system

Abstract

The system according to this embodiment aims to provide generated results that reflect the user's individuality and identity. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a protection unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The generation unit creates generation results based on the information analyzed by the analysis unit. The provision unit provides the generation results created by the generation unit to the user. The protection unit handles user information securely.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <x In the prior art, there is a problem that the generation result by AI may be contrary to the user's intention, and it is difficult to reflect the user's personality and identity.

[0005] The system according to the embodiment aims to provide a generation result that reflects the user's personality and identity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a protection unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The generation unit creates generation results based on the information analyzed by the analysis unit. The provision unit provides the generation results created by the generation unit to the user. The protection unit handles user information securely. [Effects of the Invention]

[0007] The system according to this embodiment can provide generated results that reflect the user's individuality and identity. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The generation system according to an embodiment of the present invention is a system that uses AI to provide generation results that incorporate the user's personality and identity. In this generation system, when the user inputs keywords to the generation AI and the generation AI creates generation results based on those keywords, information that reflects the user's personality and identity is also input at the same time. For example, this information may include the user's past generation results, preferences, and interests. The generation AI analyzes this information and provides generation results that align with the user's intentions. Through this mechanism, the user can obtain generation results that reflect their personality and identity. For example, if the user inputs "travel plan," the generation AI will consider the user's past travel history and preferences to suggest the optimal travel plan. Also, if the user inputs "cooking recipe," the generation AI will consider the user's preferences and past cooking experience to suggest the optimal recipe. Through this mechanism, the user can obtain generation results that reflect their personality and identity. By using the generation AI, the user can obtain results that align with their intentions, providing a more satisfying user experience. In this way, the generation system can provide generation results that reflect the user's personality and identity.

[0029] The generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a protection unit. The collection unit collects user information. User information includes, but is not limited to, personal information, behavioral history, and preferences. The collection unit collects, for example, information such as the user's past generation results, preferences, and interests. The collection unit can also collect information based on survey results and behavioral history. For example, the collection unit collects reports and recommendations that the user has generated in the past. The collection unit can also collect the user's browsing history and search history. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, a data analysis algorithm. For example, the analysis unit generates data to reflect the user's personality and identity. The analysis unit can also analyze the user's personality traits and behavioral patterns. For example, the analysis unit identifies the user's personality based on the user's past behavioral patterns. The generation unit creates generation results based on the information analyzed by the analysis unit. The generation unit creates generation results using, for example, a generation AI. The generation AI creates generated results using, for example, machine learning models or natural language generation technologies. For example, the generation unit creates generated results that align with the user's intent. The generation unit can also select the optimal generation method by referring to the user's past generation results. For example, the generation unit creates generated results based on the generation method the user has preferred in the past. The delivery unit provides the generated results created by the generation unit to the user. The delivery unit provides the generated results through, for example, a web application or a mobile application. For example, the delivery unit displays the generated results in real time. The delivery unit can also send the generated results via email. For example, the delivery unit provides the generated results in PDF format. The protection unit securely stores the collected information and uses it appropriately during analysis and generation. The protection unit protects the information using, for example, data encryption and access control. For example, the protection unit encrypts and stores the collected information. The protection unit can also restrict access to the information. For example, the protection unit allows only specific users to access the information.As a result, the generation system according to the embodiment can provide generation results that reflect the user's individuality and identity.

[0030] The data collection unit can collect information about a user's past generation results, preferences, and interests. For example, the data collection unit can collect reports and recommendations that a user has previously generated. For instance, the data collection unit can retrieve reports that a user has previously generated from a database. The data collection unit can also conduct surveys to collect information about a user's preferences and interests. For example, the data collection unit can ask users questions about their preferences and interests and collect their responses. The data collection unit can also collect a user's behavioral history. For example, the data collection unit can collect a user's website browsing history and search history. By collecting information such as a user's past generation results, preferences, and interests, the data collection unit can provide generation results that reflect the user's personality and identity.

[0031] The analysis unit can analyze collected information and generate data that reflects the user's personality and identity. For example, the analysis unit analyzes information using data analysis algorithms. For example, the analysis unit generates data that reflects the user's personality and identity. For example, the analysis unit can also analyze the user's personality traits and behavioral patterns. For example, the analysis unit identifies the user's personality based on the user's past behavioral patterns. The analysis unit can also analyze the user's interests and preferences. For example, the analysis unit identifies the user's interests by analyzing the user's browsing history and search history. As a result, the analysis unit can provide more accurate results by analyzing collected information and generating data that reflects the user's personality and identity.

