System

Abstract

An object of the system according to the embodiment is to estimate and provide a name of a related person from specific information.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives information on a specific event, incident, relationship, occupation, or hobby. The analysis unit analyzes the information received by the reception unit and estimates a name of a related person. The providing unit provides the name estimated by the analyzing unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to recall the names of people associated with specific events, occurrences, relationships, and other information.

[0005] The system according to the embodiment aims to estimate and provide the name of a person associated with specific information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives information on specific events, occurrences, relationships, occupations, and hobbies. The analysis unit analyzes the information received by the reception unit and estimates the names of related people. The provision unit provides the names estimated by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can estimate and provide the name of a person associated with specific information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A name estimation system according to an embodiment of the present invention is a system that, when a user inputs information such as a specific event or occurrence, a relationship, an occupation, or a hobby, estimates the name of a related person based on the input information. In the name estimation system, a user inputs information such as a specific event or occurrence, a relationship, an occupation, or a hobby, and a generation AI analyzes the information and suggests the name of a related person. For example, when the user inputs information such as "a high school classmate who was a member of the soccer club," the generation AI analyzes the information and suggests the name of the corresponding person. This allows the system to derive a name from other elements even if the user cannot remember the name. This allows the system to derive a name from other elements even if the user cannot remember the name. For example, the system can estimate the name of a related person simply by inputting information such as a specific event or occurrence, a relationship, an occupation, or a hobby. This allows the system to derive a name from other elements even if the user cannot remember the name.

[0029] A name estimation system according to an embodiment includes a receiving unit, an analyzing unit, and a providing unit. The receiving unit receives information such as specific events, occurrences, relationships, occupations, and hobbies. For example, the receiving unit receives user input such as "a high school classmate who was a member of the soccer club." The receiving unit can also receive various input methods, such as voice input and text input. The analyzing unit uses a generation AI to analyze the information received by the receiving unit and estimate the name of a related person. For example, the analysis unit causes the generation AI to refer to a past database and estimate the name of a related person based on the input information. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and estimates a name based on the input information. The providing unit provides the name estimated by the analyzing unit to the user. For example, the providing unit displays the suggested name to the user so that the user can confirm it. The providing unit also allows the user to confirm the suggested name and, if necessary, enter corrections or additional information. This allows the name estimation system according to an embodiment to derive a name from other elements even if the user cannot remember the name. For example, if a user simply inputs information about a specific event or occurrence, relationship, occupation, hobbies, etc., the system can infer the name of a related person. This allows the system to derive the name from other elements even if the user cannot remember the name.

[0030] The analysis unit can refer to a past database and estimate the name of a related person based on the input information. Examples of past databases include, but are not limited to, past customer data and history data. For example, the analysis unit can refer to past customer data and estimate the name of a related person based on the input information. The analysis unit can also refer to past history data and estimate the name of a related person based on the input information. For example, the analysis unit analyzes information such as specific events, occurrences, relationships, occupations, and hobbies based on the past history data to estimate the name of a related person. This enables more accurate name estimation by referring to the past database. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input a past database into the generation AI and have the generation AI estimate the name of a related person.

[0031] The providing unit can provide the suggested name to the user. The providing unit, for example, displays the suggested name to the user so that the user can confirm it. For example, the providing unit can allow the user to confirm the suggested name and input corrections or additional information as necessary. The providing unit can also notify the user of the suggested name. For example, the providing unit notifies the user of the suggested name by email or push notification. By providing the suggested name to the user, it becomes easier for the user to confirm the name. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit can display the suggested name to the user using a generation AI so that the user can confirm it.

[0032] The reception unit can accept information on specific events, occurrences, relationships, occupations, and hobbies. For example, the reception unit accepts user input of information such as "a high school classmate who was a member of the soccer club." The reception unit can also accept various input methods, such as voice input and text input. For example, the reception unit can accept user input of information using voice input. The reception unit can also accept user input of information using text input. The reception unit can also accept user input of information using image input. For example, the reception unit can allow a user to upload an image and accept information based on the image. This makes it easier to estimate the name of a related person by accepting specific information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the information entered by the user using a generation AI and provide information for estimating the name of a related person.

[0033] The analysis unit can analyze information in real time and estimate the names of related persons. "Real time" refers to, for example, a delay time of a few seconds or less, but is not limited to such an example. The analysis unit can, for example, analyze information in real time and estimate the names of related persons. For example, the analysis unit can analyze information entered by a user in real time and immediately estimate the names of related persons. The analysis unit also has high-speed processing capabilities for analyzing information in real time. For example, the analysis unit can analyze information in real time using a high-speed processor and a large-capacity memory. This allows for rapid name estimation by analyzing information in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze information in real time using a generation AI and estimate the names of related persons.

