System

Abstract

An object of a system according to an embodiment is to grasp the nature of a partner and to quantify the potential of the partner.SOLUTION: A system includes a data collection part, an analysis part, and a digitization part. The data collection part collects action data or utterance contents of the other party. The analysis unit analyzes the data collected by the data collection unit and analyzes the characteristics of the other party. The digitizing unit digitizes the ability of the opponent based on the analysis result obtained by the analyzing unit.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 had the problem of being difficult to accurately understand the other person's true nature and potential, and was heavily subjective.

[0005] The system according to the embodiment aims to see through the true nature of the other person and quantify their potential. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a quantification unit. The data collection unit collects behavioral data or speech content of the other party. The analysis unit analyzes the data collected by the data collection unit to analyze the other party's characteristics. The quantification unit quantifies the other party's abilities based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can see through the true nature of the other person and quantify their potential. [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 system according to an embodiment of the present invention collects behavioral data and speech content from other people, and a generation AI analyzes this information to discern the other person's true nature and quantify their potential. This system collects behavioral data and speech content from other people, and a generation AI analyzes this information to discern the other person's true nature and quantify the other person's potential based on the analysis results. For example, the system collects behavioral data and speech content from other people. For example, data such as what the other person said in a meeting and what attitude they showed is collected. Next, the generation AI analyzes the collected data. The generation AI analyzes the collected data to discern the other person's true nature. For example, it can analyze a person's personality and values ​​from the content of their speech and behavioral patterns. Furthermore, it quantifies the other person's potential based on the analysis results. The generation AI quantifies the other person's potential based on the analysis results and displays it as a specific numerical value. For example, it can quantify leadership potential and communication ability potential. This makes it possible to understand the other person's true nature and grasp their potential. This allows the system to accurately understand the other person's true nature and quantify that potential. For example, by utilizing generative AI during job interviews and personnel evaluations, it is possible to accurately understand the other person's true nature and quantify their potential, which will enable more appropriate personnel management and interpersonal relationship building.

[0029] The system according to the embodiment includes a data collection unit, an analysis unit, and a quantification unit. The data collection unit collects behavioral data or speech content of the other party. The behavioral data of the other party includes, but is not limited to, movement history, click history, and purchase history. The data collection unit can collect detailed data, such as what the other party said in a meeting and what attitude they showed. The data collection unit can also collect the other party's speech content as text data using speech recognition technology. For example, the data collection unit records speech during a meeting and converts it into text data using speech recognition technology. The analysis unit uses a generation AI to analyze the data collected by the data collection unit and analyze the other party's characteristics. Examples of characteristics include, but are not limited to, personality traits and behavioral traits. For example, the analysis unit analyzes the other party's personality and values ​​from the other party's speech content and behavioral patterns. The generation AI can analyze the other party's characteristics using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the other party's speech content to discern their personality traits. The quantification unit quantifies the abilities of the other party based on the analysis results obtained by the analysis unit. Examples of abilities include, but are not limited to, leadership potential and communication ability potential. For example, the quantification unit quantifies the leadership potential of the other party based on the analysis results. The quantification unit can also quantify the communication ability potential of the other party based on the analysis results. This allows the system according to the embodiment to accurately understand the essence of the other party and quantify that potential. Some or all of the above-described processing in the quantification unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the quantification unit may perform quantification using a generative AI model that receives the analysis results obtained by the analysis unit as input and outputs a quantified result.

[0030] The data collection unit can analyze the other party's past behavioral data or utterance content and select an appropriate collection method. For example, the data collection unit can analyze utterance content from past meetings and focus on collecting utterances made in similar situations. The data collection unit can also collect behavioral data at specific time periods based on past behavioral patterns. For example, the data collection unit analyzes past behavioral data and collects behavioral data at specific time periods. The data collection unit can also prioritize collecting utterances on specific topics from past utterance content. For example, the data collection unit analyzes past utterance content and prioritizes collecting utterances on specific topics. This allows for efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-described processing in the data collection unit can be performed using, or without, a generation AI. For example, the data collection unit can input past behavioral data and utterance content into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting behavioral data or comment content, the data collection unit can filter the data based on the other party's current project or area of ​​interest. For example, the data collection unit prioritizes collecting comment content related to the other party's current project. The data collection unit can also filter and collect related behavioral data based on the other party's area of ​​interest. For example, the data collection unit filters and collects related behavioral data based on the other party's area of ​​interest. The data collection unit can also focus on collecting comment content related to topics in which the other party is interested. For example, the data collection unit focuses on collecting comment content related to topics in which the other party is interested. This allows for highly relevant data to be collected by filtering data based on the other party's area of ​​interest. Some or all of the above-mentioned processing in the data collection unit can be performed using, or without, the generation AI. For example, the data collection unit can input data related to the other party's current project or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0032] When collecting behavioral data or speech content, the data collection unit can select an appropriate collection means depending on the input method of the other party. For example, the data collection unit automatically converts the speech of the other party into text and collects it. The data collection unit can also collect the text input by the other party as is. For example, the data collection unit collects the text input by the other party as is. The data collection unit can also analyze the explanation given by the other party using images and collect related data. For example, the data collection unit analyzes the explanation given by the other party using images and collects related data. This makes it possible to collect data using the optimal means depending on the input method of the other party. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data collection unit can input data depending on the input method of the other party into the generation AI and have the generation AI select the optimal collection means.

