory information and update information required by the user. As a result, the action execution unit executes an action based on the searched data, allowing the user to directly execute the action. Some or all of the above-described processing in the action execution unit may be performed using AI, for example, or may be performed without using AI. For example, the action execution unit can input the searched data into an AI model and have the AI ​​execute the action. As a result, the action execution unit executes an action based on the searched data, allowing the user to directly execute the action.

[0036] The reward providing unit can provide a reward based on the executed action. The reward providing unit can provide, for example, a monetary reward based on the executed action. The reward providing unit can provide points or a special benefit based on the executed action. For example, the reward providing unit can provide a monetary reward when the data provider makes a restaurant reservation. The reward providing unit can also provide points or a special benefit when the data provider edits inventory. As a result, the reward providing unit can provide a reward based on the executed action, allowing the data provider to earn a reward. Some or all of the above-described processing in the reward providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the reward providing unit can input the executed action into an AI model and cause the AI ​​to provide a reward. As a result, the reward can be provided based on the executed action, allowing the data provider to earn a reward.

[0037] When providing data, the data providing unit can analyze the provider's past data provision history and select the optimal provision method. When providing data, the data providing unit can analyze the provider's past data provision history and select the optimal provision method. For example, the data providing unit can analyze the type and frequency of data provided by the provider in the past and prioritize providing similar data. The data providing unit can also reuse a data provision method that the provider has previously highly rated. Furthermore, the data providing unit can provide data at the optimal timing based on the provider's past data provision history. As a result, the data providing unit can select the optimal provision method by analyzing the provider's past data provision history. Some or all of the above-mentioned processing in the data providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data providing unit can input the provider's past data provision history into the generation AI and cause the generation AI to select the optimal provision method. As a result, the optimal provision method can be selected by analyzing the provider's past data provision history.

[0038] The data providing unit can filter the data based on the provider's current situation and areas of interest when providing the data. The data providing unit can filter the data based on the provider's current situation and areas of interest when providing the data. For example, the data providing unit can prioritize providing data related to the provider's current areas of interest. The data providing unit can also adjust the level of detail of the data depending on the provider's current situation (e.g., whether the provider is at work or on vacation). Furthermore, the data providing unit can select a data provision method based on the provider's current activity (e.g., whether the provider is moving or stationary). In this way, the data providing unit can provide more relevant data by filtering the data based on the provider's current situation and areas of interest. Some or all of the above-mentioned processing in the data providing unit can be performed using AI, for example, or without AI. For example, the data providing unit can input data on the provider's current situation and areas of interest to the generation AI and cause the generation AI to perform filtering. In this way, more relevant data can be provided by filtering the data based on the provider's current situation and areas of interest.

[0039] When providing data, the data providing unit can prioritize providing highly relevant data by taking into account the geographical location information of the provider. When providing data, the data providing unit can prioritize providing highly relevant data by taking into account the geographical location information of the provider. For example, the data providing unit prioritizes providing data related to the provider's current location. The data providing unit can also provide region-specific data based on the geographical location information of the provider. Furthermore, if the provider is moving, the data providing unit can also provide data related to the provider's destination. In this way, the data providing unit can provide highly relevant data by taking into account the geographical location information of the provider. Some or all of the above-described processing in the data providing unit may be performed using AI, for example, or may be performed without using AI. For example, the data providing unit can input the geographical location information of the provider to the generation AI and cause the generation AI to provide highly relevant data. In this way, highly relevant data can be provided by taking into account the geographical location information of the provider.

[0040] The data providing unit can analyze the social media activity of the provider at the time of providing the data and provide related data. The data providing unit can analyze the social media activity of the provider at the time of providing the data and provide related data. For example, the data providing unit can provide data related to topics in which the provider is interested on social media. The data providing unit can also analyze the provider's social media activity history and provide data that may be of interest to the provider. Furthermore, the data providing unit can also provide data related to accounts that the provider follows on social media. In this way, the data providing unit can provide related data by analyzing the provider's social media activity. Some or all of the above-mentioned processing in the data providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data providing unit can input the provider's social media activity data to the generation AI and cause the generation AI to provide related data. In this way, the data providing unit can provide related data by analyzing the provider's social media activity.

[0041] When searching for data, the data search unit can analyze the searcher's past search history and select the optimal search method. When searching for data, the data search unit can analyze the searcher's past search history and select the optimal search method. The data search unit, for example, prioritizes displaying keywords that the searcher has frequently searched for in the past. The data search unit can also prioritize displaying highly relevant search results based on the searcher's past search history. Furthermore, the data search unit can also reuse search methods (such as filtering and sorting) that the searcher has used in the past. This allows the data search unit to select the optimal search method by analyzing the searcher's past search history. Some or all of the above-mentioned processing in the data search unit may be performed using, for example, AI, or may be performed without using AI. For example, the data search unit can input the searcher's past search history into the generation AI and cause the generation AI to select the optimal search method. This allows the optimal search method to be selected by analyzing the searcher's past search history.

[0042] The data search unit can perform filtering based on the searcher's current situation and areas of interest when searching for data. The data search unit can perform filtering based on the searcher's current situation and areas of interest when searching for data. For example, the data search unit can prioritize displaying search results related to the searcher's current areas of interest. The data search unit can also adjust the level of detail of the search results depending on the searcher's current situation (e.g., whether at work or on vacation). The data search unit can also filter the search results based on the searcher's current activity (e.g., whether on the move or stationary). This allows the data search unit to provide more relevant search results by filtering the search results based on the searcher's current situation and areas of interest. Some or all of the above-described processing in the data search unit can be performed using, for example, AI, or without AI. For example, the data search unit can input data on the searcher's current situation and areas of interest to the generation AI and have the generation AI perform filtering. This allows the search results to be filtered based on the searcher's current situation and areas of interest, thereby providing more relevant search results.

[0043] During a data search, the data search unit can prioritize displaying highly relevant data by taking into account the searcher's geographical location information. During a data search, the data search unit can prioritize displaying highly relevant data by taking into account the searcher's geographical location information. The data search unit, for example, prioritizes displaying search results related to the searcher's current location. The data search unit can also display region-specific search results based on the searcher's geographical location information. Furthermore, if the searcher is traveling, the data search unit can also display search results related to the searcher's destination. In this way, the data search unit can provide highly relevant search results by taking into account the searcher's geographical location information. Some or all of the above-described processing in the data search unit may be performed using, for example, AI, or may be performed without using AI. For example, the data search unit can input the searcher's geographical location information to the generation AI and cause the generation AI to display highly relevant search results. In this way, highly relevant search results can be provided by taking into account the searcher's geographical location information.

[0044] The data search unit can analyze the searcher's social media activity and display relevant data when searching for data. The data search unit can analyze the searcher's social media activity and display relevant data when searching for data. For example, the data search unit can display search results related to topics that the searcher is interested in on social media. The data search unit can also analyze the searcher's social media activity history and display search results that are likely to be of interest. Furthermore, the data search unit can display search results related to accounts that the searcher follows on social media. In this way, the data search unit can provide relevant search results by analyzing the searcher's social media activity. Some or all of the above-mentioned processing in the data search unit can be performed using, for example, AI, or can be performed without using AI. For example, the data search unit can input the searcher's social media activity data into the generation AI and cause the generation AI to display relevant search results. In this way, relevant search results can be provided by analyzing the searcher's social media activity.

[0045] When executing an action, the action execution unit can analyze the executer's past action history and select the optimal execution method. When executing an action, the action execution unit analyzes the executer's past action history and selects the optimal execution method. For example, the action execution unit can prioritize providing actions that the executer has frequently performed in the past. The action execution unit can also prioritize providing highly relevant actions from the executer's past action history. Furthermore, the action execution unit can also reuse action execution methods (such as filtering and sorting) that the executer has used in the past. This allows the action execution unit to select the optimal execution method by analyzing the executer's past action history. Some or all of the above-mentioned processing in the action execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the action execution unit can input the executer's past action history into a generation AI and have the generation AI select the optimal execution method. This allows the executer's past action history to be analyzed and the optimal execution method to be selected.

[0046] The action execution unit can perform filtering based on the current situation and areas of interest of the performer when executing an action. The action execution unit can perform filtering based on the current situation and areas of interest of the performer when executing an action. For example, the action execution unit can prioritize providing actions related to the performer's current areas of interest. The action execution unit can also adjust the level of detail of the action depending on the performer's current situation (e.g., whether the performer is at work or on vacation). Furthermore, the action execution unit can also filter actions based on the performer's current activity (e.g., whether the performer is moving or stationary). This allows the action execution unit to provide more relevant actions by filtering actions based on the performer's current situation and areas of interest. Some or all of the above-described processing in the action execution unit can be performed using, for example, AI, or without AI. For example, the action execution unit can input data on the performer's current situation and areas of interest to a generation AI and have the generation AI perform filtering. This allows more relevant actions to be provided by filtering actions based on the performer's current situation and areas of interest.