[0034] The providing unit can allow the user to review the proposed name and input corrections or additional information as necessary. For example, the providing unit can allow the user to review the proposed name and input corrections or additional information as necessary. For example, the providing unit can allow the user to review the proposed name and, if necessary, provide an input field for correction. The providing unit can also provide an interface for the user to input additional information. For example, the providing unit can provide a text box or drop-down menu for the user to input additional information. This allows the user to review the proposed name and input corrections or additional information, thereby enabling more accurate name identification. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can use a generation AI to provide an interface for the user to review the proposed name and input corrections or additional information.

[0035] The reception unit can analyze the user's past input history and select the optimal information reception method. For example, the reception unit automatically displays information that the user frequently input in the past as a candidate. For example, the reception unit automatically displays related information as a candidate based on information the user input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest information to be used in a specific time period based on the user's past input history. For example, the reception unit suggests information related to a similar time period based on information the user input in a specific time period in the past. In this way, the optimal information reception method can be selected by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's past input history using a generation AI and select the optimal information reception method.

[0036] When receiving information, the reception unit can filter the information based on the user's current situation and areas of interest. For example, the reception unit preferentially receives information related to an event in which the user is currently participating. For example, the reception unit filters and receives information related to the event in which the user is currently participating. The reception unit can also filter and receive related information based on the user's areas of interest. For example, the reception unit preferentially receives related information based on the user's areas of interest. The reception unit can also receive appropriate information depending on the user's current situation (e.g., at work, on vacation, etc.). For example, when the user is at work, the reception unit preferentially receives information related to work. In this way, by filtering information based on the user's current situation and areas of interest, highly relevant information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's current situation and areas of interest using a generation AI, and filter and receive related information.

[0037] When accepting information, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs information by voice, the acceptance unit accepts the information using voice recognition technology. For example, when the user inputs information by voice, the acceptance unit converts the voice into text using voice recognition technology and accepts the information. Furthermore, when the user inputs information by text, the acceptance unit can also accept the information using text analysis technology. For example, when the user inputs information by text, the acceptance unit can analyze the input text using text analysis technology and accept the information. Furthermore, when the user inputs information by image, the acceptance unit can also accept the information using image recognition technology. For example, the acceptance unit allows the user to upload an image and accepts information based on the image. This allows the acceptance of information to be made more efficient by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can analyze the user's input method using a generation AI and select the optimal acceptance means.

[0038] When accepting information, the reception unit can prioritize accepting highly relevant information in consideration of the user's geographical location information. The reception unit, for example, prioritizes accepting information related to the user's current location. For example, the reception unit filters and accepts information related to the user's current location. The reception unit can also prioritize accepting information related to places the user has visited in the past. For example, the reception unit filters and accepts information related to places the user has visited in the past. The reception unit can also prioritize accepting information related to places the user plans to visit in the future. For example, the reception unit filters and accepts information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's geographical location information using a generation AI and filter and accept related information.

[0039] When receiving information, the reception unit can analyze the user's social media activity and receive related information. The reception unit, for example, receives information related to places where the user has checked in on social media. For example, the reception unit filters and receives information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. For example, the reception unit analyzes the content of the user's social media posts and filters and receives related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. For example, the reception unit filters and receives related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's social media activity using a generation AI and filter and receive related information.

[0040] When accepting information, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. The acceptance unit, for example, suggests an optimal acceptance method based on feedback provided by the user in the past. For example, the acceptance unit filters and accepts related information based on feedback provided by the user in the past. The acceptance unit can also preferentially accept specific information from the user's past feedback. For example, the acceptance unit preferentially accepts specific information based on the user's past feedback. The acceptance unit can also analyze the user's past feedback and improve the acceptance method. For example, the acceptance unit analyzes the user's past feedback and improves the acceptance method. In this way, the acceptance method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can analyze the user's past feedback using a generation AI and customize the acceptance method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit performs a concise analysis on general information. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance specified by the user. In this way, by adjusting the level of detail of the analysis based on the importance of the information, it is possible to perform a detailed analysis of important information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the importance of information using a generation AI and adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an event analysis algorithm to event information. For example, the analysis unit applies an event analysis algorithm to event information. The analysis unit can also apply an occupation analysis algorithm to occupation information. For example, the analysis unit applies an occupation analysis algorithm to occupation information. The analysis unit can also apply a hobby analysis algorithm to hobby information. For example, the analysis unit applies a hobby analysis algorithm to hobby information. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the category of information using a generation AI and apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. For example, the analysis unit can extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results to improve the analysis method. For example, the analysis unit analyzes the user's past analysis results to improve the analysis method. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's past analysis results using a generation AI to improve the analysis accuracy.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also determine the priority of analysis based on the submission time specified by the user. For example, the analysis unit determines the priority of analysis based on the submission time specified by the user. The analysis unit can also postpone analysis of information that has been submitted earlier. For example, the analysis unit postpones analysis of information that has been submitted earlier. In this way, by determining the priority of analysis based on the time of submission of information, the most recent information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the time of submission of information using a generation AI and determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. For example, the analysis unit adjusts the order of analysis based on the relevance specified by the user. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit postpones analysis of less relevant information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the relevance of information using a generation AI and adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. The analysis unit can also provide analysis results in simple language if the user does not have technical expertise. For example, if the user does not have technical expertise, the analysis unit provides analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the analysis.