[0032] The generation unit can create generated results that align with the user's intent using generative AI. For example, the generation unit can create generated results using generative AI. The generative AI can create generated results using, for example, machine learning models or natural language generation technologies. For example, the generation unit can create generated results that align with the user's intent. Furthermore, the generation unit can select the optimal generation method by referring to the user's past generation results. For example, the generation unit can create generated results based on the generation method the user has preferred in the past. This allows the use of generative AI to provide generated results that align with the user's intent.

[0033] The service provider can provide the user with the generation results created by the generation unit. The service provider can provide the generation results, for example, through a web application or a mobile application. For example, the service provider can display the generation results in real time. The service provider can also send the generation results via email. For example, the service provider can provide the generation results in PDF format. This improves user satisfaction by providing users with the generation results created by the generation unit.

[0034] The protection unit securely stores the collected information and makes it available for appropriate use during analysis and generation. The protection unit protects the information using, for example, data encryption and access control. For instance, the protection unit encrypts and stores the collected information. Furthermore, the protection unit can restrict access to the information. For example, it allows only specific users to access the information. This ensures the security of the information by securely storing the collected information and making it available for appropriate use during analysis and generation.

[0035] The data collection unit can analyze the user's past generation results and select an appropriate information collection method. For example, the data collection unit can prioritize collecting similar information based on the generation results the user has preferred in the past. It can also choose not to collect related information based on the generation results the user has avoided in the past. For instance, the data collection unit can analyze patterns in the user's past generation results and select the most effective information collection method. This allows for the selection of the optimal information collection method by analyzing the user's past generation results.

[0036] The data collection unit can filter information based on the user's current areas of interest and activities. For example, it can prioritize collecting information related to topics the user is currently interested in. It can also filter information based on the user's current activities (e.g., working or on vacation). For instance, it can collect only highly relevant information based on the user's current areas of interest. This allows for the collection of highly relevant information by filtering it based on the user's current areas of interest and activities.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, it can prioritize collecting local news and event information related to the user's current location. It can also collect information on nearby restaurants and tourist attractions based on the user's location. For instance, it can prioritize collecting weather and traffic information related to the user's geographical location. This allows for the collection of highly relevant information by considering the user's geographical location.

[0038] The data collection unit analyzes the user's social media activity during information gathering and can collect relevant information. For example, the data collection unit collects relevant information based on what the user shares on social media. The data collection unit can also collect information related to topics that the user's followers and friends on social media are interested in. For example, the data collection unit analyzes the user's social media activity patterns and collects the most relevant information. In this way, highly relevant information can be collected by analyzing the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit will perform a detailed analysis on information of high importance. Conversely, it can perform a simplified analysis on information of low importance. For instance, the analysis unit will appropriately allocate analysis resources according to importance. By adjusting the level of detail of the analysis based on the importance of the collected information, efficient analysis becomes possible.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, it can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. For example, it can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible.

[0041] The analysis unit can adjust the order of analysis based on when the information was submitted. For example, the analysis unit prioritizes the analysis of the most recent information. It can also postpone the analysis of older information. For instance, the analysis unit appropriately allocates analysis resources according to the submission date. This allows for efficient analysis by adjusting the order of analysis based on the information submission date.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to the user's current areas of interest. It can also prioritize the analysis of information related to the user's past output. For example, the analysis unit can prioritize the analysis of information related to the user's current activity status. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0043] The generation unit can select the optimal generation method by referring to the user's past generation results during generation. For example, the generation unit may use a similar method based on the user's past preferred generation method. Alternatively, the generation unit may use a different method based on the user's past avoided generation method. For example, the generation unit may analyze the patterns of the user's past generation results and select the most effective generation method. In this way, the optimal generation method can be selected by referring to the user's past generation results.

[0044] The generation unit can customize the generated results based on the user's current areas of interest during the generation process. For example, the generation unit can provide generated results related to topics the user is currently interested in. Furthermore, the generation unit can also provide appropriate generated results based on the user's current activities. For example, the generation unit can provide highly relevant generated results based on the user's current areas of interest. This allows for the provision of more relevant generated results by customizing the results based on the user's current areas of interest.

[0045] The generation unit can provide optimal generation results by considering the user's geographical location information during generation. For example, the generation unit can provide generation results related to the user's current location. Furthermore, the generation unit can also provide generation results that include nearby information based on the user's location information. For example, the generation unit can provide generation results related to the user's geographical location. This allows for the provision of optimal generation results by considering the user's geographical location information.