[0047] When executing an action, the action execution unit can prioritize execution of highly relevant actions by taking into account the geographical location information of the executor. When executing an action, the action execution unit prioritize execution of highly relevant actions by taking into account the geographical location information of the executor. For example, the action execution unit prioritizes providing actions related to the location where the executor currently resides. The action execution unit can also provide region-specific actions based on the geographical location information of the executor. Furthermore, when the executor is traveling, the action execution unit can also provide actions related to the destination. In this way, the action execution unit can provide highly relevant actions by taking into account the geographical location information of the executor. Some or all of the above-described processing in the action execution unit may be performed using AI, for example, or may be performed without using AI. For example, the action execution unit can input the geographical location information of the executor to a generation AI and cause the generation AI to execute highly relevant actions. In this way, highly relevant actions can be provided by taking into account the geographical location information of the executor.

[0048] The action execution unit can analyze the social media activity of the performer and execute a related action when the action is executed. The action execution unit can analyze the social media activity of the performer and execute a related action when the action is executed. For example, the action execution unit can provide actions related to topics in which the performer is interested on social media. The action execution unit can also analyze the performer's social media activity history and provide actions that are likely to interest the performer. Furthermore, the action execution unit can also provide actions related to accounts the performer follows on social media. In this way, the action execution unit can provide related actions by analyzing the performer's social media activity. Some or all of the above-described processing in the action execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the action execution unit can input the performer's social media activity data into a generation AI and cause the generation AI to execute a related action. In this way, related actions can be provided by analyzing the performer's social media activity.

[0049] The reward providing unit can analyze the provider's past reward history and select the optimal reward method when providing a reward. The reward providing unit analyzes the provider's past reward history and selects the optimal reward method when providing a reward. For example, the reward providing unit analyzes the type and frequency of rewards received by the provider in the past and prioritizes providing similar rewards. The reward providing unit can also reuse reward providing methods that the provider has previously highly rated. Furthermore, the reward providing unit can provide rewards at the optimal timing based on the provider's past reward history. As a result, the reward providing unit can select the optimal reward method by analyzing the provider's past reward history. Some or all of the above-mentioned processing in the reward providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward providing unit can input the provider's past reward history into the generation AI and have the generation AI select the optimal reward method. As a result, the optimal reward method can be selected by analyzing the provider's past reward history.

[0050] The reward providing unit can filter rewards based on the provider's current situation and areas of interest when providing a reward. The reward providing unit can filter rewards based on the provider's current situation and areas of interest when providing a reward. For example, the reward providing unit can prioritize rewards related to the provider's current areas of interest. The reward providing unit can also adjust the level of detail of the reward depending on the provider's current situation (e.g., whether at work or on vacation). The reward providing unit can also select a reward provision method based on the provider's current activity (e.g., whether moving or stationary). In this way, the reward providing unit can provide more relevant rewards by filtering rewards based on the provider's current situation and areas of interest. Some or all of the above-described processing in the reward providing unit can be performed using AI, for example, or without AI. For example, the reward providing unit can input data on the provider's current situation and areas of interest to the generation AI and cause the generation AI to perform filtering. In this way, more relevant rewards can be provided by filtering rewards based on the provider's current situation and areas of interest.

[0051] The reward providing unit can prioritize providing highly relevant rewards by taking into consideration the geographical location information of the provider when providing a reward. The reward providing unit can prioritize providing highly relevant rewards by taking into consideration the geographical location information of the provider when providing a reward. For example, the reward providing unit prioritizes providing a reward related to the provider's current location. The reward providing unit can also provide a region-specific reward based on the geographical location information of the provider. Furthermore, if the provider is traveling, the reward providing unit can also provide a reward related to the provider's destination. In this way, the reward providing unit can provide highly relevant rewards by taking into consideration the geographical location information of the provider. Some or all of the above-described processing in the reward providing unit may be performed using AI, for example, or may be performed without using AI. For example, the reward providing unit can input the geographical location information of the provider to the generation AI and cause the generation AI to provide highly relevant rewards. In this way, highly relevant rewards can be provided by taking into consideration the geographical location information of the provider.

[0052] The reward providing unit can analyze the provider's social media activity and provide a related reward when providing a reward. The reward providing unit can analyze the provider's social media activity and provide a related reward when providing a reward. For example, the reward providing unit can provide a reward related to a topic in which the provider is interested on social media. The reward providing unit can also analyze the provider's social media activity history and provide a reward that is likely to interest the provider. Furthermore, the reward providing unit can also provide a reward related to accounts the provider follows on social media. In this way, the reward providing unit can provide a related reward by analyzing the provider's social media activity. Some or all of the above-mentioned processing in the reward providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the reward providing unit can input the provider's social media activity data into a generation AI and cause the generation AI to provide a related reward. In this way, the reward providing unit can provide a related reward by analyzing the provider's social media activity.

[0053] When analyzing data, the data analysis unit can analyze past data analysis history and select the optimal analysis method. When analyzing data, the data analysis unit analyzes past data analysis history and selects the optimal analysis method. The data analysis unit selects the optimal analysis method, for example, based on analysis methods used in the past. The data analysis unit can also select the most effective analysis method from the past analysis history. Furthermore, the data analysis unit can adjust the analysis method based on past analysis results. In this way, the data analysis unit can select the optimal analysis method by analyzing the past data analysis history. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input past data analysis history to a generation AI and have the generation AI select the optimal analysis method. In this way, the optimal analysis method can be selected by analyzing the past data analysis history.

[0054] The data analysis unit can apply different analysis algorithms depending on the type of data provided during data analysis. The data analysis unit can apply different analysis algorithms depending on the type of data provided during data analysis. For example, the data analysis unit can apply a natural language processing algorithm to text data. The data analysis unit can also apply an image recognition algorithm to image data. The data analysis unit can also apply a time series analysis algorithm to time series data. This allows the data analysis unit to provide more accurate analysis results by applying the optimal analysis algorithm depending on the type of data provided. Some or all of the above-mentioned processing in the data analysis unit can be performed using AI, for example, or without AI. For example, the data analysis unit can input the provided data into a generation AI and cause the generation AI to apply an appropriate analysis algorithm. This allows more accurate analysis results to be provided by applying the optimal analysis algorithm depending on the type of data provided.

[0055] During data analysis, the data analysis unit can determine the analysis priority based on the submission time of the provided data. During data analysis, the data analysis unit can determine the analysis priority based on the submission time of the provided data. The data analysis unit can determine the analysis priority based on, for example, the recency of the submitted data. The data analysis unit can also determine the analysis priority based on the importance of the submitted data. Furthermore, the data analysis unit can also determine the analysis priority based on the relevance of the submitted data. As a result, the data analysis unit can analyze important data more quickly by determining the analysis priority based on the submission time of the provided data. Some or all of the above-described processing in the data analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the data analysis unit can input the submission time of the submitted data to the generation AI and have the generation AI determine the analysis priority. As a result, by determining the analysis priority based on the submission time of the provided data, important data can be analyzed more quickly.

[0056] The data analysis unit can adjust the order of analysis based on the relevance of the provided data during data analysis. The data analysis unit can adjust the order of analysis based on the relevance of the provided data during data analysis. For example, the data analysis unit prioritizes analysis of the most relevant data based on the relevance of the provided data. The data analysis unit can also adjust the order of analysis based on the importance of the provided data. Furthermore, the data analysis unit can also adjust the order of analysis based on the recency of the provided data. As a result, the data analysis unit can prioritize analysis of more relevant data by adjusting the order of analysis based on the relevance of the provided data. Some or all of the above-described processing in the data analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the data analysis unit can input the relevance of the provided data to the generation AI and cause the generation AI to adjust the order of analysis. As a result, as a result of adjusting the order of analysis based on the relevance of the provided data, more relevant data can be prioritized for analysis.

[0057] The security protection unit can analyze past security incident history and select the optimal protection method during security protection. The security protection unit analyzes past security incident history and selects the optimal protection method during security protection. The security protection unit, for example, selects the optimal protection method based on past security incident history. The security protection unit can also learn from past incidents and select a protection method to prevent similar incidents. Furthermore, the security protection unit can analyze past incident history and select the most effective protection method. In this way, the security protection unit can select the optimal protection method by analyzing past security incident history. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the security protection unit can input past security incident history into a generation AI and have the generation AI select the optimal protection method. In this way, the optimal protection method can be selected by analyzing past security incident history.

[0058] The security protection unit can apply different protection algorithms depending on the type of data provided during security protection. The security protection unit can apply different protection algorithms depending on the type of data provided during security protection. For example, the security protection unit applies an encryption algorithm to text data. The security protection unit can also apply a watermarking algorithm to image data. The security protection unit can also apply a data masking algorithm to time-series data. This allows the security protection unit to provide more appropriate security protection by applying the optimal protection algorithm depending on the type of data provided. Some or all of the above-mentioned processing in the security protection unit may be performed using AI, for example, or may be performed without using AI. For example, the security protection unit can input the provided data to a generation AI and cause the generation AI to apply an appropriate protection algorithm. This allows more appropriate security protection to be provided by applying the optimal protection algorithm depending on the type of data provided.