[0033] When collecting behavioral data or utterance content, the data collection unit can prioritize collecting relevant data by taking into account the other party's geographical location information. For example, if the other party is in a specific location, the data collection unit prioritizes collecting utterance content related to that location. Furthermore, if the other party is on the move, the data collection unit can also collect behavioral data related to the destination. For example, if the other party is on the move, the data collection unit collects behavioral data related to the destination. Furthermore, if the other party is interested in a specific region, the data collection unit can prioritize collecting data related to that region. For example, if the other party is interested in a specific region, the data collection unit prioritizes collecting data related to that region. This allows for efficient collection of highly relevant data by taking geographical location information into account. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the other party's geographical location information into the generation AI and cause the generation AI to prioritize the collection of relevant data.

[0034] When collecting behavioral data or comment content, the data collection unit can analyze the other party's social media activity and collect related data. For example, the data collection unit collects content shared by the other party on social media and uses it as behavioral data. The data collection unit can also analyze the other party's social media comment content and collect related data. For example, the data collection unit analyzes the other party's social media comment content and collects related data. The data collection unit can also collect related data by referring to the activities of the other party's friends on social media. For example, the data collection unit collects related data by referring to the activities of the other party's friends on social media. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data collection unit can input the other party's social media activity data into the generation AI and cause the generation AI to collect related data.

[0035] When collecting behavioral data or speech content, the data collection unit can adjust the collection method by reflecting the other party's past feedback. The data collection unit adjusts the collection method based on, for example, feedback provided by the other party in the past. The data collection unit can also prioritize the use of a specific data collection method based on the other party's past feedback. For example, the data collection unit prioritizes the use of a specific data collection method based on the other party's past feedback. The data collection unit can also customize the type of data to be collected by referring to the other party's feedback. For example, the data collection unit customizes the type of data to be collected by referring to the other party's feedback. This allows the collection method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data collection unit can input the other party's past feedback data into the generation AI and cause the generation AI to adjust the collection method.

[0036] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0037] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of data. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. The analysis unit can also apply a natural language processing algorithm to speech content. For example, the analysis unit applies a natural language processing algorithm to speech content. The analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit applies an image analysis algorithm to image data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the other party's past analysis results. The analysis unit, for example, corrects the current analysis result based on the other party's past analysis results. The analysis unit can also extract specific patterns from the past analysis results and reflect them in the current analysis. For example, the analysis unit extracts specific patterns from the past analysis results and reflects them in the current analysis. The analysis unit can also adjust the analysis algorithm by referring to the other party's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the other party's past analysis results. In this way, the accuracy of the analysis is 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 input the other party's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can set analysis priorities based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit adjusts the analysis schedule based on the time of submission. This enables analysis to be performed efficiently by determining the analysis priorities based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI set the analysis priorities.

[0040] During analysis, the analysis unit can set the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. For example, the analysis unit adjusts the analysis schedule based on the relevance of the data. In this way, analysis can be performed efficiently by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI set the order of analysis.

[0041] During analysis, the analysis unit can set the use of technical terminology in the analysis according to the other party's level of expertise. For example, if the other party has technical expertise, the analysis unit provides analysis results that make extensive use of technical terminology. Furthermore, if the other party does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. For example, if the other party does not have technical expertise, the analysis unit provides concise and easy-to-understand analysis results. Furthermore, the analysis unit can also adjust the way in which the analysis results are presented according to the other party's level of expertise. For example, the analysis unit adjusts the way in which the analysis results are presented according to the other party's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the other party's level of expertise. 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 the other party's level of expertise into the generation AI and have the generation AI execute the setting of the use of technical terminology.

[0042] The quantification unit can adjust the precision of the quantification based on the importance of the analysis result during quantification. For example, the quantification unit performs detailed quantification on analysis results with high importance. The quantification unit can also perform simplified quantification on analysis results with low importance. For example, the quantification unit performs simplified quantification on analysis results with low importance. The quantification unit can also determine the priority of quantification according to the importance of the analysis result. For example, the quantification unit determines the priority of quantification according to the importance of the analysis result. This allows for efficient quantification by adjusting the level of detail of quantification according to the importance of the analysis result. Some or all of the above-described processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the precision of the quantification.

[0043] The quantification unit can apply an appropriate quantification algorithm depending on the category of the analysis result during quantification. For example, the quantification unit applies a leadership evaluation algorithm to leadership potential. The quantification unit can also apply a communication evaluation algorithm to communication ability potential. For example, the quantification unit applies a communication evaluation algorithm to communication ability potential. The quantification unit can also apply a problem-solving evaluation algorithm to problem-solving ability potential. For example, the quantification unit applies a problem-solving evaluation algorithm to problem-solving ability potential. This improves the accuracy of quantification by applying an appropriate quantification algorithm depending on the category of the analysis result. Some or all of the above-mentioned processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the category of the analysis result to the generation AI and cause the generation AI to apply an appropriate quantification algorithm.

