System

Abstract

An object of a system according to an embodiment is to quantify a risk of harassment and provide the quantified risk to a user.SOLUTION: A system includes a collection unit, an analysis unit, a provision unit, a feedback unit, and a learning unit. The collection unit collects a record of a mail or a conference and a call history. The analysis unit analyzes the data collected by the collection unit and quantifies the risk of harassment. The providing unit provides the user with the analysis result obtained by the analysis unit. The feedback unit receives feedback from a user. The learning unit causes the AI to learn based on the feedback received by the feedback 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 technologies have had the problem of making it difficult to detect harassment risks early and deal with them appropriately.

[0005] The system according to the embodiment aims to quantify the risk of harassment and provide it to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a feedback unit, and a learning unit. The collection unit collects email or meeting records and call history. The analysis unit analyzes the data collected by the collection unit and quantifies the risk of harassment. The provision unit provides the user with the analysis results obtained by the analysis unit. The feedback unit accepts user feedback. The learning unit allows the AI ​​to learn based on the feedback accepted by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can quantify the risk of harassment and provide it to the user. [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 harassment risk assessment system according to an embodiment of the present invention checks for the possibility of harassment based on emails, online meetings, and phone call histories, and then quantifies and suggests the risk. Users input emails, meeting records, and call histories into the system, and AI analyzes these data to quantify and suggest the risk of harassment. Users can use these numerical values ​​to understand the risk of harassment. Furthermore, the AI ​​learns from user feedback and understands individual communication styles. For example, if a user provides feedback such as "this comment is not harassment," the AI ​​learns that feedback and incorporates it into future analyses. This allows the AI ​​to understand the user's communication style and enable more accurate analysis. For example, the harassment risk assessment system can analyze email content to quantify the risk of harassment. It can also analyze recordings of online meetings to determine whether comments made during the meeting constitute harassment. It can also analyze phone call histories to determine whether comments made during the call constitute harassment. This allows the harassment risk assessment system to prevent unconscious harassment and provide victims with a sense of security. In this way, the harassment risk assessment system can reduce the risk of being accused of unintentional harassment and provide victims of harassment with a sense of security and guidance for consultation.

[0029] A harassment risk assessment system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a feedback unit, and a learning unit. The collection unit collects emails, meeting records, and call histories. For example, the collection unit collects email data by a user inputting email content into the system. The collection unit can also collect recorded data of online meetings. The collection unit can also collect telephone call histories. For example, the collection unit collects conference data by a user uploading recorded conference data. The analysis unit analyzes the data collected by the collection unit and quantifies the harassment risk. For example, the analysis unit analyzes the email content using natural language processing technology and quantifies the harassment risk. The analysis unit can also analyze recorded conference data using voice recognition technology and quantify the harassment risk. The analysis unit can also analyze telephone call histories and quantify the harassment risk. For example, the analysis unit analyzes the email content, detects specific keywords or phrases, and quantifies the harassment risk. The providing unit provides the user with the analysis results obtained by the analysis unit. The providing unit, for example, displays the analysis results as numerical values. The providing unit can also display the analysis results as graphs or charts. Furthermore, the providing unit can send the analysis results to the user as emails or notifications. For example, the providing unit displays the analysis results on a dashboard so that the user can check them in real time. The feedback unit accepts user feedback. For example, the feedback unit accepts feedback by the user inputting feedback on the analysis results. The feedback unit can also accept feedback by the user evaluating the analysis results. Furthermore, the feedback unit can accept feedback by the user inputting comments on the analysis results. For example, the feedback unit accepts feedback by the user providing feedback on the analysis results such as "this comment is not harassment." The learning unit allows the AI ​​to learn based on the feedback accepted by the feedback unit.The learning unit, for example, allows the AI ​​to adjust the analysis algorithm based on user feedback. The learning unit can also allow the AI ​​to update the analysis model based on user feedback. Furthermore, the learning unit can also allow the AI ​​to improve analysis accuracy based on user feedback. For example, the learning unit allows the AI ​​to adjust the importance of specific keywords and phrases based on user feedback. As a result, the harassment risk assessment system according to the embodiment can collect, analyze, and provide user communication data and learn based on the feedback, thereby quantifying and suggesting the risk of harassment.