[0047] The providing unit can adjust the level of detail to be provided based on the importance of the name when providing the information. For example, the providing unit provides detailed information for an important name. For example, the providing unit provides detailed information for an important name. The providing unit can also provide concise information for a common name. For example, the providing unit provides concise information for a common name. The providing unit can also adjust the level of detail to be provided based on the importance specified by the user. For example, the providing unit adjusts the level of detail to be provided based on the importance specified by the user. In this way, detailed information can be provided for important names by adjusting the level of detail to be provided based on the importance of the name. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the importance of a name using a generation AI and adjust the level of detail to be provided.

[0048] The providing unit can apply different providing algorithms depending on the category of the name when providing the information. For example, the providing unit applies an event providing algorithm to a name related to an event. For example, the providing unit applies an event providing algorithm to a name related to an event. The providing unit can also apply an occupation providing algorithm to a name related to an occupation. For example, the providing unit applies an occupation providing algorithm to a name related to an occupation. The providing unit can also apply a hobby providing algorithm to a name related to a hobby. For example, the providing unit applies a hobby providing algorithm to a name related to a hobby. This enables more appropriate information to be provided by applying different providing algorithms depending on the category of the name. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the category of the name using a generation AI and apply an appropriate providing algorithm.

[0049] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. For example, the providing unit adjusts the provision algorithm based on the user's past provision results. The providing unit can also extract a specific pattern from the user's past provision results to improve the provision accuracy. For example, the providing unit can extract a specific pattern from the user's past provision results to improve the provision accuracy. The providing unit can also analyze the user's past provision results to improve the provision method. For example, the providing unit can analyze the user's past provision results to improve the provision method. In this way, the accuracy of the provision can be improved by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the user's past provision results using a generation AI to improve the accuracy of the provision.

[0050] The providing unit can determine the priority of provision based on the time of submission of the names when the names are provided. The providing unit, for example, prioritizes providing the most recent names. For example, the providing unit prioritizes providing the most recent names. The providing unit can also determine the priority of provision based on the time of submission specified by the user. For example, the providing unit determines the priority of provision based on the time of submission specified by the user. The providing unit can also provide names that were submitted earlier at a later date. For example, the providing unit provides names that were submitted earlier at a later date. In this way, by determining the priority of provision based on the time of submission of the names, the most recent names can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the time of submission of names using a generation AI and determine the priority of provision.

[0051] The providing unit can adjust the order of providing names based on the relevance of the names when providing them. The providing unit, for example, provides highly relevant names with priority. For example, the providing unit provides highly relevant names with priority. The providing unit can also adjust the order of providing names based on the relevance specified by the user. For example, the providing unit adjusts the order of providing names based on the relevance specified by the user. The providing unit can also provide less relevant names at a later date. For example, the providing unit provides less relevant names at a later date. In this way, by adjusting the order of providing names based on the relevance of the names, it is possible to provide more relevant names with priority. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the relevance of names using a generation AI and adjust the order of providing names.

[0052] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terminology. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terminology. The providing unit can also provide information in simple language if the user does not have technical expertise. For example, if the user does not have technical expertise, the providing unit provides information in simple language. The providing unit can also adjust the way information is expressed according to the user's level of expertise. For example, the providing unit adjusts the way information is expressed according to the user's level of expertise. This makes it possible to provide information that is easier to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terminology in the provided information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can refer to the user's past search history and estimate names with greater accuracy based on previously searched names and related information. For example, it can analyze the patterns of names the user has searched for in the past and prioritize names that share similar patterns. It can also extract names related to specific events or occurrences from the past search history and reflect them in the current search. This makes it possible to estimate names that take the user's past behavior into account.