[0044] The quantification unit can improve the accuracy of the quantification by referring to the other party's past quantification results when quantifying. For example, the quantification unit corrects the current quantification result based on the other party's past quantification results. The quantification unit can also extract a specific pattern from the past quantification results and reflect it in the current quantification. For example, the quantification unit extracts a specific pattern from the past quantification results and reflects it in the current quantification. The quantification unit can also adjust the quantification algorithm by referring to the other party's past quantification results. For example, the quantification unit adjusts the quantification algorithm by referring to the other party's past quantification results. In this way, the accuracy of the quantification is improved by referring to the past quantification results. Some or all of the above-mentioned processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the other party's past quantification results into the generation AI and cause the generation AI to improve the accuracy of the quantification.

[0045] During digitization, the quantification unit can set a priority for quantification based on the submission time of the analysis results. For example, the quantification unit prioritizes quantification of the most recent analysis results. The quantification unit can also postpone quantification of analysis results that were submitted earlier. For example, the quantification unit can postpone quantification of analysis results that were submitted earlier. The quantification unit can also adjust the quantification schedule based on the submission time. For example, the quantification unit adjusts the quantification schedule based on the submission time. This enables efficient quantification by determining the priority for quantification based on the submission time of the analysis results. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can input the submission time of the analysis results to the generation AI and cause the generation AI to set the priority for quantification.

[0046] The quantification unit can set the order of quantification based on the relevance of the analysis results during quantification. For example, the quantification unit prioritizes quantification of highly relevant analysis results. The quantification unit can also postpone quantification of less relevant analysis results. For example, the quantification unit quantifies less relevant analysis results at a later date. The quantification unit can also adjust the schedule for quantification based on the relevance of the analysis results. For example, the quantification unit adjusts the schedule for quantification based on the relevance of the analysis results. This allows for efficient quantification by adjusting the order of quantification based on the relevance of the analysis results. Some or all of the above-described processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the relevance of the analysis results to the generation AI and have the generation AI set the order of quantification.

[0047] During digitization, the quantification unit can adjust the precision of the quantification according to the expertise level of the other party. For example, if the other party has expertise, the quantification unit provides detailed quantification results. Furthermore, if the other party does not have expertise, the quantification unit can also provide concise and easy-to-understand quantification results. For example, if the other party does not have expertise, the quantification unit provides concise and easy-to-understand quantification results. Furthermore, the quantification unit can also adjust the way in which the quantification results are expressed according to the expertise level of the other party. For example, the quantification unit adjusts the way in which the quantification results are expressed according to the expertise level of the other party. This allows for adjusting the level of detail of the quantification according to the expertise level of the other party, thereby providing a more appropriate quantification result. Some or all of the above-described processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the expertise level of the other party into the generation AI and cause the generation AI to adjust the precision of the quantification.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When collecting the other party's behavioral data or speech content, the data collection unit can monitor the other party's health condition and adjust the collection method based on the other party's health condition. For example, if the other party is tired, the collection frequency can be reduced. On the other hand, if the other party is healthy and active, the collection frequency can be increased. Furthermore, the type of data to be collected can be changed based on the other party's health condition. For example, if the other party is feeling stressed, stress-related data can be collected first. This makes it possible to optimize data collection according to the other party's health condition.

[0050] The data collection unit can analyze the other party's past behavioral data and comments, and set priorities for the data to be collected based on the other party's interests. For example, if the other party has shown interest in a particular topic in the past, data related to that topic can be collected preferentially. Also, if the other party has shown a particular behavioral pattern in the past, data related to that behavioral pattern can be collected preferentially. This makes it possible to optimize data collection based on the other party's interests.

[0051] The data collection unit can select the type of data to collect based on the recipient's current projects and areas of interest. For example, it can prioritize collecting data related to the project the recipient is currently working on. It can also filter and collect relevant data based on the recipient's areas of interest. It can also focus on collecting data related to topics that the recipient is interested in. This allows it to collect highly relevant data by filtering data based on the recipient's areas of interest.

[0052] The data collection unit can adjust the format of the data to be collected depending on the input method of the other party. For example, it can automatically convert what the other party says into text and collect it. It can also collect what the other party types in text as is. It can also analyze what the other party explains using images and collect related data. This makes it possible to collect data in the optimal format depending on the other party's input method.

[0053] The data collection unit can select the type of data to collect by taking into account the geographical location information of the other party. For example, if the other party is in a specific location, it can prioritize collecting utterances related to that location. Also, if the other party is on the move, it can collect behavioral data related to their destination. Furthermore, if the other party is interested in a specific region, it can prioritize collecting data related to that region. In this way, by taking geographical location information into account, it is possible to efficiently collect highly relevant data.

[0054] The data collection unit can analyze the other party's social media activity and collect related data. For example, it can collect the content the other party has shared on social media and use it as behavioral data. It can also analyze the content the other party has posted on social media and collect related data. It can also collect related data by referring to the activities of the other party's friends on social media. This allows for efficient collection of related data by analyzing social media activity.