[0030] The collection unit analyzes the user's past communication history and selects a collection method. For example, the collection unit prioritizes collection of communication means (email, telephone, etc.) that the user has frequently used in the past. The collection unit can also concentrate collection during specific time periods based on the user's past communication history. The collection unit can also analyze the user's past communication patterns and suggest the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past communication history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past communication history data into a generation AI and have the generation AI select the optimal collection method.

[0031] When collecting data, the collection unit filters the data based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting data related to the project the user is currently working on. The collection unit can also filter and collect related data based on the user's area of ​​interest. The collection unit can also collect only necessary data depending on the user's current work content. This makes it possible to collect highly relevant data by filtering data based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0032] When collecting data, the collection unit selects a collection means according to the user's input method. For example, when the user is communicating by voice, the collection unit prioritizes collecting voice data. Furthermore, when the user is communicating by text, the collection unit can also prioritize collecting text data. Furthermore, when the user is communicating using images, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting data related to the user's destination. Furthermore, if the user is staying in a specific location, the collection unit can also prioritize collecting data related to that location. This enables efficient data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0034] When collecting data, the collection unit analyzes the user's social media activities and collects related data. The collection unit collects related data based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables efficient data collection by analyzing the user's social media activities and collecting related data. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0035] When collecting data, the collection unit customizes the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also select the optimal collection method based on the user's past feedback. The collection unit can also customize the collection timing and method by reflecting the user's feedback. This enables efficient data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0036] During analysis, the analysis unit adjusts the level of detail 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. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit applies different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to email data. The analysis unit can also apply a voice recognition algorithm to voice data. The analysis unit can also apply an image analysis algorithm to image data. This enables efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also dynamically adjust the analysis accuracy by referring to the user's past analysis results. This improves the analysis accuracy by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0039] During analysis, the analysis unit determines the analysis priority 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. The analysis unit can also dynamically adjust the analysis priority according to the time of data submission. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit adjusts 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. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis 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, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. The analysis unit can also dynamically adjust the way in which the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0042] The providing unit adjusts the level of detail of the provided information based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. The providing unit can also dynamically adjust the level of detail of the provided information according to the importance of the analysis result. This enables efficient information provision by adjusting the level of detail of the provided information based on the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0043] The providing unit applies different providing algorithms depending on the category of the analysis results when providing the information. For example, the providing unit applies a text-based providing method to email analysis results. The providing unit can also apply an audio-based providing method to audio analysis results. The providing unit can also apply an image-based providing method to image analysis results. This enables efficient information provision by applying different providing algorithms depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the analysis results to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0044] The providing unit improves the accuracy of the provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also learn specific patterns from the user's past provision results and improve the accuracy of the provision. The providing unit can also dynamically adjust the accuracy of the provision by referring to the user's past provision results. This improves the accuracy of the provision by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0045] The providing unit determines the priority of provision based on the submission time of the analysis results at the time of provision. For example, the providing unit provides the latest analysis results preferentially. The providing unit can also provide analysis results that were submitted earlier later. The providing unit can also dynamically adjust the priority of provision according to the submission time of the analysis results. This enables efficient information provision by determining the priority of provision based on the submission time of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the submission time of the analysis results to the generation AI and have the generation AI determine the priority of provision.

[0046] The providing unit adjusts the order of provision based on the relevance of the analysis results when providing the results. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit can also provide analysis results with low relevance later. The providing unit can also dynamically adjust the order of provision according to the relevance of the analysis results. This enables efficient information provision by adjusting the order of provision based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0047] The providing unit adjusts the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit provides a method of providing information that makes extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide a method of providing information in simple language. The providing unit can also dynamically adjust the expression of the method of providing information according to the user's level of expertise. This makes it possible to provide information that is easy for the user to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology.

[0048] When collecting feedback, the feedback unit selects the optimal collection method by referring to the user's past feedback history. The feedback unit selects the optimal collection method, for example, based on the feedback format provided by the user in the past. The feedback unit can also preferentially use a specific question format based on the user's past feedback history. The feedback unit can also analyze the user's past feedback history and suggest the optimal collection method. This enables efficient feedback collection by selecting the optimal collection method by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select the optimal collection method.

[0049] The feedback unit customizes the content of the feedback based on the user's current situation when collecting the feedback. For example, if the user is busy, the feedback unit collects feedback in the form of a simple question. The feedback unit can also request detailed feedback if the user is relaxed. The feedback unit can also dynamically adjust the content of the feedback according to the user's current situation. This enables efficient feedback collection by customizing the content of the feedback based on the user's current situation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input data of the user's current situation to a generation AI and cause the generation AI to customize the content of the feedback.