[0055] The reception unit can preferentially receive highly relevant information by taking into account the user's geographical location information. For example, information related to the user's current location can be filtered and received. Information related to places the user has visited in the past can also be preferentially received. In this way, highly relevant information can be preferentially received by taking into account the user's geographical location information.

[0056] The analysis unit can analyze information in real time and infer the names of related people. For example, it can analyze information entered by a user in real time and immediately infer the names of related people. It also has high-speed processing capabilities for analyzing information in real time, allowing it to quickly infer names.

[0057] The reception unit can analyze the user's social media activity and receive related information. For example, it can filter and receive information related to places where the user has checked in on social media. It can also analyze the content of the user's posts on social media and receive related information. In this way, it is possible to receive related information by analyzing the user's social media activity.

[0058] The providing unit can improve the accuracy of the provision by referring to the user's past provision results. For example, the providing algorithm can be adjusted based on the user's past provision results. In addition, it can also extract specific patterns from the user's past provision results to improve the provision accuracy. In this way, the accuracy of the provision can be improved by referring to the past provision results.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives information such as specific events, occurrences, relationships, occupations, and hobbies. For example, the reception unit receives information such as "a high school classmate who was a member of the soccer club." The reception unit can also receive input via various methods, such as voice input and text input. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and estimate the name of the associated person. For example, the analysis unit uses the generation AI to refer to a past database and estimate the name of the associated person based on the input information. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and estimates the name based on the input information. Step 3: The providing unit provides the name estimated by the analyzing unit to the user. For example, the providing unit displays the suggested name to the user so that the user can confirm it. The providing unit also allows the user to confirm the suggested name and input corrections or additional information as necessary.

[0061] (Example 2) A name estimation system according to an embodiment of the present invention is a system that, when a user inputs information such as a specific event or occurrence, a relationship, an occupation, or a hobby, estimates the name of a related person based on the input information. In the name estimation system, a user inputs information such as a specific event or occurrence, a relationship, an occupation, or a hobby, and a generation AI analyzes the information and suggests the name of a related person. For example, when the user inputs information such as "a high school classmate who was a member of the soccer club," the generation AI analyzes the information and suggests the name of the corresponding person. This allows the system to derive a name from other elements even if the user cannot remember the name. This allows the system to derive a name from other elements even if the user cannot remember the name. For example, the system can estimate the name of a related person simply by inputting information such as a specific event or occurrence, a relationship, an occupation, or a hobby. This allows the system to derive a name from other elements even if the user cannot remember the name.

[0062] A name estimation system according to an embodiment includes a receiving unit, an analyzing unit, and a providing unit. The receiving unit receives information such as specific events, occurrences, relationships, occupations, and hobbies. For example, the receiving unit receives user input such as "a high school classmate who was a member of the soccer club." The receiving unit can also receive various input methods, such as voice input and text input. The analyzing unit uses a generation AI to analyze the information received by the receiving unit and estimate the name of a related person. For example, the analysis unit causes the generation AI to refer to a past database and estimate the name of a related person based on the input information. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and estimates a name based on the input information. The providing unit provides the name estimated by the analyzing unit to the user. For example, the providing unit displays the suggested name to the user so that the user can confirm it. The providing unit also allows the user to confirm the suggested name and, if necessary, enter corrections or additional information. This allows the name estimation system according to an embodiment to derive a name from other elements even if the user cannot remember the name. For example, if a user simply inputs information about a specific event or occurrence, relationship, occupation, hobbies, etc., the system can infer the name of a related person. This allows the system to derive the name from other elements even if the user cannot remember the name.

[0063] The analysis unit can refer to a past database and estimate the name of a related person based on the input information. Examples of past databases include, but are not limited to, past customer data and history data. For example, the analysis unit can refer to past customer data and estimate the name of a related person based on the input information. The analysis unit can also refer to past history data and estimate the name of a related person based on the input information. For example, the analysis unit analyzes information such as specific events, occurrences, relationships, occupations, and hobbies based on the past history data to estimate the name of a related person. This enables more accurate name estimation by referring to the past database. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input a past database into the generation AI and have the generation AI estimate the name of a related person.

[0064] The providing unit can provide the suggested name to the user. The providing unit, for example, displays the suggested name to the user so that the user can confirm it. For example, the providing unit can allow the user to confirm the suggested name and input corrections or additional information as necessary. The providing unit can also notify the user of the suggested name. For example, the providing unit notifies the user of the suggested name by email or push notification. By providing the suggested name to the user, it becomes easier for the user to confirm the name. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the providing unit can display the suggested name to the user using a generation AI so that the user can confirm it.