[0055] The data collection unit can adjust the collection method by reflecting the other party's past feedback. For example, the data collection unit can adjust the collection method based on feedback provided by the other party in the past. It can also prioritize the use of specific data collection methods based on the other party's past feedback. Furthermore, it can also customize the type of data to be collected by referring to the other party's feedback. This allows the collection method to be optimized by reflecting past feedback.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection unit collects the behavioral data or speech content of the other party. The behavioral data of the other party includes, but is not limited to, movement history, click history, purchase history, etc. The data collection unit can collect detailed data, such as what the other party said in a meeting and what attitude they showed. The data collection unit can also collect the speech content of the other party as text data using voice recognition technology. For example, the data collection unit records speech during a meeting and converts it into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the data collected by the data collection unit and analyze the characteristics of the other person. Characteristics include, but are not limited to, personality traits and behavioral traits. For example, the analysis unit analyzes the other person's personality and values ​​from the content of their statements and behavioral patterns. The generation AI can analyze the other person's characteristics using text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation AI analyzes the content of the other person's statements and discerns their personality traits. Step 3: The quantification unit quantifies the other person's abilities based on the analysis results obtained by the analysis unit. Examples of abilities include, but are not limited to, leadership potential and communication ability potential. For example, the quantification unit quantifies the other person's leadership potential based on the analysis results. The quantification unit can also quantify the other person's communication ability potential based on the analysis results. This allows the system according to the embodiment to accurately understand the other person's true nature and quantify that potential. Some or all of the above-described processing in the quantification unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the quantification unit may perform quantification using a generative AI model that receives the analysis results obtained by the analysis unit as input and outputs a quantified result.

[0058] (Example 2) A system according to an embodiment of the present invention collects behavioral data and speech content from other people, and a generation AI analyzes this information to discern the other person's true nature and quantify their potential. This system collects behavioral data and speech content from other people, and a generation AI analyzes this information to discern the other person's true nature and quantify the other person's potential based on the analysis results. For example, the system collects behavioral data and speech content from other people. For example, data such as what the other person said in a meeting and what attitude they showed is collected. Next, the generation AI analyzes the collected data. The generation AI analyzes the collected data to discern the other person's true nature. For example, it can analyze a person's personality and values ​​from the content of their speech and behavioral patterns. Furthermore, it quantifies the other person's potential based on the analysis results. The generation AI quantifies the other person's potential based on the analysis results and displays it as a specific numerical value. For example, it can quantify leadership potential and communication ability potential. This makes it possible to understand the other person's true nature and grasp their potential. This allows the system to accurately understand the other person's true nature and quantify that potential. For example, by utilizing generative AI during job interviews and personnel evaluations, it is possible to accurately understand the other person's true nature and quantify their potential, which will enable more appropriate personnel management and interpersonal relationship building.

[0059] The system according to the embodiment includes a data collection unit, an analysis unit, and a quantification unit. The data collection unit collects behavioral data or speech content of the other party. The behavioral data of the other party includes, but is not limited to, movement history, click history, and purchase history. The data collection unit can collect detailed data, such as what the other party said in a meeting and what attitude they showed. The data collection unit can also collect the other party's speech content as text data using speech recognition technology. For example, the data collection unit records speech during a meeting and converts it into text data using speech recognition technology. The analysis unit uses a generation AI to analyze the data collected by the data collection unit and analyze the other party's characteristics. Examples of characteristics include, but are not limited to, personality traits and behavioral traits. For example, the analysis unit analyzes the other party's personality and values ​​from the other party's speech content and behavioral patterns. The generation AI can analyze the other party's characteristics using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI analyzes the other party's speech content to discern their personality traits. The quantification unit quantifies the abilities of the other party based on the analysis results obtained by the analysis unit. Examples of abilities include, but are not limited to, leadership potential and communication ability potential. For example, the quantification unit quantifies the leadership potential of the other party based on the analysis results. The quantification unit can also quantify the communication ability potential of the other party based on the analysis results. This allows the system according to the embodiment to accurately understand the essence of the other party and quantify that potential. Some or all of the above-described processing in the quantification unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the quantification unit may perform quantification using a generative AI model that receives the analysis results obtained by the analysis unit as input and outputs a quantified result.

[0060] The data collection unit can estimate the other party's emotions and appropriately adjust the timing of collecting behavioral data or speech content based on the estimated emotions. The data collection unit, for example, captures the other party's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expressions. The data collection unit can also record the other party's voice and estimate the emotions using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the voice and calculates an emotion score. The data collection unit can also collect the other party's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on heart rate fluctuations. This allows data to be collected at the optimal timing depending on the other party's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data collection unit may input image data of the other person taken with a camera into the generation AI, and have the generation AI estimate the other person's emotions.

[0061] The data collection unit can analyze the other party's past behavioral data or utterance content and select an appropriate collection method. For example, the data collection unit can analyze utterance content from past meetings and focus on collecting utterances made in similar situations. The data collection unit can also collect behavioral data at specific time periods based on past behavioral patterns. For example, the data collection unit analyzes past behavioral data and collects behavioral data at specific time periods. The data collection unit can also prioritize collecting utterances on specific topics from past utterance content. For example, the data collection unit analyzes past utterance content and prioritizes collecting utterances on specific topics. This allows for efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-described processing in the data collection unit can be performed using, or without, a generation AI. For example, the data collection unit can input past behavioral data and utterance content into the generation AI and have the generation AI select the optimal collection method.