[0046] The generation unit can analyze the user's social media activity during generation to customize the generated results. For example, the generation unit can provide relevant generated results based on what the user has shared on social media. It can also provide generated results related to topics of interest to the user's social media followers and friends. For instance, the generation unit can analyze the user's social media activity patterns and provide the most relevant generated results. This allows for the provision of more relevant generated results by analyzing the user's social media activity.

[0047] The service provider can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may use a similar method based on the user's past preferred delivery method. Alternatively, the service provider may use a different method based on the user's past avoidance of delivery methods. For example, the service provider may analyze the user's past usage history and select the most effective delivery method. In this way, the optimal delivery method can be selected by referring to the user's past usage history.

[0048] The service provider can customize the content offered based on the user's current areas of interest at the time of delivery. For example, the service provider can offer content related to topics the user is currently interested in. Furthermore, the service provider can also offer appropriate content based on the user's current activities. For example, the service provider can offer highly relevant content based on the user's current areas of interest. This allows for the provision of more relevant content by customizing it based on the user's current areas of interest.

[0049] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can provide content related to the user's current location. Alternatively, the service provider can provide content that includes nearby information based on the user's location information. For example, the service provider can provide content related to the user's geographical location. This allows the service provider to select the optimal delivery method by considering the user's geographical location information.

[0050] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can provide relevant content based on what the user has shared on social media. It can also provide content related to topics of interest to the user's social media followers and friends. For instance, the service provider can analyze the user's social media activity patterns and provide the most relevant content. This allows for the provision of more relevant content by analyzing the user's social media activity.

[0051] The protection unit can adjust the level of protection based on the importance of the collected information during protection. For example, the protection unit can apply strict protection methods to highly important information. Conversely, it can also apply standard protection methods to less important information. For instance, the protection unit can appropriately allocate protection resources according to importance. This allows for efficient information protection by adjusting the level of protection based on the importance of the collected information.

[0052] The protection unit can apply different protection algorithms depending on the category of information during protection. For example, it can apply an encryption algorithm to text data. It can also apply a watermarking algorithm to image data. For example, it can apply a speech recognition algorithm to audio data. By applying different protection algorithms depending on the category of information, more appropriate information protection becomes possible.

[0053] The protection unit can adjust the order of protection based on when the information was submitted. For example, the protection unit will prioritize the protection of the most recent information. It can also postpone the protection of older information. For instance, the protection unit will appropriately allocate protection resources according to the submission date. This allows for efficient information protection by adjusting the order of protection based on the submission date.

[0054] The protection unit can adjust the order of protection based on the relevance of the information during the protection process. For example, the protection unit can prioritize the protection of information related to the user's current areas of interest. It can also prioritize the protection of information related to the user's past output. For example, the protection unit can prioritize the protection of information related to the user's current activity status. This allows for efficient information protection by adjusting the order of protection based on the relevance of the information.

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

[0056] The service provider can consider the user's device environment when providing the generated results. For example, if the user is using a smartphone, the generated results can be provided in a mobile-friendly format. If the user is using a desktop computer, the service provider can also provide generated results containing more detailed information. Furthermore, the service provider can adjust the amount of data in the generated results, taking into account the user's internet connection status. For example, if the user is using a slow internet connection, a lightweight generated result can be provided. This allows the service provider to deliver generated results in the optimal format according to the user's device environment and internet connection status.

[0057] The protection unit can include a function to anonymize user data. For example, it can anonymize collected personal information so that individuals cannot be identified during analysis or generation. The protection unit can also store user data in distributed storage to improve data security. Furthermore, the protection unit can record access logs to user data and include a function to detect unauthorized access. This ensures that user data is securely protected and privacy is ensured.

[0058] The service provider can consider the user's device environment when providing the generated results. For example, if the user is using a smartphone, the generated results can be provided in a mobile-friendly format. If the user is using a desktop computer, the service provider can also provide generated results containing more detailed information. Furthermore, the service provider can adjust the amount of data in the generated results, taking into account the user's internet connection status. For example, if the user is using a slow internet connection, a lightweight generated result can be provided. This allows the service provider to deliver generated results in the optimal format according to the user's device environment and internet connection status.

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

[0060] Step 1: The collection unit collects user information. User information includes personal information, behavioral history, preferences, etc. The collection unit collects information such as the user's past output, preferences, interests, survey results, behavioral history, browsing history, and search history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses data analysis algorithms to generate data that reflects the user's individuality and identity, and analyzes personality traits and behavioral patterns. Step 3: The generation unit creates the generation results based on the information analyzed by the analysis unit. The generation unit uses generation AI, employing machine learning models and natural language generation technologies to create the generation results and provide results that align with the user's intent. It also selects the optimal generation method by referring to past generation results. Step 4: The delivery unit provides the user with the generation results created by the generation unit. The delivery unit can display the generation results in real time via a web application or mobile application, and can also send them via email. For example, the generation results can be provided in PDF format. Step 5: The protection unit securely stores the collected information and uses it appropriately for analysis and generation. The protection unit protects the information using data encryption and access control, ensuring that only specific users can access it.