[0059] The security protection unit can determine the priority of protection based on the time of submission of the provided data during security protection. The security protection unit can determine the priority of protection based on the time of submission of the provided data during security protection. The security protection unit can determine the priority of protection based on, for example, the newness of the submitted data. The security protection unit can also determine the priority of protection based on the importance of the submitted data. Furthermore, the security protection unit can also determine the priority of protection based on the relevance of the submitted data. As a result, the security protection unit can protect important data more quickly by determining the priority of protection based on the time of submission of the provided data. Some or all of the above-described processing in the security protection unit can be performed using AI, for example, or without AI. For example, the security protection unit can input the time of submission of the submitted data to the generation AI and cause the generation AI to determine the priority of protection. As a result, the security protection unit can protect important data more quickly by determining the priority of protection based on the time of submission of the provided data.

[0060] The security protection unit can adjust the order of protection based on the relevance of the provided data during security protection. The security protection unit can adjust the order of protection based on the relevance of the provided data during security protection. For example, the security protection unit prioritizes protection of the most relevant data based on the relevance of the provided data. The security protection unit can also adjust the order of protection based on the importance of the provided data. Furthermore, the security protection unit can also adjust the order of protection based on the recency of the provided data. As a result, the security protection unit can prioritize protection of more relevant data by adjusting the order of protection based on the relevance of the provided data. Some or all of the above-described processing in the security protection unit can be performed using AI, for example, or without using AI. For example, the security protection unit can input the relevance of the provided data to the generation AI and cause the generation AI to adjust the order of protection. As a result, as a result of adjusting the order of protection based on the relevance of the provided data, more relevant data can be prioritized for protection.

[0061] The privacy protection unit can analyze past privacy incident history and select the optimal protection method during privacy protection. The privacy protection unit can analyze past privacy incident history and select the optimal protection method during privacy protection. For example, the privacy protection unit selects the optimal protection method based on past privacy incident history. The privacy protection unit can also learn from past incidents and select a protection method to prevent similar incidents. Furthermore, the privacy protection unit can analyze past incident history and select the most effective protection method. As a result, the privacy protection unit can select the optimal protection method by analyzing past privacy incident history. Some or all of the above-mentioned processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input past privacy incident history into a generation AI and have the generation AI select the optimal protection method. As a result, the optimal protection method can be selected by analyzing past privacy incident history.

[0062] The privacy protection unit can apply different protection algorithms depending on the type of data provided during privacy protection. The privacy protection unit can apply different protection algorithms depending on the type of data provided during privacy protection. For example, the privacy protection unit can apply an anonymization algorithm to text data. The privacy protection unit can also apply a facial recognition algorithm to image data. The privacy protection unit can also apply a data masking algorithm to time-series data. This allows the privacy protection unit to provide more appropriate privacy protection by applying the optimal protection algorithm depending on the type of data provided. Some or all of the above-mentioned processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input the provided data to a generation AI and cause the generation AI to apply an appropriate protection algorithm. This allows more appropriate privacy protection to be provided by applying the optimal protection algorithm depending on the type of data provided.

[0063] The privacy protection unit can determine the priority of protection based on the time of submission of the provided data during privacy protection. The privacy protection unit can determine the priority of protection based on the time of submission of the provided data during privacy protection. The privacy protection unit can determine the priority of protection based on, for example, the recency of the submitted data. The privacy protection unit can also determine the priority of protection based on the importance of the submitted data. Furthermore, the privacy protection unit can also determine the priority of protection based on the relevance of the submitted data. As a result, the privacy protection unit can protect important data more quickly by determining the priority of protection based on the time of submission of the provided data. Some or all of the above-described processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input the time of submission of the submitted data into the generation AI and have the generation AI determine the priority of protection. As a result, important data can be protected more quickly by determining the priority of protection based on the time of submission of the provided data.

[0064] The privacy protection unit can adjust the order of protection based on the relevance of the provided data during privacy protection. The privacy protection unit can adjust the order of protection based on the relevance of the provided data during privacy protection. For example, the privacy protection unit prioritizes protecting the most relevant data based on the relevance of the provided data. The privacy protection unit can also adjust the order of protection based on the importance of the provided data. Furthermore, the privacy protection unit can also adjust the order of protection based on the recency of the provided data. As a result, the privacy protection unit can prioritize protecting more relevant data by adjusting the order of protection based on the relevance of the provided data. Some or all of the above-described processing in the privacy protection unit can be performed using AI, for example, or without using AI. For example, the privacy protection unit can input the relevance of the provided data to the generation AI and cause the generation AI to adjust the order of protection. As a result, the privacy protection unit can prioritize protecting more relevant data by adjusting the order of protection based on the relevance of the provided data.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The generation AI connect system can further include a reliability evaluation unit that evaluates the reliability of data providers. The reliability evaluation unit can score reliability based on the data provider's past data provision history and evaluations, and preferentially display reliable data providers to data users. For example, the reliability evaluation unit can evaluate the accuracy and timeliness of data previously provided by the data provider and calculate a reliability score. The reliability evaluation unit can also re-evaluate the reliability based on feedback and evaluations received by the data provider. Furthermore, the reliability evaluation unit can display the reliability score of the data provider to the data user, providing it as reference information when selecting a reliable data provider. This allows data users to efficiently obtain reliable data.

[0067] The generative AI connect system can further include a personalized search unit that analyzes the data user's search history and provides personalized search results. The personalized search unit can analyze the data user's past search history and behavioral patterns and prioritize displaying highly relevant search results. For example, the personalized search unit can prioritize displaying related data based on keywords and categories that the data user has searched for in the past. The personalized search unit can also customize search results based on the data user's interests. Furthermore, the personalized search unit can learn the data user's behavioral patterns and provide optimal search results for future searches. This allows the data user to efficiently obtain the information they need.

[0068] The generation AI connect system can further include a region-specific data providing unit that takes into consideration the geographical location information of the data provider and preferentially provides region-specific data. The region-specific data providing unit can preferentially provide region-related data based on the data provider's current location and past location information. For example, the region-specific data providing unit can provide event information and store information in the region where the data provider is currently located. The region-specific data providing unit can also provide data related to regions that the data provider has visited in the past. Furthermore, the region-specific data providing unit can analyze the data provider's movement patterns and provide data related to future destinations. This allows the data provider to efficiently obtain useful information related to the region.

[0069] The generative AI connect system may further include a social media linking unit that analyzes the social media activities of the data provider and provides relevant data. The social media linking unit may provide relevant data based on topics that the data provider is interested in and accounts that the data provider follows on social media. For example, the social media linking unit may preferentially provide data related to topics that the data provider is interested in. The social media linking unit may also provide data related to accounts that the data provider follows. Furthermore, the social media linking unit may analyze the data provider's social media activity history and provide data that may be of interest to the data provider. This allows the data provider to efficiently obtain highly relevant data based on their social media activities.

[0070] The generation AI connect system can further include a history analysis unit that analyzes the data provider's past data provision history and selects the optimal data provision method. The history analysis unit can analyze the type and frequency of data provided by the data provider in the past and provide similar data preferentially. For example, the history analysis unit may reuse a data provision method that the data provider has previously highly rated. The history analysis unit can also provide data at the optimal timing based on the data provider's past data provision history. Furthermore, the history analysis unit can learn data provision patterns based on the data provider's provision history and select the optimal method for future data provision. This allows data providers to provide data efficiently.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The data provider provides data. For example, it provides real-time data and actionable information. Specifically, it can provide data that is updated in real time, such as restaurant reservation status and inventory information. The data provider provides the data to the generation AI connect. Step 2: The data search unit searches the data provided by the data provider. For example, you can check the reservation status of a specific restaurant or check inventory information. The data search unit can efficiently search the provided data. Step 3: The action execution unit executes an action based on the data retrieved by the data retrieval unit. For example, the action execution unit can make a restaurant reservation or edit inventory. By executing an action based on the retrieved data, the action execution unit allows the user to perform the action directly. Step 4: The reward providing unit provides a reward based on the action performed by the action performing unit. For example, the data provider can receive a reward. The reward providing unit provides a reward based on the action performed, allowing the data provider to receive a reward.

[0073] (Example 2) The Generative AI Connect system according to an embodiment of the present invention is a platform that connects data providers and data users, ranging from individuals to large corporations. This system allows data providers to receive rewards for providing real-time data and actionable information (e.g., reservations and edits). Data users can search the provided data, obtain necessary information, and directly take action. This creates a new style of information sharing. For example, a data provider provides data to Generative AI Connect. For example, the data provider can provide real-time updated data, such as restaurant reservation status and inventory information. The provided data is analyzed by Generative AI and provided to users. Next, data users search the provided data through Generative AI Connect. For example, they can check the reservation status of a specific restaurant or check inventory information. Users can not only obtain necessary information but also directly take action. For example, they can make restaurant reservations or edit inventory. This system allows data providers to receive rewards and data users to efficiently obtain and utilize necessary information. This creates a new style of information sharing and realizes a platform that can be used by a wide range of users, from individuals to large corporations. This allows the Generative AI Connect System to efficiently connect data providers and data users.

[0074] The generative AI connect system according to the embodiment includes a data providing unit, a data search unit, an action execution unit, and a reward providing unit. The data providing unit provides data. The data providing unit provides, for example, real-time data and actionable information. The data providing unit can provide, for example, data that is updated in real time, such as restaurant reservation status and inventory information. The data providing unit provides data to the generative AI connect. The data search unit searches for data provided by the data providing unit. The data search unit can, for example, check the reservation status of a specific restaurant or check inventory information. The data search unit can efficiently search the provided data. The action execution unit executes an action based on the data searched by the data search unit. The action execution unit can, for example, make a restaurant reservation or edit inventory. The action execution unit executes an action based on the searched data, thereby allowing a user to directly perform an action. The reward providing unit provides a reward based on an action executed by the action execution unit. The reward providing unit, for example, allows a data provider to receive a reward. The reward providing unit provides a reward based on an executed action, thereby allowing the data provider to receive a reward. As a result, the generation AI connect system according to the embodiment can efficiently connect data providers and data users.