[0062] When collecting behavioral data or comment content, the data collection unit can filter the data based on the other party's current project or area of ​​interest. For example, the data collection unit prioritizes collecting comment content related to the other party's current project. The data collection unit can also filter and collect related behavioral data based on the other party's area of ​​interest. For example, the data collection unit filters and collects related behavioral data based on the other party's area of ​​interest. The data collection unit can also focus on collecting comment content related to topics in which the other party is interested. For example, the data collection unit focuses on collecting comment content related to topics in which the other party is interested. This allows for highly relevant data to be collected by filtering data based on the other party's area of ​​interest. Some or all of the above-mentioned processing in the data collection unit can be performed using, or without, the generation AI. For example, the data collection unit can input data related to the other party's current project or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0063] When collecting behavioral data or speech content, the data collection unit can select an appropriate collection means depending on the input method of the other party. For example, the data collection unit automatically converts the speech of the other party into text and collects it. The data collection unit can also collect the text input by the other party as is. For example, the data collection unit collects the text input by the other party as is. The data collection unit can also analyze the explanation given by the other party using images and collect related data. For example, the data collection unit analyzes the explanation given by the other party using images and collects related data. This makes it possible to collect data using the optimal means depending on the input method of the other party. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data collection unit can input data depending on the input method of the other party into the generation AI and have the generation AI select the optimal collection means.

[0064] The data collection unit can estimate the emotions of the other party and set a priority order for the data to be collected based on the estimated emotions of the other party. For example, if the other party is excited, the data collection unit prioritizes collecting utterances related to the heightened emotions. Furthermore, if the other party is calm, the data collection unit can also prioritize collecting calm utterances. For example, if the other party is calm, the data collection unit prioritizes collecting calm utterances. Furthermore, if the other party is feeling anxious, the data collection unit can also prioritize collecting utterances related to the cause of the anxiety. For example, if the other party is feeling anxious, the data collection unit prioritizes collecting utterances related to the cause of the anxiety. Thus, by determining the priority order of data according to the other party's emotions, important data can be collected preferentially. The 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 data collection unit can be performed, for example, using the generation AI, or without the generation AI. For example, the data collection unit can input the other person's emotional data into the generation AI and have the generation AI set the priority of the data to be collected.

[0065] When collecting behavioral data or utterance content, the data collection unit can prioritize collecting relevant data by taking into account the other party's geographical location information. For example, if the other party is in a specific location, the data collection unit prioritizes collecting utterance content related to that location. Furthermore, if the other party is on the move, the data collection unit can also collect behavioral data related to the destination. For example, if the other party is on the move, the data collection unit collects behavioral data related to the destination. Furthermore, if the other party is interested in a specific region, the data collection unit can prioritize collecting data related to that region. For example, if the other party is interested in a specific region, the data collection unit prioritizes collecting data related to that region. This allows for efficient collection of highly relevant data by taking geographical location information into account. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the other party's geographical location information into the generation AI and cause the generation AI to prioritize the collection of relevant data.

[0066] When collecting behavioral data or comment content, the data collection unit can analyze the other party's social media activity and collect related data. For example, the data collection unit collects content shared by the other party on social media and uses it as behavioral data. The data collection unit can also analyze the other party's social media comment content and collect related data. For example, the data collection unit analyzes the other party's social media comment content and collects related data. The data collection unit can also collect related data by referring to the activities of the other party's friends on social media. For example, the data collection unit collects related data by referring to the activities of the other party's friends on social media. In this way, related data can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data collection unit can input the other party's social media activity data into the generation AI and cause the generation AI to collect related data.

[0067] When collecting behavioral data or speech content, the data collection unit can adjust the collection method by reflecting the other party's past feedback. The data collection unit adjusts the collection method based on, for example, feedback provided by the other party in the past. The data collection unit can also prioritize the use of a specific data collection method based on the other party's past feedback. For example, the data collection unit prioritizes the use of a specific data collection method based on the other party's past feedback. The data collection unit can also customize the type of data to be collected by referring to the other party's feedback. For example, the data collection unit customizes the type of data to be collected by referring to the other party's feedback. This allows the collection method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the data collection unit can input the other party's past feedback data into the generation AI and cause the generation AI to adjust the collection method.

[0068] The analysis unit can estimate the emotions of the other party and adjust the analysis method based on the estimated emotions of the other party. For example, if the other party is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the other party is nervous, the analysis unit can provide a concise and to-the-point analysis result. For example, if the other party is nervous, the analysis unit provides a concise and to-the-point analysis result. Furthermore, if the other party is excited, the analysis unit can provide a visually appealing analysis result. For example, if the other party is excited, the analysis unit provides a visually appealing analysis result. This allows for adjusting the analysis presentation method according to the other party's emotions, thereby providing 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, for example, using the generation AI, or without the generation AI. For example, the analysis unit can input the other person's emotional data into the generation AI and have the generation AI adjust the analysis method.