[0050] The feedback unit improves the collection method by reflecting user feedback when collecting feedback. For example, the feedback unit adjusts the collection method based on feedback provided by the user. The feedback unit can also improve a specific question format based on user feedback. The feedback unit can also dynamically improve the collection method by reflecting user feedback. This enables efficient feedback collection by improving the collection method by reflecting user feedback. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input user feedback data into a generation AI and cause the generation AI to improve the collection method.

[0051] When collecting feedback, the feedback unit selects the optimal collection method taking into account the user's geographical location information. For example, when the user is in a specific area, the feedback unit prioritizes collecting feedback related to that area. Furthermore, when the user is traveling, the feedback unit can also prioritize collecting feedback related to the user's destination. Furthermore, when the user is staying in a specific location, the feedback unit can also prioritize collecting feedback related to that location. This enables efficient feedback collection by selecting the optimal collection method taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal collection method.

[0052] When collecting feedback, the feedback unit analyzes the user's social media activity and suggests feedback content. The feedback unit, for example, suggests relevant feedback based on information shared by the user on social media. The feedback unit can also analyze the user's social media activity content and suggest relevant feedback. The feedback unit can also suggest relevant feedback based on the activity of the user's friends on social media. This enables efficient feedback collection by analyzing the user's social media activity and suggesting feedback content. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest feedback content.

[0053] When collecting feedback, the feedback unit customizes the collection method by reflecting the user's past feedback. The feedback unit adjusts the collection method based on, for example, feedback provided by the user in the past. The feedback unit can also select the optimal collection method based on the user's past feedback. The feedback unit can also customize the collection timing and method by reflecting the user's feedback. This enables efficient feedback collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0054] During learning, the learning unit adjusts the learning algorithm by referring to past learning data. The learning unit adjusts the learning algorithm based on, for example, past learning data. The learning unit can also learn specific patterns from past learning data and optimize the algorithm. The learning unit can also dynamically optimize the learning algorithm by referring to past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to adjust the learning algorithm.

[0055] During learning, the learning unit updates the learning data to reflect user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also learn specific patterns from user feedback and update the data. The learning unit can also dynamically update the learning data to reflect user feedback. This enables efficient learning by updating the learning data to reflect user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0056] During learning, the learning unit weights the learning data based on the time of data submission. For example, the learning unit assigns a higher weight to the most recent data. The learning unit can also assign a lower weight to data that was submitted earlier. The learning unit can also dynamically adjust the weighting of the learning data depending on the time of data submission. This enables efficient learning by weighting the learning data based on the time of data submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of data submission to the generation AI and have the generation AI perform weighting of the learning data.

[0057] During learning, the learning unit integrates information from different data sources to enrich the learning data. For example, the learning unit integrates email data and voice data to enrich the learning data. The learning unit can also integrate social media data and call history to enrich the learning data. The learning unit can also integrate information from different data sources to dynamically enrich the learning data. This enables efficient learning by integrating information from different data sources to enrich the learning data. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit may input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can also analyze the user's past communication history and select a collection method. For example, it can prioritize collection of communication methods (email, telephone, etc.) that the user has frequently used in the past. The collection unit can also concentrate collection during specific time periods based on the user's past communication history. Furthermore, the collection unit can analyze the user's past communication patterns and suggest the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past communication history.

[0060] The providing unit can also adjust the level of detail to be provided based on the importance of the analysis result when providing the information. For example, detailed information is provided for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. Furthermore, the providing unit can dynamically adjust the level of detail to be provided depending on the importance of the analysis result. This allows for efficient information provision by adjusting the level of detail to be provided based on the importance of the analysis result.

[0061] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to email data. The analysis unit can also apply a voice recognition algorithm to voice data. Furthermore, the analysis unit can also apply an image analysis algorithm to image data. This allows for efficient analysis by applying different analysis algorithms depending on the data category.

[0062] When collecting feedback, the feedback unit can also select the optimal collection method by referring to the user's past feedback history. For example, the optimal collection method can be selected based on the feedback format provided by the user in the past. The feedback unit can also preferentially use a specific question format based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest the optimal collection method. In this way, efficient feedback collection is possible by selecting the optimal collection method by referring to the user's past feedback history.