[0075] The generative AI connect system includes a data analysis unit that analyzes provided data. The data analysis unit analyzes the provided data. The data analysis unit analyzes the data by applying, for example, statistical analysis or a machine learning algorithm. The data analysis unit can analyze patterns and trends in the data to increase the usefulness of the provided data. For example, the data analysis unit can build a predictive model based on the provided data and predict future trends. The data analysis unit can also cluster the provided data and identify data groups. This allows the data analysis unit to increase the usefulness of the provided data. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input the provided data into an AI model and have the AI ​​analyze the data. This allows the usefulness of the data to be increased by analyzing the provided data.

[0076] The generation AI connect system includes a security protection unit that protects the security of data. The security protection unit protects the security of data. The security protection unit protects the data using, for example, encryption technology. The security protection unit can perform access control of data to ensure that only authorized users can access the data. For example, the security protection unit can periodically back up data to prevent data loss. The security protection unit can also introduce a data monitoring system to detect unauthorized access. In this way, the security protection unit can ensure the security of data. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the security protection unit can have an AI model encrypt the data. In this way, the security of data can be ensured.

[0077] The generative AI connect system includes a privacy protection unit that protects the privacy of data. The privacy protection unit protects the privacy of data. The privacy protection unit protects the privacy of data, for example, by using data anonymization technology. The privacy protection unit can mask data to protect personal information. For example, the privacy protection unit can restrict access to data so that only specific users can access the data. The privacy protection unit can also record an audit log of data and monitor data usage. In this way, the privacy protection unit can protect the privacy of data. Some or all of the above-mentioned processing in the privacy protection unit may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection unit can cause an AI model to anonymize data. In this way, the privacy of data can be protected.

[0078] The data providing unit can provide real-time data or actionable information. The data providing unit provides, for example, real-time data. Real-time data includes, for example, restaurant reservation status and inventory information, but is not limited to these examples. The data providing unit can provide data that is updated in real time. For example, the data providing unit updates restaurant reservation status in real time and provides it to the user. The data providing unit can also update inventory information in real time and provide it to the user. This allows the data providing unit to enable the user to utilize the latest information. Some or all of the above-described processing in the data providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data providing unit can input real-time data into an AI model and have the AI ​​update the data. This allows the user to utilize the latest information by providing real-time data and actionable information.

[0079] The data search unit can search the provided data. For example, the data search unit performs a keyword search on the provided data. The data search unit can filter the provided data to enable the user to efficiently search for the information they need. For example, the data search unit performs a keyword search for the reservation status of a specific restaurant and provides the result to the user. The data search unit can also filter inventory information to enable the user to quickly obtain the information they need. This allows the data search unit to efficiently search the provided data. Some or all of the above-mentioned processing in the data search unit may be performed using AI, for example, or may be performed without using AI. For example, the data search unit can input the provided data into an AI model and have the AI ​​perform a data search. This allows the provided data to be efficiently searched.

[0080] The action execution unit can execute an action based on the searched data. For example, the action execution unit can make a restaurant reservation based on the searched data. The action execution unit can edit inventory based on the searched data. For example, the action execution unit can check the reservation status of a specific restaurant and execute the reservation. The action execution unit can also edit inventory information and update information required by the user. As a result, the action execution unit executes an action based on the searched data, allowing the user to directly execute the action. Some or all of the above-described processing in the action execution unit may be performed using AI, for example, or may be performed without using AI. For example, the action execution unit can input the searched data into an AI model and have the AI ​​execute the action. As a result, the action execution unit executes an action based on the searched data, allowing the user to directly execute the action.

[0081] The reward providing unit can provide a reward based on the executed action. The reward providing unit can provide, for example, a monetary reward based on the executed action. The reward providing unit can provide points or a special benefit based on the executed action. For example, the reward providing unit can provide a monetary reward when the data provider makes a restaurant reservation. The reward providing unit can also provide points or a special benefit when the data provider edits inventory. As a result, the reward providing unit can provide a reward based on the executed action, allowing the data provider to earn a reward. Some or all of the above-described processing in the reward providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the reward providing unit can input the executed action into an AI model and cause the AI ​​to provide a reward. As a result, the reward can be provided based on the executed action, allowing the data provider to earn a reward.

[0082] The generative AI connect system includes a data providing unit that estimates a user's emotions and adjusts the timing of data provision based on the estimated user emotions. The data providing unit estimates the user's emotions and adjusts the timing of data provision based on the estimated user emotions. For example, when the user is feeling stressed, the data providing unit reduces the frequency of data provision and provides only important information. Furthermore, when the user is relaxed, the data providing unit can provide detailed data and add information that the user is interested in. Furthermore, when the user is in a hurry, the data providing unit can quickly provide necessary data and omit unnecessary information. This allows the data providing unit to adjust the timing of data provision according to the user's emotions, thereby providing data at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 data providing unit may be performed using, for example, AI, or without AI. For example, the data providing unit can input user emotion data into the generation AI and have the generation AI adjust the timing of data provision. This allows the timing of data provision to be adjusted according to the user's emotion, making it possible to provide data at more appropriate timing.

[0083] When providing data, the data providing unit can analyze the provider's past data provision history and select the optimal provision method. When providing data, the data providing unit can analyze the provider's past data provision history and select the optimal provision method. For example, the data providing unit can analyze the type and frequency of data provided by the provider in the past and prioritize providing similar data. The data providing unit can also reuse a data provision method that the provider has previously highly rated. Furthermore, the data providing unit can provide data at the optimal timing based on the provider's past data provision history. As a result, the data providing unit can select the optimal provision method by analyzing the provider's past data provision history. Some or all of the above-mentioned processing in the data providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data providing unit can input the provider's past data provision history into the generation AI and cause the generation AI to select the optimal provision method. As a result, the optimal provision method can be selected by analyzing the provider's past data provision history.

[0084] The data providing unit can filter the data based on the provider's current situation and areas of interest when providing the data. The data providing unit can filter the data based on the provider's current situation and areas of interest when providing the data. For example, the data providing unit can prioritize providing data related to the provider's current areas of interest. The data providing unit can also adjust the level of detail of the data depending on the provider's current situation (e.g., whether the provider is at work or on vacation). Furthermore, the data providing unit can select a data provision method based on the provider's current activity (e.g., whether the provider is moving or stationary). In this way, the data providing unit can provide more relevant data by filtering the data based on the provider's current situation and areas of interest. Some or all of the above-mentioned processing in the data providing unit can be performed using AI, for example, or without AI. For example, the data providing unit can input data on the provider's current situation and areas of interest to the generation AI and cause the generation AI to perform filtering. In this way, more relevant data can be provided by filtering the data based on the provider's current situation and areas of interest.

[0085] The data providing unit can estimate the user's emotions and determine the priority of data to be provided based on the estimated user emotions. The data providing unit can estimate the user's emotions and determine the priority of data to be provided based on the estimated user emotions. For example, when the user is stressed, the data providing unit can prioritize providing data of high importance. Furthermore, when the user is relaxed, the data providing unit can prioritize providing interesting data. Furthermore, when the user is in a hurry, the data providing unit can prioritize providing data that requires a quick response. In this way, the data providing unit can prioritize data to be provided based on the user's emotions, thereby providing more appropriate data. Emotion estimation is realized using an emotion estimation function, for example, using 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 data providing unit may be performed using an AI, for example, or without an AI. For example, the data providing unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the data. This allows the system to determine the priority of data to be provided according to the user's emotions, thereby providing more appropriate data.

[0086] When providing data, the data providing unit can prioritize providing highly relevant data by taking into account the geographical location information of the provider. When providing data, the data providing unit can prioritize providing highly relevant data by taking into account the geographical location information of the provider. For example, the data providing unit prioritizes providing data related to the provider's current location. The data providing unit can also provide region-specific data based on the geographical location information of the provider. Furthermore, if the provider is moving, the data providing unit can also provide data related to the provider's destination. In this way, the data providing unit can provide highly relevant data by taking into account the geographical location information of the provider. Some or all of the above-described processing in the data providing unit may be performed using AI, for example, or may be performed without using AI. For example, the data providing unit can input the geographical location information of the provider to the generation AI and cause the generation AI to provide highly relevant data. In this way, highly relevant data can be provided by taking into account the geographical location information of the provider.

[0087] The data providing unit can analyze the social media activity of the provider at the time of providing the data and provide related data. The data providing unit can analyze the social media activity of the provider at the time of providing the data and provide related data. For example, the data providing unit can provide data related to topics in which the provider is interested on social media. The data providing unit can also analyze the provider's social media activity history and provide data that may be of interest to the provider. Furthermore, the data providing unit can also provide data related to accounts that the provider follows on social media. In this way, the data providing unit can provide related data by analyzing the provider's social media activity. Some or all of the above-mentioned processing in the data providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data providing unit can input the provider's social media activity data to the generation AI and cause the generation AI to provide related data. In this way, the data providing unit can provide related data by analyzing the provider's social media activity.