[0069] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0070] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of data. For example, the analysis unit applies a behavioral analysis algorithm to behavioral data. The analysis unit can also apply a natural language processing algorithm to speech content. For example, the analysis unit applies a natural language processing algorithm to speech content. The analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit applies an image analysis algorithm to image data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0071] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the other party's past analysis results. The analysis unit, for example, corrects the current analysis result based on the other party's past analysis results. The analysis unit can also extract specific patterns from the past analysis results and reflect them in the current analysis. For example, the analysis unit extracts specific patterns from the past analysis results and reflects them in the current analysis. The analysis unit can also adjust the analysis algorithm by referring to the other party's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the other party's past analysis results. In this way, the accuracy of the analysis is 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 input the other party's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0072] The analysis unit can estimate the other party's emotions and adjust the analysis time based on the estimated emotions. For example, if the other party is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the other party is relaxed, the analysis unit can provide a detailed analysis result. For example, if the other party is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the other party is excited, the analysis unit can provide a visually appealing analysis result. For example, if the other party is excited, the analysis unit can provide a visually appealing analysis result. This allows for adjusting the length of the analysis according to the other party's emotions to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the other party's emotion data into the generation AI and have the generation AI adjust the analysis time.

[0073] During analysis, the analysis unit can set analysis priorities based on the time of data submission. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit adjusts the analysis schedule based on the time of submission. This enables analysis to be performed efficiently by determining the analysis priorities based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI set the analysis priorities.

[0074] During analysis, the analysis unit can set the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. For example, the analysis unit adjusts the analysis schedule based on the relevance of the data. In this way, analysis can be performed efficiently by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI set the order of analysis.

[0075] During analysis, the analysis unit can set the use of technical terminology in the analysis according to the other party's level of expertise. For example, if the other party has technical expertise, the analysis unit provides analysis results that make extensive use of technical terminology. Furthermore, if the other party does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. For example, if the other party does not have technical expertise, the analysis unit provides concise and easy-to-understand analysis results. Furthermore, the analysis unit can also adjust the way in which the analysis results are presented according to the other party's level of expertise. For example, the analysis unit adjusts the way in which the analysis results are presented according to the other party's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the other party's level of expertise. 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 the other party's level of expertise into the generation AI and have the generation AI execute the setting of the use of technical terminology.

[0076] The quantification unit can estimate the other party's emotions and set a quantification method based on the estimated emotions. For example, if the other party is relaxed, the quantification unit provides a detailed quantification result. Furthermore, if the other party is nervous, the quantification unit can provide a concise and to-the-point quantification result. For example, if the other party is nervous, the quantification unit provides a concise and to-the-point quantification result. Furthermore, if the other party is excited, the quantification unit can provide a visually appealing quantification result. For example, if the other party is excited, the quantification unit provides a visually appealing quantification result. This allows for adjusting the quantification method according to the other party's emotions to provide a more appropriate quantification result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the quantification unit can be performed, for example, using the generation AI, or without the generation AI. For example, the quantification unit can input the other person's emotional data into the generation AI and have the generation AI execute the settings for the quantification method.

[0077] The quantification unit can adjust the precision of the quantification based on the importance of the analysis result during quantification. For example, the quantification unit performs detailed quantification on analysis results with high importance. The quantification unit can also perform simplified quantification on analysis results with low importance. For example, the quantification unit performs simplified quantification on analysis results with low importance. The quantification unit can also determine the priority of quantification according to the importance of the analysis result. For example, the quantification unit determines the priority of quantification according to the importance of the analysis result. This allows for efficient quantification by adjusting the level of detail of quantification according to the importance of the analysis result. Some or all of the above-described processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the importance of the analysis result to the generation AI and cause the generation AI to adjust the precision of the quantification.

[0078] The quantification unit can apply an appropriate quantification algorithm depending on the category of the analysis result during quantification. For example, the quantification unit applies a leadership evaluation algorithm to leadership potential. The quantification unit can also apply a communication evaluation algorithm to communication ability potential. For example, the quantification unit applies a communication evaluation algorithm to communication ability potential. The quantification unit can also apply a problem-solving evaluation algorithm to problem-solving ability potential. For example, the quantification unit applies a problem-solving evaluation algorithm to problem-solving ability potential. This improves the accuracy of quantification by applying an appropriate quantification algorithm depending on the category of the analysis result. Some or all of the above-mentioned processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the category of the analysis result to the generation AI and cause the generation AI to apply an appropriate quantification algorithm.

[0079] The quantification unit can improve the accuracy of the quantification by referring to the other party's past quantification results when quantifying. For example, the quantification unit corrects the current quantification result based on the other party's past quantification results. The quantification unit can also extract a specific pattern from the past quantification results and reflect it in the current quantification. For example, the quantification unit extracts a specific pattern from the past quantification results and reflects it in the current quantification. The quantification unit can also adjust the quantification algorithm by referring to the other party's past quantification results. For example, the quantification unit adjusts the quantification algorithm by referring to the other party's past quantification results. In this way, the accuracy of the quantification is improved by referring to the past quantification results. Some or all of the above-mentioned processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the other party's past quantification results into the generation AI and cause the generation AI to improve the accuracy of the quantification.