[0063] During learning, the learning unit can also adjust the learning algorithm by referring to past learning data. For example, the learning algorithm is adjusted based on past learning data. The learning unit can also learn specific patterns from past learning data and optimize the algorithm. Furthermore, the learning unit can dynamically optimize the learning algorithm by referring to past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects emails, meeting records, and call histories. For example, email data is collected when a user enters email content into the system. The collection unit can also collect recording data of online meetings and telephone call histories. For example, meeting data is collected when a user uploads recording data of a meeting. Step 2: The analysis unit analyzes the data collected by the collection unit and quantifies the risk of harassment. For example, it can use natural language processing technology to analyze the content of emails and speech recognition technology to analyze recorded meeting data. It can also analyze phone call histories to detect specific keywords and phrases and quantify the risk of harassment. Step 3: The providing unit provides the analysis results obtained by the analysis unit to the user. For example, the analysis results can be displayed as numerical values ​​or as graphs or charts. Furthermore, the analysis results can be sent to the user by email or notification. For example, the providing unit displays the analysis results on a dashboard so that the user can check them in real time. Step 4: The feedback unit accepts user feedback. For example, the feedback unit accepts feedback by allowing the user to enter feedback, evaluation, or comments on the analysis results. For example, the feedback unit accepts feedback by allowing the user to say, "This comment is not harassment" on the analysis results. Step 5: The learning unit allows the AI ​​to learn based on the feedback received by the feedback unit. For example, based on user feedback, the AI ​​can adjust the analysis algorithm, update the analysis model, and improve analysis accuracy. For example, it can adjust the importance of specific keywords or phrases.

[0066] (Example 2) A harassment risk assessment system according to an embodiment of the present invention checks for the possibility of harassment based on emails, online meetings, and phone call histories, and then quantifies and suggests the risk. Users input emails, meeting records, and call histories into the system, and AI analyzes these data to quantify and suggest the risk of harassment. Users can use these numerical values ​​to understand the risk of harassment. Furthermore, the AI ​​learns from user feedback and understands individual communication styles. For example, if a user provides feedback such as "this comment is not harassment," the AI ​​learns that feedback and incorporates it into future analyses. This allows the AI ​​to understand the user's communication style and enable more accurate analysis. For example, the harassment risk assessment system can analyze email content to quantify the risk of harassment. It can also analyze recordings of online meetings to determine whether comments made during the meeting constitute harassment. It can also analyze phone call histories to determine whether comments made during the call constitute harassment. This allows the harassment risk assessment system to prevent unconscious harassment and provide victims with a sense of security. In this way, the harassment risk assessment system can reduce the risk of being accused of unintentional harassment and provide victims of harassment with a sense of security and guidance for consultation.

[0067] A harassment risk assessment system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a feedback unit, and a learning unit. The collection unit collects emails, meeting records, and call histories. For example, the collection unit collects email data by a user inputting email content into the system. The collection unit can also collect recorded data of online meetings. The collection unit can also collect telephone call histories. For example, the collection unit collects conference data by a user uploading recorded conference data. The analysis unit analyzes the data collected by the collection unit and quantifies the harassment risk. For example, the analysis unit analyzes the email content using natural language processing technology and quantifies the harassment risk. The analysis unit can also analyze recorded conference data using voice recognition technology and quantify the harassment risk. The analysis unit can also analyze telephone call histories and quantify the harassment risk. For example, the analysis unit analyzes the email content, detects specific keywords or phrases, and quantifies the harassment risk. The providing unit provides the user with the analysis results obtained by the analysis unit. The providing unit, for example, displays the analysis results as numerical values. The providing unit can also display the analysis results as graphs or charts. Furthermore, the providing unit can send the analysis results to the user as emails or notifications. For example, the providing unit displays the analysis results on a dashboard so that the user can check them in real time. The feedback unit accepts user feedback. For example, the feedback unit accepts feedback by the user inputting feedback on the analysis results. The feedback unit can also accept feedback by the user evaluating the analysis results. Furthermore, the feedback unit can accept feedback by the user inputting comments on the analysis results. For example, the feedback unit accepts feedback by the user providing feedback on the analysis results such as "this comment is not harassment." The learning unit allows the AI ​​to learn based on the feedback accepted by the feedback unit.The learning unit, for example, allows the AI ​​to adjust the analysis algorithm based on user feedback. The learning unit can also allow the AI ​​to update the analysis model based on user feedback. Furthermore, the learning unit can also allow the AI ​​to improve analysis accuracy based on user feedback. For example, the learning unit allows the AI ​​to adjust the importance of specific keywords and phrases based on user feedback. As a result, the harassment risk assessment system according to the embodiment can collect, analyze, and provide user communication data and learn based on the feedback, thereby quantifying and suggesting the risk of harassment.