[0061] (Example of form 2) The generation system according to an embodiment of the present invention is a system that uses AI to provide generation results that incorporate the user's personality and identity. In this generation system, when the user inputs keywords to the generation AI and the generation AI creates generation results based on those keywords, information that reflects the user's personality and identity is also input at the same time. For example, this information may include the user's past generation results, preferences, and interests. The generation AI analyzes this information and provides generation results that align with the user's intentions. Through this mechanism, the user can obtain generation results that reflect their personality and identity. For example, if the user inputs "travel plan," the generation AI will consider the user's past travel history and preferences to suggest the optimal travel plan. Also, if the user inputs "cooking recipe," the generation AI will consider the user's preferences and past cooking experience to suggest the optimal recipe. Through this mechanism, the user can obtain generation results that reflect their personality and identity. By using the generation AI, the user can obtain results that align with their intentions, providing a more satisfying user experience. In this way, the generation system can provide generation results that reflect the user's personality and identity.

[0062] The generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a protection unit. The collection unit collects user information. User information includes, but is not limited to, personal information, behavioral history, and preferences. The collection unit collects, for example, information such as the user's past generation results, preferences, and interests. The collection unit can also collect information based on survey results and behavioral history. For example, the collection unit collects reports and recommendations that the user has generated in the past. The collection unit can also collect the user's browsing history and search history. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using, for example, a data analysis algorithm. For example, the analysis unit generates data to reflect the user's personality and identity. The analysis unit can also analyze the user's personality traits and behavioral patterns. For example, the analysis unit identifies the user's personality based on the user's past behavioral patterns. The generation unit creates generation results based on the information analyzed by the analysis unit. The generation unit creates generation results using, for example, a generation AI. The generation AI creates generated results using, for example, machine learning models or natural language generation technologies. For example, the generation unit creates generated results that align with the user's intent. The generation unit can also select the optimal generation method by referring to the user's past generation results. For example, the generation unit creates generated results based on the generation method the user has preferred in the past. The delivery unit provides the generated results created by the generation unit to the user. The delivery unit provides the generated results through, for example, a web application or a mobile application. For example, the delivery unit displays the generated results in real time. The delivery unit can also send the generated results via email. For example, the delivery unit provides the generated results in PDF format. The protection unit securely stores the collected information and uses it appropriately during analysis and generation. The protection unit protects the information using, for example, data encryption and access control. For example, the protection unit encrypts and stores the collected information. The protection unit can also restrict access to the information. For example, the protection unit allows only specific users to access the information.As a result, the generation system according to the embodiment can provide generation results that reflect the user's individuality and identity.

[0063] The data collection unit can collect information about a user's past generation results, preferences, and interests. For example, the data collection unit can collect reports and recommendations that a user has previously generated. For instance, the data collection unit can retrieve reports that a user has previously generated from a database. The data collection unit can also conduct surveys to collect information about a user's preferences and interests. For example, the data collection unit can ask users questions about their preferences and interests and collect their responses. The data collection unit can also collect a user's behavioral history. For example, the data collection unit can collect a user's website browsing history and search history. By collecting information such as a user's past generation results, preferences, and interests, the data collection unit can provide generation results that reflect the user's personality and identity.

[0064] The analysis unit can analyze collected information and generate data that reflects the user's personality and identity. For example, the analysis unit analyzes information using data analysis algorithms. For example, the analysis unit generates data that reflects the user's personality and identity. For example, the analysis unit can also analyze the user's personality traits and behavioral patterns. For example, the analysis unit identifies the user's personality based on the user's past behavioral patterns. The analysis unit can also analyze the user's interests and preferences. For example, the analysis unit identifies the user's interests by analyzing the user's browsing history and search history. As a result, the analysis unit can provide more accurate results by analyzing collected information and generating data that reflects the user's personality and identity.

[0065] The generation unit can create generated results that align with the user's intent using generative AI. For example, the generation unit can create generated results using generative AI. The generative AI can create generated results using, for example, machine learning models or natural language generation technologies. For example, the generation unit can create generated results that align with the user's intent. Furthermore, the generation unit can select the optimal generation method by referring to the user's past generation results. For example, the generation unit can create generated results based on the generation method the user has preferred in the past. This allows the use of generative AI to provide generated results that align with the user's intent.