[0065] The reception unit can accept information on specific events, occurrences, relationships, occupations, and hobbies. For example, the reception unit accepts user input of information such as "a high school classmate who was a member of the soccer club." The reception unit can also accept various input methods, such as voice input and text input. For example, the reception unit can accept user input of information using voice input. The reception unit can also accept user input of information using text input. The reception unit can also accept user input of information using image input. For example, the reception unit can allow a user to upload an image and accept information based on the image. This makes it easier to estimate the name of a related person by accepting specific information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the information entered by the user using a generation AI and provide information for estimating the name of a related person.

[0066] The analysis unit can analyze information in real time and estimate the names of related persons. "Real time" refers to, for example, a delay time of a few seconds or less, but is not limited to such an example. The analysis unit can, for example, analyze information in real time and estimate the names of related persons. For example, the analysis unit can analyze information entered by a user in real time and immediately estimate the names of related persons. The analysis unit also has high-speed processing capabilities for analyzing information in real time. For example, the analysis unit can analyze information in real time using a high-speed processor and a large-capacity memory. This allows for rapid name estimation by analyzing information in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze information in real time using a generation AI and estimate the names of related persons.

[0067] The providing unit can allow the user to review the proposed name and input corrections or additional information as necessary. For example, the providing unit can allow the user to review the proposed name and input corrections or additional information as necessary. For example, the providing unit can allow the user to review the proposed name and, if necessary, provide an input field for correction. The providing unit can also provide an interface for the user to input additional information. For example, the providing unit can provide a text box or drop-down menu for the user to input additional information. This allows the user to review the proposed name and input corrections or additional information, thereby enabling more accurate name identification. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can use a generation AI to provide an interface for the user to review the proposed name and input corrections or additional information.

[0068] The reception unit can estimate the user's emotions and adjust the information reception method based on the estimated user emotions. For example, if the user is anxious, the reception unit provides a simple and quick input interface. For example, if the user is anxious, the reception unit can display a minimal number of input fields to allow the user to quickly input information. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, if the user is relaxed, the reception unit can display multiple input options to allow the user to select one. Furthermore, if the user is confused, the reception unit can provide guided input procedures to allow the user to input information step by step. For example, if the user is confused, the reception unit can display an explanation for each step to allow the user to input information without hesitation. This allows the information reception method to be adjusted according to the user's emotions, thereby enabling more appropriate information reception. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may estimate the user's emotion using the generation AI and adjust the method of receiving information based on the estimated emotion.

[0069] The reception unit can analyze the user's past input history and select the optimal information reception method. For example, the reception unit automatically displays information that the user frequently input in the past as a candidate. For example, the reception unit automatically displays related information as a candidate based on information the user input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest information to be used in a specific time period based on the user's past input history. For example, the reception unit suggests information related to a similar time period based on information the user input in a specific time period in the past. In this way, the optimal information reception method can be selected by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's past input history using a generation AI and select the optimal information reception method.

[0070] When receiving information, the reception unit can filter the information based on the user's current situation and areas of interest. For example, the reception unit preferentially receives information related to an event in which the user is currently participating. For example, the reception unit filters and receives information related to the event in which the user is currently participating. The reception unit can also filter and receive related information based on the user's areas of interest. For example, the reception unit preferentially receives related information based on the user's areas of interest. The reception unit can also receive appropriate information depending on the user's current situation (e.g., at work, on vacation, etc.). For example, when the user is at work, the reception unit preferentially receives information related to work. In this way, by filtering information based on the user's current situation and areas of interest, highly relevant information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's current situation and areas of interest using a generation AI, and filter and receive related information.

[0071] When accepting information, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs information by voice, the acceptance unit accepts the information using voice recognition technology. For example, when the user inputs information by voice, the acceptance unit converts the voice into text using voice recognition technology and accepts the information. Furthermore, when the user inputs information by text, the acceptance unit can also accept the information using text analysis technology. For example, when the user inputs information by text, the acceptance unit can analyze the input text using text analysis technology and accept the information. Furthermore, when the user inputs information by image, the acceptance unit can also accept the information using image recognition technology. For example, the acceptance unit allows the user to upload an image and accepts information based on the image. This allows the acceptance of information to be made more efficient by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can analyze the user's input method using a generation AI and select the optimal acceptance means.