[0088] The data search unit can estimate the user's emotions and adjust the display method of search results based on the estimated user emotions. The data search unit can estimate the user's emotions and adjust the display method of search results based on the estimated user emotions. For example, if the user is nervous, the data search unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the data search unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the data search unit can provide a display method that focuses on the main points. This allows the data search unit to adjust the display method of search results according to the user's emotions, thereby enabling more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using 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 data search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data search unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the search results. This allows for more appropriate display by adjusting the way search results are displayed according to the user's emotions.

[0089] When searching for data, the data search unit can analyze the searcher's past search history and select the optimal search method. When searching for data, the data search unit can analyze the searcher's past search history and select the optimal search method. The data search unit, for example, prioritizes displaying keywords that the searcher has frequently searched for in the past. The data search unit can also prioritize displaying highly relevant search results based on the searcher's past search history. Furthermore, the data search unit can also reuse search methods (such as filtering and sorting) that the searcher has used in the past. This allows the data search unit to select the optimal search method by analyzing the searcher's past search history. Some or all of the above-mentioned processing in the data search unit may be performed using, for example, AI, or may be performed without using AI. For example, the data search unit can input the searcher's past search history into the generation AI and cause the generation AI to select the optimal search method. This allows the optimal search method to be selected by analyzing the searcher's past search history.

[0090] The data search unit can perform filtering based on the searcher's current situation and areas of interest when searching for data. The data search unit can perform filtering based on the searcher's current situation and areas of interest when searching for data. For example, the data search unit can prioritize displaying search results related to the searcher's current areas of interest. The data search unit can also adjust the level of detail of the search results depending on the searcher's current situation (e.g., whether at work or on vacation). The data search unit can also filter the search results based on the searcher's current activity (e.g., whether on the move or stationary). This allows the data search unit to provide more relevant search results by filtering the search results based on the searcher's current situation and areas of interest. Some or all of the above-described processing in the data search unit can be performed using, for example, AI, or without AI. For example, the data search unit can input data on the searcher's current situation and areas of interest to the generation AI and have the generation AI perform filtering. This allows the search results to be filtered based on the searcher's current situation and areas of interest, thereby providing more relevant search results.

[0091] The data search unit can estimate the user's emotions and prioritize search results based on the estimated user emotions. The data search unit can estimate the user's emotions and prioritize search results based on the estimated user emotions. For example, when the user is stressed, the data search unit can prioritize search results that are highly important. Furthermore, when the user is relaxed, the data search unit can prioritize search results that require a quick response. In this way, the data search unit can prioritize search results according to the user's emotions, thereby providing more appropriate search results. 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 data search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data search unit can input user emotion data into the generation AI and have the generation AI determine the priority of the search results. This allows the system to provide more appropriate search results by prioritizing search results according to the user's feelings.

[0092] During a data search, the data search unit can prioritize displaying highly relevant data by taking into account the searcher's geographical location information. During a data search, the data search unit can prioritize displaying highly relevant data by taking into account the searcher's geographical location information. The data search unit, for example, prioritizes displaying search results related to the searcher's current location. The data search unit can also display region-specific search results based on the searcher's geographical location information. Furthermore, if the searcher is traveling, the data search unit can also display search results related to the searcher's destination. In this way, the data search unit can provide highly relevant search results by taking into account the searcher's geographical location information. Some or all of the above-described processing in the data search unit may be performed using, for example, AI, or may be performed without using AI. For example, the data search unit can input the searcher's geographical location information to the generation AI and cause the generation AI to display highly relevant search results. In this way, highly relevant search results can be provided by taking into account the searcher's geographical location information.

[0093] The data search unit can analyze the searcher's social media activity and display relevant data when searching for data. The data search unit can analyze the searcher's social media activity and display relevant data when searching for data. For example, the data search unit can display search results related to topics that the searcher is interested in on social media. The data search unit can also analyze the searcher's social media activity history and display search results that are likely to be of interest. Furthermore, the data search unit can display search results related to accounts that the searcher follows on social media. In this way, the data search unit can provide relevant search results by analyzing the searcher's social media activity. Some or all of the above-mentioned processing in the data search unit can be performed using, for example, AI, or can be performed without using AI. For example, the data search unit can input the searcher's social media activity data into the generation AI and cause the generation AI to display relevant search results. In this way, relevant search results can be provided by analyzing the searcher's social media activity.

[0094] The action execution unit can estimate the user's emotions and adjust the action execution method based on the estimated user emotions. The action execution unit can estimate the user's emotions and adjust the action execution method based on the estimated user emotions. For example, when the user is nervous, the action execution unit can provide a simple, highly visible action execution method. Furthermore, when the user is relaxed, the action execution unit can provide an action execution method that includes detailed information. Furthermore, when the user is in a hurry, the action execution unit can provide an action execution method that focuses on the main points. This allows the action execution unit to adjust the action execution method according to the user's emotions and execute a more appropriate action. 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 action execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the action execution unit can input the user's emotion data into the generation AI and have the generation AI adjust the action execution method. This allows the method of executing an action to be adjusted according to the user's emotions, thereby enabling more appropriate actions to be executed.

[0095] When executing an action, the action execution unit can analyze the executer's past action history and select the optimal execution method. When executing an action, the action execution unit analyzes the executer's past action history and selects the optimal execution method. For example, the action execution unit can prioritize providing actions that the executer has frequently performed in the past. The action execution unit can also prioritize providing highly relevant actions from the executer's past action history. Furthermore, the action execution unit can also reuse action execution methods (such as filtering and sorting) that the executer has used in the past. This allows the action execution unit to select the optimal execution method by analyzing the executer's past action history. Some or all of the above-mentioned processing in the action execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the action execution unit can input the executer's past action history into a generation AI and have the generation AI select the optimal execution method. This allows the executer's past action history to be analyzed and the optimal execution method to be selected.

[0096] The action execution unit can perform filtering based on the current situation and areas of interest of the performer when executing an action. The action execution unit can perform filtering based on the current situation and areas of interest of the performer when executing an action. For example, the action execution unit can prioritize providing actions related to the performer's current areas of interest. The action execution unit can also adjust the level of detail of the action depending on the performer's current situation (e.g., whether the performer is at work or on vacation). Furthermore, the action execution unit can also filter actions based on the performer's current activity (e.g., whether the performer is moving or stationary). This allows the action execution unit to provide more relevant actions by filtering actions based on the performer's current situation and areas of interest. Some or all of the above-described processing in the action execution unit can be performed using, for example, AI, or without AI. For example, the action execution unit can input data on the performer's current situation and areas of interest to a generation AI and have the generation AI perform filtering. This allows more relevant actions to be provided by filtering actions based on the performer's current situation and areas of interest.

[0097] The action execution unit can estimate the user's emotions and determine the priority of actions based on the estimated user emotions. The action execution unit can estimate the user's emotions and determine the priority of actions based on the estimated user emotions. For example, when the user is stressed, the action execution unit can prioritize providing highly important actions. Furthermore, when the user is relaxed, the action execution unit can prioritize providing interesting actions. Furthermore, when the user is in a hurry, the action execution unit can prioritize providing actions that require a quick response. This allows the action execution unit to prioritize actions based on the user's emotions and provide more appropriate actions. 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 action execution unit may be performed using an AI, for example, or without an AI. For example, the action execution unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of actions. This allows the system to prioritize actions based on the user's emotions, thereby providing more appropriate actions.

[0098] When executing an action, the action execution unit can prioritize execution of highly relevant actions by taking into account the geographical location information of the executor. When executing an action, the action execution unit prioritize execution of highly relevant actions by taking into account the geographical location information of the executor. For example, the action execution unit prioritizes providing actions related to the location where the executor currently resides. The action execution unit can also provide region-specific actions based on the geographical location information of the executor. Furthermore, when the executor is traveling, the action execution unit can also provide actions related to the destination. In this way, the action execution unit can provide highly relevant actions by taking into account the geographical location information of the executor. Some or all of the above-described processing in the action execution unit may be performed using AI, for example, or may be performed without using AI. For example, the action execution unit can input the geographical location information of the executor to a generation AI and cause the generation AI to execute highly relevant actions. In this way, highly relevant actions can be provided by taking into account the geographical location information of the executor.

[0099] The action execution unit can analyze the social media activity of the performer and execute a related action when the action is executed. The action execution unit can analyze the social media activity of the performer and execute a related action when the action is executed. For example, the action execution unit can provide actions related to topics in which the performer is interested on social media. The action execution unit can also analyze the performer's social media activity history and provide actions that are likely to interest the performer. Furthermore, the action execution unit can also provide actions related to accounts the performer follows on social media. In this way, the action execution unit can provide related actions by analyzing the performer's social media activity. Some or all of the above-described processing in the action execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the action execution unit can input the performer's social media activity data into a generation AI and cause the generation AI to execute a related action. In this way, related actions can be provided by analyzing the performer's social media activity.