[0066] The service provider can provide the user with the generation results created by the generation unit. The service provider can provide the generation results, for example, through a web application or a mobile application. For example, the service provider can display the generation results in real time. The service provider can also send the generation results via email. For example, the service provider can provide the generation results in PDF format. This improves user satisfaction by providing users with the generation results created by the generation unit.

[0067] The protection unit securely stores the collected information and makes it available for appropriate use during analysis and generation. The protection unit protects the information using, for example, data encryption and access control. For instance, the protection unit encrypts and stores the collected information. Furthermore, the protection unit can restrict access to the information. For example, it allows only specific users to access the information. This ensures the security of the information by securely storing the collected information and making it available for appropriate use during analysis and generation.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is relaxed, the unit can collect data immediately, acquiring it at the time when the user is most receptive. Conversely, if the user is stressed, the unit can temporarily delay data collection, acquiring it after the user has calmed down. For example, if the user is excited, the unit can collect data quickly, acquiring it immediately to maintain the user's high level of interest. This allows for information to be collected at a more appropriate time by adjusting the timing of data collection based on the user's emotions.

[0069] The data collection unit can analyze the user's past generation results and select an appropriate information collection method. For example, the data collection unit can prioritize collecting similar information based on the generation results the user has preferred in the past. It can also choose not to collect related information based on the generation results the user has avoided in the past. For instance, the data collection unit can analyze patterns in the user's past generation results and select the most effective information collection method. This allows for the selection of the optimal information collection method by analyzing the user's past generation results.

[0070] The data collection unit can filter information based on the user's current areas of interest and activities. For example, it can prioritize collecting information related to topics the user is currently interested in. It can also filter information based on the user's current activities (e.g., working or on vacation). For instance, it can collect only highly relevant information based on the user's current areas of interest. This allows for the collection of highly relevant information by filtering it based on the user's current areas of interest and activities.

[0071] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on those emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting interesting information. Similarly, if the user is stressed, the data collection unit can prioritize collecting relaxing information. For instance, if the user is excited, the data collection unit will prioritize collecting stimulating information. This allows for the collection of more relevant information by prioritizing information based on the user's emotions.

[0072] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, it can prioritize collecting local news and event information related to the user's current location. It can also collect information on nearby restaurants and tourist attractions based on the user's location. For instance, it can prioritize collecting weather and traffic information related to the user's geographical location. This allows for the collection of highly relevant information by considering the user's geographical location.

[0073] The data collection unit analyzes the user's social media activity during information gathering and can collect relevant information. For example, the data collection unit collects relevant information based on what the user shares on social media. The data collection unit can also collect information related to topics that the user's followers and friends on social media are interested in. For example, the data collection unit analyzes the user's social media activity patterns and collects the most relevant information. In this way, highly relevant information can be collected by analyzing the user's social media activity.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. Conversely, if the user is stressed, the analysis unit can perform a concise and to-the-point analysis. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the analysis method based on the user's emotions, more appropriate analysis results can be provided.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. For example, the analysis unit will perform a detailed analysis on information of high importance. Conversely, it can perform a simplified analysis on information of low importance. For instance, the analysis unit will appropriately allocate analysis resources according to importance. By adjusting the level of detail of the analysis based on the importance of the collected information, efficient analysis becomes possible.

[0076] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, it can apply a natural language processing algorithm to text data. It can also apply an image recognition algorithm to image data. For example, it can apply a speech recognition algorithm to audio data. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible.

[0077] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is relaxed, the analysis unit will prioritize analyzing interesting information. Conversely, if the user is stressed, the analysis unit can prioritize analyzing relaxing information. For instance, if the user is excited, the analysis unit will prioritize analyzing stimulating information. By prioritizing analysis based on the user's emotions, more appropriate analysis results can be provided.

[0078] The analysis unit can adjust the order of analysis based on when the information was submitted. For example, the analysis unit prioritizes the analysis of the most recent information. It can also postpone the analysis of older information. For instance, the analysis unit appropriately allocates analysis resources according to the submission date. This allows for efficient analysis by adjusting the order of analysis based on the information submission date.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of information related to the user's current areas of interest. It can also prioritize the analysis of information related to the user's past output. For example, the analysis unit can prioritize the analysis of information related to the user's current activity status. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0080] The generation unit can estimate the user's emotions and adjust the presentation of the generated results based on those emotions. For example, if the user is relaxed, the generation unit will use a gentle presentation. If the user is stressed, the generation unit can use a concise and to-the-point presentation. For example, if the user is excited, the generation unit will use a visually stimulating presentation. By adjusting the presentation of the generated results based on the user's emotions, the system can provide more appropriate results.