[0072] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user's emotions. For example, when the user is impatient, the reception unit prioritizes receiving important information. For example, when the user is impatient, the reception unit prioritizes receiving information of high importance. The reception unit can also prioritize receiving detailed information when the user is relaxed. For example, when the user is relaxed, the reception unit prioritizes receiving detailed information. The reception unit can also prioritize receiving simple information when the user is confused. For example, when the user is confused, the reception unit prioritizes receiving simple and easy-to-understand information. In this way, by determining the priority of information according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit can use a generation AI to estimate the user's emotions and determine the priority of information based on the estimated emotions.

[0073] When accepting information, the reception unit can prioritize accepting highly relevant information in consideration of the user's geographical location information. The reception unit, for example, prioritizes accepting information related to the user's current location. For example, the reception unit filters and accepts information related to the user's current location. The reception unit can also prioritize accepting information related to places the user has visited in the past. For example, the reception unit filters and accepts information related to places the user has visited in the past. The reception unit can also prioritize accepting information related to places the user plans to visit in the future. For example, the reception unit filters and accepts information related to places the user plans to visit in the future. In this way, highly relevant information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's geographical location information using a generation AI and filter and accept related information.

[0074] When receiving information, the reception unit can analyze the user's social media activity and receive related information. The reception unit, for example, receives information related to places where the user has checked in on social media. For example, the reception unit filters and receives information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related information. For example, the reception unit analyzes the content of the user's social media posts and filters and receives related information. The reception unit can also receive related information by referring to the activities of the user's friends on social media. For example, the reception unit filters and receives related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's social media activity using a generation AI and filter and receive related information.

[0075] When accepting information, the acceptance unit can customize the acceptance method by reflecting the user's past feedback. The acceptance unit, for example, suggests an optimal acceptance method based on feedback provided by the user in the past. For example, the acceptance unit filters and accepts related information based on feedback provided by the user in the past. The acceptance unit can also preferentially accept specific information from the user's past feedback. For example, the acceptance unit preferentially accepts specific information based on the user's past feedback. The acceptance unit can also analyze the user's past feedback and improve the acceptance method. For example, the acceptance unit analyzes the user's past feedback and improves the acceptance method. In this way, the acceptance method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can analyze the user's past feedback using a generation AI and customize the acceptance method.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. Furthermore, if the user is confused, the analysis unit can provide a visually easy-to-understand analysis result. For example, if the user is confused, the analysis unit provides a visually easy-to-understand analysis result. This allows for adjusting the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can use a generative AI to estimate the user's emotions and adjust the way the analysis is expressed based on the estimated emotions.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit performs a concise analysis on general information. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance specified by the user. In this way, by adjusting the level of detail of the analysis based on the importance of the information, it is possible to perform a detailed analysis of important information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the importance of information using a generation AI and adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies an event analysis algorithm to event information. For example, the analysis unit applies an event analysis algorithm to event information. The analysis unit can also apply an occupation analysis algorithm to occupation information. For example, the analysis unit applies an occupation analysis algorithm to occupation information. The analysis unit can also apply a hobby analysis algorithm to hobby information. For example, the analysis unit applies a hobby analysis algorithm to hobby information. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the category of information using a generation AI and apply an appropriate analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. For example, the analysis unit can extract specific patterns from the user's past analysis results to improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results to improve the analysis method. For example, the analysis unit analyzes the user's past analysis results to improve the analysis method. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's past analysis results using a generation AI to improve the analysis accuracy.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually easy-to-understand analysis result if the user is confused. For example, if the user is confused, the analysis unit provides a visually easy-to-understand analysis result. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can use a generation AI to estimate the user's emotions and adjust the length of the analysis based on the estimated emotions.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also determine the priority of analysis based on the submission time specified by the user. For example, the analysis unit determines the priority of analysis based on the submission time specified by the user. The analysis unit can also postpone analysis of information that has been submitted earlier. For example, the analysis unit postpones analysis of information that has been submitted earlier. In this way, by determining the priority of analysis based on the time of submission of information, the most recent information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the time of submission of information using a generation AI and determine the priority of analysis.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. For example, the analysis unit adjusts the order of analysis based on the relevance specified by the user. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit postpones analysis of less relevant information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the relevance of information using a generation AI and adjust the order of analysis.

[0083] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. The analysis unit can also provide analysis results in simple language if the user does not have technical expertise. For example, if the user does not have technical expertise, the analysis unit provides analysis results in simple language. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terms in the analysis.