[0100] The reward providing unit can estimate the user's emotion and adjust the reward provision method based on the estimated user's emotion. The reward providing unit can estimate the user's emotion and adjust the reward provision method based on the estimated user's emotion. For example, if the user is nervous, the reward providing unit can provide a simple and highly visible reward provision method. Furthermore, if the user is relaxed, the reward providing unit can provide a reward provision method that includes detailed information. Furthermore, if the user is in a hurry, the reward providing unit can provide a reward provision method that focuses on the main points. This allows the reward providing unit to adjust the reward provision method according to the user's emotion, thereby providing a more appropriate reward. Emotion estimation is realized using an emotion estimation function, for example, using 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 reward providing unit may be performed using an AI, for example, or without an AI. For example, the reward providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the reward provision method. This allows the reward provision method to be adjusted according to the user's emotions, making it possible to provide more appropriate rewards.

[0101] The reward providing unit can analyze the provider's past reward history and select the optimal reward method when providing a reward. The reward providing unit analyzes the provider's past reward history and selects the optimal reward method when providing a reward. For example, the reward providing unit analyzes the type and frequency of rewards received by the provider in the past and prioritizes providing similar rewards. The reward providing unit can also reuse reward providing methods that the provider has previously highly rated. Furthermore, the reward providing unit can provide rewards at the optimal timing based on the provider's past reward history. As a result, the reward providing unit can select the optimal reward method by analyzing the provider's past reward history. Some or all of the above-mentioned processing in the reward providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the reward providing unit can input the provider's past reward history into the generation AI and have the generation AI select the optimal reward method. As a result, the optimal reward method can be selected by analyzing the provider's past reward history.

[0102] The reward providing unit can filter rewards based on the provider's current situation and areas of interest when providing a reward. The reward providing unit can filter rewards based on the provider's current situation and areas of interest when providing a reward. For example, the reward providing unit can prioritize rewards related to the provider's current areas of interest. The reward providing unit can also adjust the level of detail of the reward depending on the provider's current situation (e.g., whether at work or on vacation). The reward providing unit can also select a reward provision method based on the provider's current activity (e.g., whether moving or stationary). In this way, the reward providing unit can provide more relevant rewards by filtering rewards based on the provider's current situation and areas of interest. Some or all of the above-described processing in the reward providing unit can be performed using AI, for example, or without AI. For example, the reward providing unit can input data on the provider's current situation and areas of interest to the generation AI and cause the generation AI to perform filtering. In this way, more relevant rewards can be provided by filtering rewards based on the provider's current situation and areas of interest.

[0103] The reward providing unit can estimate the user's emotions and determine the priority of rewards based on the estimated user emotions. The reward providing unit can estimate the user's emotions and determine the priority of rewards based on the estimated user emotions. For example, when the user is stressed, the reward providing unit can prioritize rewards that are highly important. Furthermore, when the user is relaxed, the reward providing unit can prioritize rewards that require a quick response. Furthermore, when the user is in a hurry, the reward providing unit can prioritize rewards that require a quick response. This allows the reward providing unit to prioritize rewards according to the user's emotions, thereby providing more appropriate rewards. Emotion estimation is realized using an emotion estimation function, for example, using 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 reward providing unit may be performed using an AI, for example, or without an AI. For example, the reward providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of rewards. This allows rewards to be prioritized according to the user's emotions, making it possible to provide more appropriate rewards.

[0104] The reward providing unit can prioritize providing highly relevant rewards by taking into consideration the geographical location information of the provider when providing a reward. The reward providing unit can prioritize providing highly relevant rewards by taking into consideration the geographical location information of the provider when providing a reward. For example, the reward providing unit prioritizes providing a reward related to the provider's current location. The reward providing unit can also provide a region-specific reward based on the geographical location information of the provider. Furthermore, if the provider is traveling, the reward providing unit can also provide a reward related to the provider's destination. In this way, the reward providing unit can provide highly relevant rewards by taking into consideration the geographical location information of the provider. Some or all of the above-described processing in the reward providing unit may be performed using AI, for example, or may be performed without using AI. For example, the reward providing unit can input the geographical location information of the provider to the generation AI and cause the generation AI to provide highly relevant rewards. In this way, highly relevant rewards can be provided by taking into consideration the geographical location information of the provider.

[0105] The reward providing unit can analyze the provider's social media activity and provide a related reward when providing a reward. The reward providing unit can analyze the provider's social media activity and provide a related reward when providing a reward. For example, the reward providing unit can provide a reward related to a topic in which the provider is interested on social media. The reward providing unit can also analyze the provider's social media activity history and provide a reward that is likely to interest the provider. Furthermore, the reward providing unit can also provide a reward related to accounts the provider follows on social media. In this way, the reward providing unit can provide a related reward by analyzing the provider's social media activity. Some or all of the above-mentioned processing in the reward providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the reward providing unit can input the provider's social media activity data into a generation AI and cause the generation AI to provide a related reward. In this way, the reward providing unit can provide a related reward by analyzing the provider's social media activity.

[0106] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the data analysis unit can perform a detailed analysis to provide deep insights. Furthermore, if the user is in a hurry, the data analysis unit can perform a simplified analysis to quickly obtain results. Furthermore, if the user is excited, the data analysis unit can provide visually stimulating analysis results. This allows the data analysis unit to provide more appropriate analysis results by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI 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 data analysis unit can be performed using, for example, an AI, or without an AI. For example, the data analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the data analysis method. This allows the data analysis method to be adjusted according to the user's emotions, providing more appropriate analysis results.

[0107] When analyzing data, the data analysis unit can analyze past data analysis history and select the optimal analysis method. When analyzing data, the data analysis unit analyzes past data analysis history and selects the optimal analysis method. The data analysis unit selects the optimal analysis method, for example, based on analysis methods used in the past. The data analysis unit can also select the most effective analysis method from the past analysis history. Furthermore, the data analysis unit can adjust the analysis method based on past analysis results. In this way, the data analysis unit can select the optimal analysis method by analyzing the past data analysis history. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input past data analysis history to a generation AI and have the generation AI select the optimal analysis method. In this way, the optimal analysis method can be selected by analyzing the past data analysis history.

[0108] The data analysis unit can apply different analysis algorithms depending on the type of data provided during data analysis. The data analysis unit can apply different analysis algorithms depending on the type of data provided during data analysis. For example, the data analysis unit can apply a natural language processing algorithm to text data. The data analysis unit can also apply an image recognition algorithm to image data. The data analysis unit can also apply a time series analysis algorithm to time series data. This allows the data analysis unit to provide more accurate analysis results by applying the optimal analysis algorithm depending on the type of data provided. Some or all of the above-mentioned processing in the data analysis unit can be performed using AI, for example, or without AI. For example, the data analysis unit can input the provided data into a generation AI and cause the generation AI to apply an appropriate analysis algorithm. This allows more accurate analysis results to be provided by applying the optimal analysis algorithm depending on the type of data provided.

[0109] The data analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The data analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the data analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the data analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the data analysis unit can provide a display method that focuses on the main points. This allows the data analysis unit to adjust the display method of the analysis results according to the user's emotions, enabling more appropriate display. 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 data analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions.

[0110] During data analysis, the data analysis unit can determine the analysis priority based on the submission time of the provided data. During data analysis, the data analysis unit can determine the analysis priority based on the submission time of the provided data. The data analysis unit can determine the analysis priority based on, for example, the recency of the submitted data. The data analysis unit can also determine the analysis priority based on the importance of the submitted data. Furthermore, the data analysis unit can also determine the analysis priority based on the relevance of the submitted data. As a result, the data analysis unit can analyze important data more quickly by determining the analysis priority based on the submission time of the provided data. Some or all of the above-described processing in the data analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the data analysis unit can input the submission time of the submitted data to the generation AI and have the generation AI determine the analysis priority. As a result, by determining the analysis priority based on the submission time of the provided data, important data can be analyzed more quickly.

[0111] The data analysis unit can adjust the order of analysis based on the relevance of the provided data during data analysis. The data analysis unit can adjust the order of analysis based on the relevance of the provided data during data analysis. For example, the data analysis unit prioritizes analysis of the most relevant data based on the relevance of the provided data. The data analysis unit can also adjust the order of analysis based on the importance of the provided data. Furthermore, the data analysis unit can also adjust the order of analysis based on the recency of the provided data. As a result, the data analysis unit can prioritize analysis of more relevant data by adjusting the order of analysis based on the relevance of the provided data. Some or all of the above-described processing in the data analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the data analysis unit can input the relevance of the provided data to the generation AI and cause the generation AI to adjust the order of analysis. As a result, as a result of adjusting the order of analysis based on the relevance of the provided data, more relevant data can be prioritized for analysis.

[0112] The security protection unit can estimate a user's emotion and adjust a security protection method based on the estimated user's emotion. The security protection unit can estimate a user's emotion and adjust a security protection method based on the estimated user's emotion. For example, when the user is nervous, the security protection unit can provide a simple and highly visible security protection method. Furthermore, when the user is relaxed, the security protection unit can provide a security protection method that includes detailed information. Furthermore, when the user is in a hurry, the security protection unit can provide a security protection method that focuses on the main points. This allows the security protection unit to adjust the security protection method according to the user's emotion, thereby providing more appropriate security protection. 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-mentioned processing in the security protection unit may be performed using AI, for example, or without AI. For example, the security protection unit can input user emotion data into the generation AI and cause the generation AI to adjust the security protection method. This allows the security protection method to be adjusted according to the user's feelings, thereby providing more appropriate security protection.