[0081] The generation unit can select the optimal generation method by referring to the user's past generation results during generation. For example, the generation unit may use a similar method based on the user's past preferred generation method. Alternatively, the generation unit may use a different method based on the user's past avoided generation method. For example, the generation unit may analyze the patterns of the user's past generation results and select the most effective generation method. In this way, the optimal generation method can be selected by referring to the user's past generation results.

[0082] The generation unit can customize the generated results based on the user's current areas of interest during the generation process. For example, the generation unit can provide generated results related to topics the user is currently interested in. Furthermore, the generation unit can also provide appropriate generated results based on the user's current activities. For example, the generation unit can provide highly relevant generated results based on the user's current areas of interest. This allows for the provision of more relevant generated results by customizing the results based on the user's current areas of interest.

[0083] The generation unit can estimate the user's emotions and determine the priority of generated results based on those emotions. For example, if the user is relaxed, the generation unit will prioritize interesting results. It can also prioritize relaxing results if the user is stressed. For instance, if the user is excited, the generation unit will prioritize stimulating results. This allows for the provision of more appropriate results by prioritizing them based on the user's emotions.

[0084] The generation unit can provide optimal generation results by considering the user's geographical location information during generation. For example, the generation unit can provide generation results related to the user's current location. Furthermore, the generation unit can also provide generation results that include nearby information based on the user's location information. For example, the generation unit can provide generation results related to the user's geographical location. This allows for the provision of optimal generation results by considering the user's geographical location information.

[0085] The generation unit can analyze the user's social media activity during generation to customize the generated results. For example, the generation unit can provide relevant generated results based on what the user has shared on social media. It can also provide generated results related to topics of interest to the user's social media followers and friends. For instance, the generation unit can analyze the user's social media activity patterns and provide the most relevant generated results. This allows for the provision of more relevant generated results by analyzing the user's social media activity.

[0086] The delivery unit can estimate the user's emotions and adjust the delivery method based on those emotions. For example, if the user is relaxed, the delivery unit will deliver the results in a gentle manner. If the user is stressed, the delivery unit can deliver the results in a concise and to-the-point manner. For example, if the user is excited, the delivery unit will deliver the results in a visually stimulating manner. This allows for the selection of a more appropriate delivery method by adjusting the delivery method based on the user's emotions.

[0087] The service provider can select the optimal delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may use a similar method based on the user's past preferred delivery method. Alternatively, the service provider may use a different method based on the user's past avoidance of delivery methods. For example, the service provider may analyze the user's past usage history and select the most effective delivery method. In this way, the optimal delivery method can be selected by referring to the user's past usage history.

[0088] The service provider can customize the content offered based on the user's current areas of interest at the time of delivery. For example, the service provider can offer content related to topics the user is currently interested in. Furthermore, the service provider can also offer appropriate content based on the user's current activities. For example, the service provider can offer highly relevant content based on the user's current areas of interest. This allows for the provision of more relevant content by customizing it based on the user's current areas of interest.

[0089] The service provider can estimate the user's emotions and prioritize the content offered based on those emotions. For example, if the user is relaxed, the service provider will prioritize interesting content. Similarly, if the user is stressed, the service provider can prioritize relaxing content. For instance, if the user is excited, the service provider will prioritize stimulating content. This allows for more appropriate content to be offered by prioritizing it based on the user's emotions.

[0090] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, the service provider can provide content related to the user's current location. Alternatively, the service provider can provide content that includes nearby information based on the user's location information. For example, the service provider can provide content related to the user's geographical location. This allows the service provider to select the optimal delivery method by considering the user's geographical location information.

[0091] The service provider can analyze the user's social media activity and customize the content offered at the time of delivery. For example, the service provider can provide relevant content based on what the user has shared on social media. It can also provide content related to topics of interest to the user's social media followers and friends. For instance, the service provider can analyze the user's social media activity patterns and provide the most relevant content. This allows for the provision of more relevant content by analyzing the user's social media activity.

[0092] The protection unit can estimate the user's emotions and adjust the method of information protection based on those emotions. For example, if the user is relaxed, the protection unit will apply the normal protection method. Conversely, if the user is stressed, the protection unit can apply a more stringent protection method. For instance, if the user is agitated, the protection unit will apply a method to quickly protect the information. This allows for more appropriate information protection by adjusting the method of information protection based on the user's emotions.