[0080] The quantification unit can estimate the emotions of the other party and set a priority for quantification based on the estimated emotions of the other party. For example, if the other party is excited, the quantification unit can prioritize quantification of potentials related to heightened emotions. Furthermore, if the other party is calm, the quantification unit can also prioritize quantification of potentials in a calm state. For example, if the other party is calm, the quantification unit can prioritize quantification of potentials in a calm state. Furthermore, if the other party is feeling anxious, the quantification unit can also prioritize quantification of potentials related to the cause of the anxiety. For example, if the other party is feeling anxious, the quantification unit can prioritize quantification of potentials related to the cause of the anxiety. In this way, by determining the priority for quantification according to the other party's emotions, important potentials can be prioritized for quantification. 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 quantification unit can be performed, for example, using the generation AI, or without using the generation AI. For example, the quantification unit can input the other person's emotional data into the generation AI and have the generation AI set the priority of quantification.

[0081] During digitization, the quantification unit can set a priority for quantification based on the submission time of the analysis results. For example, the quantification unit prioritizes quantification of the most recent analysis results. The quantification unit can also postpone quantification of analysis results that were submitted earlier. For example, the quantification unit can postpone quantification of analysis results that were submitted earlier. The quantification unit can also adjust the quantification schedule based on the submission time. For example, the quantification unit adjusts the quantification schedule based on the submission time. This enables efficient quantification by determining the priority for quantification based on the submission time of the analysis results. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can input the submission time of the analysis results to the generation AI and cause the generation AI to set the priority for quantification.

[0082] The quantification unit can set the order of quantification based on the relevance of the analysis results during quantification. For example, the quantification unit prioritizes quantification of highly relevant analysis results. The quantification unit can also postpone quantification of less relevant analysis results. For example, the quantification unit quantifies less relevant analysis results at a later date. The quantification unit can also adjust the schedule for quantification based on the relevance of the analysis results. For example, the quantification unit adjusts the schedule for quantification based on the relevance of the analysis results. This allows for efficient quantification by adjusting the order of quantification based on the relevance of the analysis results. Some or all of the above-described processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the relevance of the analysis results to the generation AI and have the generation AI set the order of quantification.

[0083] During digitization, the quantification unit can adjust the precision of the quantification according to the expertise level of the other party. For example, if the other party has expertise, the quantification unit provides detailed quantification results. Furthermore, if the other party does not have expertise, the quantification unit can also provide concise and easy-to-understand quantification results. For example, if the other party does not have expertise, the quantification unit provides concise and easy-to-understand quantification results. Furthermore, the quantification unit can also adjust the way in which the quantification results are expressed according to the expertise level of the other party. For example, the quantification unit adjusts the way in which the quantification results are expressed according to the expertise level of the other party. This allows for adjusting the level of detail of the quantification according to the expertise level of the other party, thereby providing a more appropriate quantification result. Some or all of the above-described processing in the quantification unit may be performed using, or without, a generation AI. For example, the quantification unit can input the expertise level of the other party into the generation AI and cause the generation AI to adjust the precision of the quantification. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, analysis unit, and quantification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects behavioral data and speech content of the other party using the camera 42 and microphone 38B of the smart device 14, and processes the data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generation AI, and discerns the other party's true nature. The quantification unit is realized by the specific processing unit 290 of the data processing device 12, and quantifies the other party's potential based on the analysis results. Some or all of the data collection unit, analysis unit, and quantification unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, and quantification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects behavioral data and speech content of the other party using the camera 42 and microphone 238 of the smart glasses 214, and processes the data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI to discern the other party's true nature. The quantification unit is realized by the specific processing unit 290 of the data processing device 12, and quantifies the other party's potential based on the analysis results. Some or all of the data collection unit, analysis unit, and quantification unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, and quantification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the data collection unit collects behavioral data and speech content of the other party using the camera 42 and microphone 238 of the headset-type terminal 314, and processes the data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI to discern the other party's true nature. The quantification unit is realized by the specific processing unit 290 of the data processing device 12, and quantifies the other party's potential based on the analysis results. Some or all of the data collection unit, analysis unit, and quantification unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, and quantification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects behavioral data and speech content of the other party using the camera 42 and microphone 238 of the robot 414, and processes the data by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generative AI to discern the other party's true nature. The quantification unit is realized by the specific processing unit 290 of the data processing device 12, and quantifies the other party's potential based on the analysis results. Some or all of the data collection unit, analysis unit, and quantification unit may be realized, for example, by the control unit 46A of the robot 414.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] When collecting the other party's behavioral data or speech content, the data collection unit can monitor the other party's health condition and adjust the collection method based on the other party's health condition. For example, if the other party is tired, the collection frequency can be reduced. On the other hand, if the other party is healthy and active, the collection frequency can be increased. Furthermore, the type of data to be collected can be changed based on the other party's health condition. For example, if the other party is feeling stressed, stress-related data can be collected first. This makes it possible to optimize data collection according to the other party's health condition.

[0086] The data collection unit can estimate the other person's emotions and select the type of data to collect based on the estimated emotions. For example, if the other person is happy, it can prioritize collecting positive comments. Also, if the other person is sad, it can prioritize collecting negative comments. Furthermore, if the other person is angry, it can collect comments related to the cause of that anger. In this way, by selecting the type of data to collect depending on the other person's emotions, it is possible to collect more appropriate data.