[0113] The security protection unit can analyze past security incident history and select the optimal protection method during security protection. The security protection unit analyzes past security incident history and selects the optimal protection method during security protection. The security protection unit, for example, selects the optimal protection method based on past security incident history. The security protection unit can also learn from past incidents and select a protection method to prevent similar incidents. Furthermore, the security protection unit can analyze past incident history and select the most effective protection method. In this way, the security protection unit can select the optimal protection method by analyzing past security incident history. Some or all of the above-mentioned processing in the security protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the security protection unit can input past security incident history into a generation AI and have the generation AI select the optimal protection method. In this way, the optimal protection method can be selected by analyzing past security incident history.

[0114] The security protection unit can apply different protection algorithms depending on the type of data provided during security protection. The security protection unit can apply different protection algorithms depending on the type of data provided during security protection. For example, the security protection unit applies an encryption algorithm to text data. The security protection unit can also apply a watermarking algorithm to image data. The security protection unit can also apply a data masking algorithm to time-series data. This allows the security protection unit to provide more appropriate security protection by applying the optimal protection algorithm depending on the type of data provided. Some or all of the above-mentioned processing in the security protection unit may be performed using AI, for example, or may be performed without using AI. For example, the security protection unit can input the provided data to a generation AI and cause the generation AI to apply an appropriate protection algorithm. This allows more appropriate security protection to be provided by applying the optimal protection algorithm depending on the type of data provided.

[0115] The security protection unit can estimate the user's emotions and determine the priority of security protection based on the estimated user emotions. The security protection unit can estimate the user's emotions and determine the priority of security protection based on the estimated user emotions. For example, when the user is stressed, the security protection unit can prioritize providing security protection with high importance. Furthermore, when the user is relaxed, the security protection unit can prioritize providing security protection that is interesting. Furthermore, when the user is in a hurry, the security protection unit can prioritize providing security protection that requires a quick response. In this way, the security protection unit can determine the priority of security protection according to the user's emotions and provide more appropriate security protection. Emotion estimation is realized using an emotion estimation function, for example, using 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 security protection unit may be performed, for example, using AI or without AI. For example, the security protection unit can input user emotion data into the generation AI and have the generation AI determine the priority of security protection. This allows the security protection priority to be determined according to the user's emotion, thereby providing more appropriate security protection.

[0116] The security protection unit can determine the priority of protection based on the time of submission of the provided data during security protection. The security protection unit can determine the priority of protection based on the time of submission of the provided data during security protection. The security protection unit can determine the priority of protection based on, for example, the newness of the submitted data. The security protection unit can also determine the priority of protection based on the importance of the submitted data. Furthermore, the security protection unit can also determine the priority of protection based on the relevance of the submitted data. As a result, the security protection unit can protect important data more quickly by determining the priority of protection based on the time of submission of the provided data. Some or all of the above-described processing in the security protection unit can be performed using AI, for example, or without AI. For example, the security protection unit can input the time of submission of the submitted data to the generation AI and cause the generation AI to determine the priority of protection. As a result, the security protection unit can protect important data more quickly by determining the priority of protection based on the time of submission of the provided data.

[0117] The security protection unit can adjust the order of protection based on the relevance of the provided data during security protection. The security protection unit can adjust the order of protection based on the relevance of the provided data during security protection. For example, the security protection unit prioritizes protection of the most relevant data based on the relevance of the provided data. The security protection unit can also adjust the order of protection based on the importance of the provided data. Furthermore, the security protection unit can also adjust the order of protection based on the recency of the provided data. As a result, the security protection unit can prioritize protection of more relevant data by adjusting the order of protection based on the relevance of the provided data. Some or all of the above-described processing in the security protection unit can be performed using AI, for example, or without using AI. For example, the security protection unit can input the relevance of the provided data to the generation AI and cause the generation AI to adjust the order of protection. As a result, as a result of adjusting the order of protection based on the relevance of the provided data, more relevant data can be prioritized for protection.

[0118] The privacy protection unit can estimate a user's emotions and adjust the privacy protection method based on the estimated user emotions. The privacy protection unit can estimate a user's emotions and adjust the privacy protection method based on the estimated user emotions. For example, if the user is nervous, the privacy protection unit can provide a simple, highly visible privacy protection method. Furthermore, if the user is relaxed, the privacy protection unit can provide a privacy protection method that includes detailed information. Furthermore, if the user is in a hurry, the privacy protection unit can provide a privacy protection method that focuses on the main points. This allows the privacy protection unit to adjust the privacy protection method according to the user's emotions, thereby providing more appropriate privacy protection. 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 privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input user emotion data into the generation AI and cause the generation AI to adjust the privacy protection method. This allows for more appropriate privacy protection by adjusting the privacy protection method according to the user's feelings.

[0119] The privacy protection unit can analyze past privacy incident history and select the optimal protection method during privacy protection. The privacy protection unit can analyze past privacy incident history and select the optimal protection method during privacy protection. For example, the privacy protection unit selects the optimal protection method based on past privacy incident history. The privacy protection unit can also learn from past incidents and select a protection method to prevent similar incidents. Furthermore, the privacy protection unit can analyze past incident history and select the most effective protection method. As a result, the privacy protection unit can select the optimal protection method by analyzing past privacy incident history. Some or all of the above-mentioned processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input past privacy incident history into a generation AI and have the generation AI select the optimal protection method. As a result, the optimal protection method can be selected by analyzing past privacy incident history.

[0120] The privacy protection unit can apply different protection algorithms depending on the type of data provided during privacy protection. The privacy protection unit can apply different protection algorithms depending on the type of data provided during privacy protection. For example, the privacy protection unit can apply an anonymization algorithm to text data. The privacy protection unit can also apply a facial recognition algorithm to image data. The privacy protection unit can also apply a data masking algorithm to time-series data. This allows the privacy protection unit to provide more appropriate privacy protection by applying the optimal protection algorithm depending on the type of data provided. Some or all of the above-mentioned processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input the provided data to a generation AI and cause the generation AI to apply an appropriate protection algorithm. This allows more appropriate privacy protection to be provided by applying the optimal protection algorithm depending on the type of data provided.

[0121] The privacy protection unit can estimate a user's emotions and determine the priority of privacy protection based on the estimated user emotions. The privacy protection unit can estimate a user's emotions and determine the priority of privacy protection based on the estimated user emotions. For example, when a user is stressed, the privacy protection unit can prioritize providing privacy protection with a high level of importance. Furthermore, when a user is relaxed, the privacy protection unit can prioritize providing privacy protection that is interesting. Furthermore, when a user is in a hurry, the privacy protection unit can prioritize providing privacy protection that requires a quick response. In this way, the privacy protection unit can provide more appropriate privacy protection by determining the priority of privacy protection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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 privacy protection unit can be performed, for example, using AI or without AI. For example, the privacy protection unit can input user emotion data into the generation AI and have the generation AI determine the priority of privacy protection. This allows the priority of privacy protection to be determined according to the user's emotion, thereby providing more appropriate privacy protection.

[0122] The privacy protection unit can determine the priority of protection based on the time of submission of the provided data during privacy protection. The privacy protection unit can determine the priority of protection based on the time of submission of the provided data during privacy protection. The privacy protection unit can determine the priority of protection based on, for example, the recency of the submitted data. The privacy protection unit can also determine the priority of protection based on the importance of the submitted data. Furthermore, the privacy protection unit can also determine the priority of protection based on the relevance of the submitted data. As a result, the privacy protection unit can protect important data more quickly by determining the priority of protection based on the time of submission of the provided data. Some or all of the above-described processing in the privacy protection unit can be performed using AI, for example, or without AI. For example, the privacy protection unit can input the time of submission of the submitted data into the generation AI and have the generation AI determine the priority of protection. As a result, important data can be protected more quickly by determining the priority of protection based on the time of submission of the provided data.