[0084] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, when the user is relaxed, the providing unit provides detailed information. For example, when the user is relaxed, the providing unit provides detailed information. Furthermore, when the user is in a hurry, the providing unit can provide concise information. For example, when the user is in a hurry, the providing unit provides concise information. Furthermore, when the user is confused, the providing unit can provide visually easy-to-understand information. For example, when the user is confused, the providing unit provides visually easy-to-understand information. This enables more appropriate information to be provided by adjusting the method of providing information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can estimate the user's emotions using the generation AI and adjust the method of providing information based on the estimated emotions.

[0085] The providing unit can adjust the level of detail to be provided based on the importance of the name when providing the information. For example, the providing unit provides detailed information for an important name. For example, the providing unit provides detailed information for an important name. The providing unit can also provide concise information for a common name. For example, the providing unit provides concise information for a common name. The providing unit can also adjust the level of detail to be provided based on the importance specified by the user. For example, the providing unit adjusts the level of detail to be provided based on the importance specified by the user. In this way, detailed information can be provided for important names by adjusting the level of detail to be provided based on the importance of the name. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the importance of a name using a generation AI and adjust the level of detail to be provided.

[0086] The providing unit can apply different providing algorithms depending on the category of the name when providing the information. For example, the providing unit applies an event providing algorithm to a name related to an event. For example, the providing unit applies an event providing algorithm to a name related to an event. The providing unit can also apply an occupation providing algorithm to a name related to an occupation. For example, the providing unit applies an occupation providing algorithm to a name related to an occupation. The providing unit can also apply a hobby providing algorithm to a name related to a hobby. For example, the providing unit applies a hobby providing algorithm to a name related to a hobby. This enables more appropriate information to be provided by applying different providing algorithms depending on the category of the name. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the category of the name using a generation AI and apply an appropriate providing algorithm.

[0087] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. For example, the providing unit adjusts the provision algorithm based on the user's past provision results. The providing unit can also extract a specific pattern from the user's past provision results to improve the provision accuracy. For example, the providing unit can extract a specific pattern from the user's past provision results to improve the provision accuracy. The providing unit can also analyze the user's past provision results to improve the provision method. For example, the providing unit can analyze the user's past provision results to improve the provision method. In this way, the accuracy of the provision can be improved by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the user's past provision results using a generation AI to improve the accuracy of the provision.

[0088] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated user's emotions. For example, when the user is in a hurry, the providing unit provides short, concise information. For example, when the user is in a hurry, the providing unit provides short, concise information. The providing unit can also provide detailed information when the user is relaxed. For example, when the user is relaxed, the providing unit provides detailed information. The providing unit can also provide visually easy-to-understand information when the user is confused. For example, when the user is confused, the providing unit provides visually easy-to-understand information. This allows for adjusting the length of the information provided according to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can use a generation AI to estimate the user's emotions and adjust the length of the presentation based on the estimated emotions.

[0089] The providing unit can determine the priority of provision based on the time of submission of the names when the names are provided. The providing unit, for example, prioritizes providing the most recent names. For example, the providing unit prioritizes providing the most recent names. The providing unit can also determine the priority of provision based on the time of submission specified by the user. For example, the providing unit determines the priority of provision based on the time of submission specified by the user. The providing unit can also provide names that were submitted earlier at a later date. For example, the providing unit provides names that were submitted earlier at a later date. In this way, by determining the priority of provision based on the time of submission of the names, the most recent names can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the time of submission of names using a generation AI and determine the priority of provision.

[0090] The providing unit can adjust the order of providing names based on the relevance of the names when providing them. The providing unit, for example, provides highly relevant names with priority. For example, the providing unit provides highly relevant names with priority. The providing unit can also adjust the order of providing names based on the relevance specified by the user. For example, the providing unit adjusts the order of providing names based on the relevance specified by the user. The providing unit can also provide less relevant names at a later date. For example, the providing unit provides less relevant names at a later date. In this way, by adjusting the order of providing names based on the relevance of the names, it is possible to provide more relevant names with priority. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the relevance of names using a generation AI and adjust the order of providing names.

[0091] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terminology. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terminology. The providing unit can also provide information in simple language if the user does not have technical expertise. For example, if the user does not have technical expertise, the providing unit provides information in simple language. The providing unit can also adjust the way information is expressed according to the user's level of expertise. For example, the providing unit adjusts the way information is expressed according to the user's level of expertise. This makes it possible to provide information that is easier to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the user's level of expertise using a generation AI and adjust the use of technical terminology in the provided information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives user input of information such as specific events, occurrences, relationships, occupations, and hobbies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to estimate the name of a related person. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the user with the name estimated by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives user input of information such as specific events, occurrences, relationships, occupations, and hobbies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to estimate the name of a related person. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with the name estimated by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives user input of information such as specific events, occurrences, relationships, occupations, and hobbies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to estimate the name of a related person. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides the user with the name estimated by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives user input of information such as specific events or occurrences, relationships, occupations, and hobbies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit using a generation AI to estimate the name of a related person. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with the name estimated by the analysis unit.