[0093] The protection unit can adjust the level of protection based on the importance of the collected information during protection. For example, the protection unit can apply strict protection methods to highly important information. Conversely, it can also apply standard protection methods to less important information. For instance, the protection unit can appropriately allocate protection resources according to importance. This allows for efficient information protection by adjusting the level of protection based on the importance of the collected information.

[0094] The protection unit can apply different protection algorithms depending on the category of information during protection. For example, it can apply an encryption algorithm to text data. It can also apply a watermarking algorithm to image data. For example, it can apply a speech recognition algorithm to audio data. By applying different protection algorithms depending on the category of information, more appropriate information protection becomes possible.

[0095] The protection unit can estimate the user's emotions and determine the priority of information protection based on those emotions. For example, if the user is relaxed, the protection unit will prioritize normal protection methods. Conversely, if the user is stressed, the protection unit can prioritize stricter protection methods. For example, if the user is agitated, the protection unit will prioritize methods that protect information quickly. This allows for more appropriate information protection by determining the priority of information protection based on the user's emotions.

[0096] The protection unit can adjust the order of protection based on when the information was submitted. For example, the protection unit will prioritize the protection of the most recent information. It can also postpone the protection of older information. For instance, the protection unit will appropriately allocate protection resources according to the submission date. This allows for efficient information protection by adjusting the order of protection based on the submission date.

[0097] The protection unit can adjust the order of protection based on the relevance of the information during the protection process. For example, the protection unit can prioritize the protection of information related to the user's current areas of interest. It can also prioritize the protection of information related to the user's past output. For example, the protection unit can prioritize the protection of information related to the user's current activity status. This allows for efficient information protection by adjusting the order of protection based on the relevance of the information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and protection unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates generation results based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the generation results to the user. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and securely stores and appropriately utilizes the collected information. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and protection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and creates generation results based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides the generation results to the user. The protection unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and safely stores and appropriately utilizes the collected information. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and protection unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and creates generation results based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the headset terminal 314 and provides the generation results to the user. The protection unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and securely stores and appropriately utilizes the collected information. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and protection unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates generation results based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generation results to the user. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and safely stores and appropriately utilizes the collected information.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The generation system can also include a feedback unit. The feedback unit collects feedback provided by the user regarding the generated results and sends it to the analysis unit. For example, if the user rates the generated results as "satisfied" or "dissatisfied," the feedback unit collects that rating. The feedback unit can also collect information if the user provides specific comments or suggestions for improvement. This allows the analysis unit to adjust the generation algorithm based on the collected feedback, improving the accuracy of future generation results. Furthermore, the feedback unit can estimate the user's emotions and determine the importance of the feedback based on those emotions. For example, if the user expresses strong emotions, that feedback can be prioritized for analysis. This allows for effective utilization of user feedback and continuous improvement of the quality of the generated results.

[0100] The data collection unit can also collect the user's biometric data. For example, it can collect biometric data such as heart rate and skin electrical activity to estimate the user's stress level and relaxation state. Furthermore, the data collection unit can analyze the user's voice tone and facial expressions to estimate their emotional state. This allows the data collection unit to perform more accurate emotion estimation based on the user's biometric data. In addition, the data collection unit can transmit this biometric data to the analysis unit for use in customizing the generated results. For example, if the user is relaxed, the generated results can be provided in a calmer tone. This allows for the provision of more personalized generated results by utilizing the user's biometric data.

[0101] The analysis unit can analyze a user's social network information. For example, it can analyze the interests of a user's friends and followers to identify topics that the user is likely to be interested in. The analysis unit can also analyze a user's activity patterns on social media to identify their behavioral characteristics. This allows the analysis unit to provide more accurate results based on the user's social network information. Furthermore, the analysis unit can estimate a user's emotions and adjust the analysis method based on those emotions. For example, if a user is feeling stressed, it can perform a concise and to-the-point analysis. This allows the analysis unit to provide more personalized results by utilizing the user's social network information.

[0102] The generation unit can incorporate real-time user feedback on the generated results. For example, if a user comments or makes corrections in real time while reviewing the generated results, the generation unit can immediately reflect that feedback and update the results. The generation unit can also estimate the user's emotions and adjust the presentation of the generated results based on those emotions. For example, if the user is excited, a visually stimulating presentation method can be used. This allows the generation unit to leverage real-time user feedback to provide more satisfying generated results. Furthermore, the generation unit can refer to past user feedback and continuously improve the generation algorithm. This allows the generation unit to effectively utilize user feedback to improve the quality of the generated results.