[0087] The data collection unit can analyze the other party's past behavioral data and comments, and set priorities for the data to be collected based on the other party's interests. For example, if the other party has shown interest in a particular topic in the past, data related to that topic can be collected preferentially. Also, if the other party has shown a particular behavioral pattern in the past, data related to that behavioral pattern can be collected preferentially. This makes it possible to optimize data collection based on the other party's interests.

[0088] The data collection unit can select the type of data to collect based on the recipient's current projects and areas of interest. For example, it can prioritize collecting data related to the project the recipient is currently working on. It can also filter and collect relevant data based on the recipient's areas of interest. It can also focus on collecting data related to topics that the recipient is interested in. This allows it to collect highly relevant data by filtering data based on the recipient's areas of interest.

[0089] The data collection unit can adjust the format of the data to be collected depending on the input method of the other party. For example, it can automatically convert what the other party says into text and collect it. It can also collect what the other party types in text as is. It can also analyze what the other party explains using images and collect related data. This makes it possible to collect data in the optimal format depending on the other party's input method.

[0090] The data collection unit can estimate the emotions of the other party and set the priority of the data to be collected based on the estimated emotions of the other party. For example, if the other party is excited, it can prioritize collecting statements related to the heightened emotions. Also, if the other party is calm, it can prioritize collecting calm statements. Furthermore, if the other party is feeling anxious, it can prioritize collecting statements related to the cause of that anxiety. In this way, by determining the priority of data according to the other party's emotions, it is possible to prioritize the collection of important data.

[0091] The data collection unit can select the type of data to collect by taking into account the geographical location information of the other party. For example, if the other party is in a specific location, it can prioritize collecting utterances related to that location. Also, if the other party is on the move, it can collect behavioral data related to their destination. Furthermore, if the other party is interested in a specific region, it can prioritize collecting data related to that region. In this way, by taking geographical location information into account, it is possible to efficiently collect highly relevant data.

[0092] The data collection unit can analyze the other party's social media activity and collect related data. For example, it can collect the content the other party has shared on social media and use it as behavioral data. It can also analyze the content the other party has posted on social media and collect related data. It can also collect related data by referring to the activities of the other party's friends on social media. This allows for efficient collection of related data by analyzing social media activity.

[0093] The data collection unit can adjust the collection method by reflecting the other party's past feedback. For example, the data collection unit can adjust the collection method based on feedback provided by the other party in the past. It can also prioritize the use of specific data collection methods based on the other party's past feedback. Furthermore, it can also customize the type of data to be collected by referring to the other party's feedback. This allows the collection method to be optimized by reflecting past feedback.

[0094] The analysis unit can estimate the other party's emotions and adjust the analysis method based on the estimated emotions. For example, if the other party is relaxed, it can provide detailed analysis results. If the other party is nervous, it can provide concise and to-the-point analysis results. Furthermore, if the other party is excited, it can provide visually appealing analysis results. This allows the system to provide more appropriate analysis results by adjusting the analysis presentation method according to the other party's emotions.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The data collection unit collects the behavioral data or speech content of the other party. The behavioral data of the other party includes, but is not limited to, movement history, click history, purchase history, etc. The data collection unit can collect detailed data, such as what the other party said in a meeting and what attitude they showed. The data collection unit can also collect the speech content of the other party as text data using voice recognition technology. For example, the data collection unit records speech during a meeting and converts it into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the data collected by the data collection unit and analyze the characteristics of the other person. Characteristics include, but are not limited to, personality traits and behavioral traits. For example, the analysis unit analyzes the other person's personality and values ​​from the content of their statements and behavioral patterns. The generation AI can analyze the other person's characteristics using text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation AI analyzes the content of the other person's statements and discerns their personality traits. Step 3: The quantification unit quantifies the other person's abilities based on the analysis results obtained by the analysis unit. Examples of abilities include, but are not limited to, leadership potential and communication ability potential. For example, the quantification unit quantifies the other person's leadership potential based on the analysis results. The quantification unit can also quantify the other person's communication ability potential based on the analysis results. This allows the system according to the embodiment to accurately understand the other person's true nature and quantify that potential. Some or all of the above-described processing in the quantification unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the quantification unit may perform quantification using a generative AI model that receives the analysis results obtained by the analysis unit as input and outputs a quantified result.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects behavioral data or speech content of the other party; an analysis unit that analyzes the data collected by the data collection unit and analyzes the characteristics of the other party; a quantification unit that quantifies the opponent's ability based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The data collection unit Estimate the emotions of the other party and appropriately adjust the timing of collecting behavioral data or speech content based on the estimated emotions of the other party. The system of claim 1 .

3. The data collection unit Analyze the other person's past behavioral data or statements and select the appropriate collection method The system of claim 1 .

4. The data collection unit When collecting behavioral data or statements, filter them based on their current projects or areas of interest. The system of claim 1 .

5. The data collection unit When collecting behavioral data or spoken content, choose the appropriate collection method depending on the other person's input method. The system of claim 1 .

6. The data collection unit Inferring the other person's emotions and prioritizing the data to collect based on the inferred emotions The system of claim 1 .

7. The data collection unit When collecting behavioral data or speech content, consider the geographic location of the other party and prioritize collecting relevant data. The system of claim 1 .

8. The data collection unit When collecting behavioral data or statements, analyze the other person's social media activity and collect appropriate data. The system of claim 1 .

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