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

[0093] The name estimation system may further include an emotion estimation unit that estimates the user's emotion and adjusts the name suggestion method based on the estimated emotion. For example, if the user is anxious, the system can quickly suggest a name, while if the user is relaxed, it can provide detailed information. Also, if the user is confused, the system can suggest a name in a visually easy-to-understand format. This allows for appropriate name suggestions based on the user's emotion.

[0094] The analysis unit can refer to the user's past search history and estimate names with greater accuracy based on previously searched names and related information. For example, it can analyze the patterns of names the user has searched for in the past and prioritize names that share similar patterns. It can also extract names related to specific events or occurrences from the past search history and reflect them in the current search. This makes it possible to estimate names that take the user's past behavior into account.

[0095] The providing unit can estimate the user's emotions and adjust the name providing method based on the estimated emotions. For example, if the user is relaxed, detailed information can be provided, and if the user is in a hurry, brief information can be provided. Also, if the user is confused, visually easy-to-understand information can be provided. This makes it possible to provide appropriate information according to the user's emotions.

[0096] The reception unit can preferentially receive highly relevant information by taking into account the user's geographical location information. For example, information related to the user's current location can be filtered and received. Information related to places the user has visited in the past can also be preferentially received. In this way, highly relevant information can be preferentially received by taking into account the user's geographical location information.

[0097] The analysis unit can analyze information in real time and infer the names of related people. For example, it can analyze information entered by a user in real time and immediately infer the names of related people. It also has high-speed processing capabilities for analyzing information in real time, allowing it to quickly infer names.

[0098] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is in a hurry, short, to-the-point information can be provided, and if the user is relaxed, detailed information can be provided. Also, if the user is confused, visually easy-to-understand information can be provided. This makes it possible to provide appropriate information according to the user's emotions.

[0099] The reception unit can analyze the user's social media activity and receive related information. For example, it can filter and receive information related to places where the user has checked in on social media. It can also analyze the content of the user's posts on social media and receive related information. In this way, it is possible to receive related information by analyzing the user's social media activity.

[0100] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided, and if the user is in a hurry, concise analysis results can be provided. Also, if the user is confused, analysis results that are easy to understand visually can be provided. This makes it possible to provide appropriate analysis results according to the user's emotions.

[0101] The providing unit can improve the accuracy of the provision by referring to the user's past provision results. For example, the providing algorithm can be adjusted based on the user's past provision results. In addition, it can also extract specific patterns from the user's past provision results to improve the provision accuracy. In this way, the accuracy of the provision can be improved by referring to the past provision results.

[0102] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated emotions. For example, if the user is relaxed, detailed information can be provided, and if the user is in a hurry, brief information can be provided. Also, if the user is confused, visually easy-to-understand information can be provided. This makes it possible to provide appropriate information according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives information such as specific events, occurrences, relationships, occupations, and hobbies. For example, the reception unit receives information such as "a high school classmate who was a member of the soccer club." The reception unit can also receive input via various methods, such as voice input and text input. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and estimate the name of the associated person. For example, the analysis unit uses the generation AI to refer to a past database and estimate the name of the associated person based on the input information. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and estimates the name based on the input information. Step 3: The providing unit provides the name estimated by the analyzing unit to the user. For example, the providing unit displays the suggested name to the user so that the user can confirm it. The providing unit also allows the user to confirm the suggested name and input corrections or additional information as necessary.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about specific events, occurrences, relationships, occupations, and hobbies; an analysis unit that analyzes the information received by the reception unit and estimates the name of a related person; a providing unit that provides the name estimated by the analyzing unit to the user. A system characterized by:

2. The analysis unit Look up historical databases and guess the names of people involved based on the information you enter 2. The system of claim 1.

3. The providing unit Providing users with suggested names 2. The system of claim 1.

4. The reception unit Accepts information about specific events, occurrences, relationships, occupations, and hobbies 2. The system of claim 1.

5. The analysis unit Analyze information in real time and guess the names of related people 2. The system of claim 1.

6. The providing unit The user can review the proposed name and enter corrections or additional information if necessary.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the way information is received based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the optimal method of receiving information 2. The system of claim 1.

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