[0103] The service provider can consider the user's device environment when providing the generated results. For example, if the user is using a smartphone, the generated results can be provided in a mobile-friendly format. If the user is using a desktop computer, the service provider can also provide generated results containing more detailed information. Furthermore, the service provider can adjust the amount of data in the generated results, taking into account the user's internet connection status. For example, if the user is using a slow internet connection, a lightweight generated result can be provided. This allows the service provider to deliver generated results in the optimal format according to the user's device environment and internet connection status.

[0104] The protection unit can include a function to anonymize user data. For example, it can anonymize collected personal information so that individuals cannot be identified during analysis or generation. The protection unit can also store user data in distributed storage to improve data security. Furthermore, the protection unit can record access logs to user data and include a function to detect unauthorized access. This ensures that user data is securely protected and privacy is ensured.

[0105] The data collection unit can analyze the user's voice commands and gather information. For example, if the user says "Tell me the latest news" by voice, the data collection unit will gather the latest news information based on that instruction. The data collection unit can also analyze the user's voice tone and speaking style to estimate their emotional state. This allows the data collection unit to utilize the user's voice commands for more intuitive information gathering. Furthermore, the data collection unit can determine the priority of information gathering based on the user's voice commands. For example, if the user gives an urgent instruction, that instruction can be processed with priority. This allows for more efficient information gathering by utilizing the user's voice commands.

[0106] The analysis unit can analyze trends in user generation results based on the user's past generation results. For example, it can identify particularly popular topics and formats in the user's past generation results and reflect them in future generation results. The analysis unit can also analyze patterns in the user's past generation results and identify changes in the user's interests and preferences. This allows the analysis unit to leverage the user's past generation results to provide more accurate generation results. Furthermore, the analysis unit can estimate the user's emotions and determine analysis priorities based on those emotions. For example, if the user is relaxed, it can prioritize the analysis of interesting information. This allows the analysis unit to leverage the user's past generation results to provide more personalized generation results.

[0107] The generation unit can reflect user feedback on the generated results in real time. For example, if a user comments that they "want more detailed information" regarding the generated results, the generation unit immediately reflects that feedback and updates the results. The generation unit can also estimate the user's emotions and adjust the way the generated results are presented based on those emotions. For example, if the user is feeling stressed, a concise and to-the-point expression can be used. This allows for effective use of user feedback to provide more satisfying generated results. Furthermore, the generation unit can refer to past user feedback and continuously improve the generation algorithm. This allows for effective use of user feedback to improve the quality of the generated results.

[0108] The service provider can consider the user's device environment when providing the generated results. For example, if the user is using a smartphone, the generated results can be provided in a mobile-friendly format. If the user is using a desktop computer, the service provider can also provide generated results containing more detailed information. Furthermore, the service provider can adjust the amount of data in the generated results, taking into account the user's internet connection status. For example, if the user is using a slow internet connection, a lightweight generated result can be provided. This allows the service provider to deliver generated results in the optimal format according to the user's device environment and internet connection status.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects user information. User information includes personal information, behavioral history, preferences, etc. The collection unit collects information such as the user's past output, preferences, interests, survey results, behavioral history, browsing history, and search history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses data analysis algorithms to generate data that reflects the user's individuality and identity, and analyzes personality traits and behavioral patterns. Step 3: The generation unit creates the generation results based on the information analyzed by the analysis unit. The generation unit uses generation AI, employing machine learning models and natural language generation technologies to create the generation results and provide results that align with the user's intent. It also selects the optimal generation method by referring to past generation results. Step 4: The delivery unit provides the user with the generation results created by the generation unit. The delivery unit can display the generation results in real time via a web application or mobile application, and can also send them via email. For example, the generation results can be provided in PDF format. Step 5: The protection unit securely stores the collected information and uses it appropriately for analysis and generation. The protection unit protects the information using data encryption and access control, ensuring that only specific users can access it.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that creates a generation result based on the information analyzed by the analysis unit, A providing unit that provides the user with the generation results created by the generation unit, It includes a protection unit that securely handles user information. A system characterized by the following features.

2. The system according to claim 1, characterized in that the collection unit collects information on the user's past generation results or preferences and interests.

3. The aforementioned analysis unit, The collected information is analyzed to generate data that reflects the user's individuality and identity. The system according to feature 1.

4. The generating unit is Using generative AI, we create generated results that align with the user's intent. The system according to feature 1.

5. The aforementioned supply unit is, The generation results created by the generation unit are provided to the user. The system according to feature 1.

6. The aforementioned protective part is The collected information will be securely stored and used appropriately for analysis and generation. The system according to feature 1.

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

8. The system according to claim 1, characterized in that the collection unit analyzes the user's past generation results and selects an appropriate information collection method.

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