[0123] The privacy protection unit can adjust the order of protection based on the relevance of the provided data during privacy protection. The privacy protection unit can adjust the order of protection based on the relevance of the provided data during privacy protection. For example, the privacy protection unit prioritizes protecting the most relevant data based on the relevance of the provided data. The privacy protection unit can also adjust the order of protection based on the importance of the provided data. Furthermore, the privacy protection unit can also adjust the order of protection based on the recency of the provided data. As a result, the privacy protection unit can prioritize protecting more relevant data by adjusting the order of protection based on the relevance of the provided data. Some or all of the above-described processing in the privacy protection unit can be performed using AI, for example, or without using AI. For example, the privacy protection unit can input the relevance of the provided data to the generation AI and cause the generation AI to adjust the order of protection. As a result, the privacy protection unit can prioritize protecting more relevant data by adjusting the order of protection based on the relevance of the provided data. === Hard Collateral 1-1 === Each of the multiple elements, including the data providing unit, data searching unit, action performing unit, reward providing unit, data analysis unit, security protection unit, privacy protection unit, and emotion estimation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data providing unit is realized by the control unit 46A of the smart device 14 and provides real-time data and actionable information. The data searching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and efficiently searches the provided data. The action performing unit is realized, for example, by the control unit 46A of the smart device 14 and performs an action based on the searched data. The reward providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a reward based on the executed action. The data analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the provided data. The security protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the security of the data. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and protects the privacy of data. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of providing data. === Hard Collateral 1-2 === Each of the multiple elements, including the data providing unit, data searching unit, action performing unit, reward providing unit, data analysis unit, security protection unit, privacy protection unit, and emotion estimation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data providing unit is realized by the control unit 46A of the smart glasses 214 and provides real-time data and actionable information. The data searching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and efficiently searches the provided data. The action performing unit is realized, for example, by the control unit 46A of the smart glasses 214 and performs an action based on the searched data. The reward providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a reward based on the executed action. The data analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the provided data. The security protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the security of the data. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and protects the privacy of data. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of providing data. === Hard Collateral 1-3 === Each of the multiple elements, including the data providing unit, data searching unit, action performing unit, reward providing unit, data analysis unit, security protection unit, privacy protection unit, and emotion estimation unit, is implemented, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the data providing unit is implemented by the control unit 46A of the headset type terminal 314 and provides real-time data and actionable information. The data searching unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and efficiently searches the provided data. The action performing unit is implemented, for example, by the control unit 46A of the headset type terminal 314 and performs an action based on the searched data. The reward providing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides a reward based on the executed action. The data analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the provided data. The security protection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and protects the security of the data. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and protects the privacy of data. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of providing data. === Hard Collateral 1-4 === Each of the multiple elements, including the data providing unit, data searching unit, action performing unit, reward providing unit, data analysis unit, security protection unit, privacy protection unit, and emotion estimation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data providing unit is realized by the control unit 46A of the robot 414 and provides real-time data and actionable information. The data searching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and efficiently searches the provided data. The action performing unit is realized, for example, by the control unit 46A of the robot 414 and performs an action based on the searched data. The reward providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a reward based on the executed action. The data analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the provided data. The security protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the security of the data. The privacy protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and protects the privacy of data. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of providing data.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The generation AI connect system can further include a reliability evaluation unit that evaluates the reliability of data providers. The reliability evaluation unit can score reliability based on the data provider's past data provision history and evaluations, and preferentially display reliable data providers to data users. For example, the reliability evaluation unit can evaluate the accuracy and timeliness of data previously provided by the data provider and calculate a reliability score. The reliability evaluation unit can also re-evaluate the reliability based on feedback and evaluations received by the data provider. Furthermore, the reliability evaluation unit can display the reliability score of the data provider to the data user, providing it as reference information when selecting a reliable data provider. This allows data users to efficiently obtain reliable data.

[0126] The generative AI connect system can further include an incentive adjustment unit that estimates the emotions of the data provider and adjusts the incentive for data provision based on the estimated emotions. The incentive adjustment unit can provide rewards or benefits according to the emotions to increase the data provider's motivation. For example, if the data provider is feeling stressed, the incentive adjustment unit can provide a relaxation benefit or a vacation. Furthermore, if the data provider is highly motivated, the incentive adjustment unit can provide additional rewards or benefits. Furthermore, the incentive adjustment unit can adjust the type and timing of rewards based on the emotions of the data provider. This can maintain the motivation of data providers and encourage them to provide high-quality data.

[0127] The generative AI connect system can further include a personalized search unit that analyzes the data user's search history and provides personalized search results. The personalized search unit can analyze the data user's past search history and behavioral patterns and prioritize displaying highly relevant search results. For example, the personalized search unit can prioritize displaying related data based on keywords and categories that the data user has searched for in the past. The personalized search unit can also customize search results based on the data user's interests. Furthermore, the personalized search unit can learn the data user's behavioral patterns and provide optimal search results for future searches. This allows the data user to efficiently obtain the information they need.

[0128] The generative AI connect system can further include a feedback adjustment unit that estimates the emotions of the data provider and adjusts the feedback on the data provision based on the estimated emotions. The feedback adjustment unit can adjust the content and timing of the feedback received by the data provider according to the emotions. For example, if the data provider is feeling stressed, the feedback adjustment unit can provide positive feedback preferentially to increase motivation. Alternatively, if the data provider is relaxed, the feedback adjustment unit can provide detailed feedback and specifically indicate areas for improvement. Furthermore, the feedback adjustment unit can adjust the frequency and format of feedback based on the emotions of the data provider. This allows the data provider to receive appropriate feedback and improve the quality of their data provision.

[0129] The generative AI connect system can further include a display adjustment unit that estimates the emotions of the data user and adjusts the way the data is displayed based on the estimated emotions. The display adjustment unit can change the display format and layout of the data according to the emotions of the data user. For example, if the data user is nervous, the display adjustment unit can provide a simple, highly visible display format. If the data user is relaxed, the display adjustment unit can provide a display format that includes detailed information. Furthermore, if the data user is in a hurry, the display adjustment unit can provide a display format that focuses on the main points. This allows the data user to view data in the optimal display format according to their emotions.

[0130] The generation AI connect system can further include a region-specific data providing unit that takes into consideration the geographical location information of the data provider and preferentially provides region-specific data. The region-specific data providing unit can preferentially provide region-related data based on the data provider's current location and past location information. For example, the region-specific data providing unit can provide event information and store information in the region where the data provider is currently located. The region-specific data providing unit can also provide data related to regions that the data provider has visited in the past. Furthermore, the region-specific data providing unit can analyze the data provider's movement patterns and provide data related to future destinations. This allows the data provider to efficiently obtain useful information related to the region.

[0131] The generation AI connect system can further include a priority determination unit that estimates the emotions of the data user and determines the priority of data based on the estimated emotions. The priority determination unit can adjust the priority of data to be displayed according to the emotions of the data user. For example, if the data user is feeling stressed, the priority determination unit can prioritize displaying data of high importance. Also, if the data user is relaxed, the priority determination unit can prioritize displaying interesting data. Furthermore, if the data user is in a hurry, data that requires a quick response can be prioritized. This allows the data user to efficiently obtain the optimal data according to their emotions.

[0132] The generative AI connect system may further include a social media linking unit that analyzes the social media activities of the data provider and provides relevant data. The social media linking unit may provide relevant data based on topics that the data provider is interested in and accounts that the data provider follows on social media. For example, the social media linking unit may preferentially provide data related to topics that the data provider is interested in. The social media linking unit may also provide data related to accounts that the data provider follows. Furthermore, the social media linking unit may analyze the data provider's social media activity history and provide data that may be of interest to the data provider. This allows the data provider to efficiently obtain highly relevant data based on their social media activities.

[0133] The generative AI connect system can further include a filtering unit that estimates the emotions of the data user and filters the data based on the estimated emotions. The filtering unit can adjust the type and amount of data to be displayed depending on the emotions of the data user. For example, if the data user is nervous, the filtering unit can display only simple and important data. Alternatively, if the data user is relaxed, detailed data can be displayed. Furthermore, if the data user is in a hurry, data that requires a quick response can be displayed preferentially. This allows the data user to efficiently obtain the optimal data according to their emotions.

[0134] The generation AI connect system can further include a history analysis unit that analyzes the data provider's past data provision history and selects the optimal data provision method. The history analysis unit can analyze the type and frequency of data provided by the data provider in the past and provide similar data preferentially. For example, the history analysis unit may reuse a data provision method that the data provider has previously highly rated. The history analysis unit can also provide data at the optimal timing based on the data provider's past data provision history. Furthermore, the history analysis unit can learn data provision patterns based on the data provider's provision history and select the optimal method for future data provision. This allows data providers to provide data efficiently.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The data provider provides data. For example, it provides real-time data and actionable information. Specifically, it can provide data that is updated in real time, such as restaurant reservation status and inventory information. The data provider provides the data to the generation AI connect. Step 2: The data search unit searches the data provided by the data provider. For example, you can check the reservation status of a specific restaurant or check inventory information. The data search unit can efficiently search the provided data. Step 3: The action execution unit executes an action based on the data retrieved by the data retrieval unit. For example, the action execution unit can make a restaurant reservation or edit inventory. By executing an action based on the retrieved data, the action execution unit allows the user to perform the action directly. Step 4: The reward providing unit provides a reward based on the action performed by the action performing unit. For example, the data provider can receive a reward. The reward providing unit provides a reward based on the action performed, allowing the data provider to receive a reward.

[0137] 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.

[0138] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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 data providing unit that provides data; a data search unit that searches for data provided by the data providing unit; an action execution unit that executes an action based on the data searched by the data search unit; a reward providing unit that provides a reward based on the action performed by the action performing unit; Equipped with A system characterized by:

2. Equipped with a data analysis unit that analyzes the provided data 2. The system of claim 1.

3. Equipped with a security protection unit to protect data safety 2. The system of claim 1.

4. Equipped with a privacy protection department to protect data privacy 2. The system of claim 1.

5. The data providing unit Providing real-time data or actionable information 2. The system of claim 1.

6. The data search unit Searching for provided data 2. The system of claim 1.

7. The action execution unit Take action based on the data found 2. The system of claim 1.

8. The reward providing unit: Offer rewards based on actions taken 2. The system of claim 1.

9. The data providing unit Estimate user emotions and adjust the timing of data provision based on the estimated user emotions 2. The system of claim 1.

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