[0068] The collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the timing of collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can immediately collect data and perform analysis in real time. Furthermore, if the user is busy, the collection unit can adjust the timing of collection to avoid interfering with the user's work. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The collection unit analyzes the user's past communication history and selects a collection method. For example, the collection unit prioritizes collection of communication means (email, telephone, etc.) that the user has frequently used in the past. The collection unit can also concentrate collection during specific time periods based on the user's past communication history. The collection unit can also analyze the user's past communication patterns and suggest the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past communication history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past communication history data into a generation AI and have the generation AI select the optimal collection method.

[0070] When collecting data, the collection unit filters the data based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting data related to the project the user is currently working on. The collection unit can also filter and collect related data based on the user's area of ​​interest. The collection unit can also collect only necessary data depending on the user's current work content. This makes it possible to collect highly relevant data by filtering data based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0071] When collecting data, the collection unit selects a collection means according to the user's input method. For example, when the user is communicating by voice, the collection unit prioritizes collecting voice data. Furthermore, when the user is communicating by text, the collection unit can also prioritize collecting text data. Furthermore, when the user is communicating using images, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0072] The collection unit estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. Furthermore, when the user is relaxed, the collection unit can also collect all data equally. Furthermore, when the user is busy, the collection unit can prioritize collecting work-related data. Thus, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0073] When collecting data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, if the user is traveling, the collection unit can also prioritize collecting data related to the user's destination. Furthermore, if the user is staying in a specific location, the collection unit can also prioritize collecting data related to that location. This enables efficient data collection by prioritizing the collection of highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0074] When collecting data, the collection unit analyzes the user's social media activities and collects related data. The collection unit collects related data based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables efficient data collection by analyzing the user's social media activities and collecting related data. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related data.

[0075] When collecting data, the collection unit customizes the collection method by reflecting the user's past feedback. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also select the optimal collection method based on the user's past feedback. The collection unit can also customize the collection timing and method by reflecting the user's feedback. This enables efficient data collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0076] The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation method of the analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0077] During analysis, the analysis unit adjusts the level of detail 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. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit applies different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to email data. The analysis unit can also apply a voice recognition algorithm to voice data. The analysis unit can also apply an image analysis algorithm to image data. This enables efficient analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0079] During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also dynamically adjust the analysis accuracy by referring to the user's past analysis results. This improves the analysis accuracy by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0080] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0081] During analysis, the analysis unit determines the analysis priority 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. The analysis unit can also dynamically adjust the analysis priority according to the time of data submission. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0082] During analysis, the analysis unit adjusts 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. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis 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, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

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

[0084] The providing unit estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible presentation method. Furthermore, if the user is relaxed, the providing unit can also provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a presentation method that focuses on the main points. This allows the presentation method to be adjusted according to the user's emotions, making it possible to provide information that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method.

[0085] The providing unit adjusts the level of detail of the provided information based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. The providing unit can also dynamically adjust the level of detail of the provided information according to the importance of the analysis result. This enables efficient information provision by adjusting the level of detail of the provided information based on the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the analysis result to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0086] The providing unit applies different providing algorithms depending on the category of the analysis results when providing the information. For example, the providing unit applies a text-based providing method to email analysis results. The providing unit can also apply an audio-based providing method to audio analysis results. The providing unit can also apply an image-based providing method to image analysis results. This enables efficient information provision by applying different providing algorithms depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the analysis results to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0087] The providing unit improves the accuracy of the provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also learn specific patterns from the user's past provision results and improve the accuracy of the provision. The providing unit can also dynamically adjust the accuracy of the provision by referring to the user's past provision results. This improves the accuracy of the provision by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0088] The providing unit estimates the user's emotions and adjusts the length of the information provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit provides a short, to-the-point information providing method. Furthermore, if the user is relaxed, the providing unit can provide a longer information providing method including detailed information. Furthermore, if the user is excited, the providing unit can provide a information providing method that adds visually stimulating effects. By adjusting the length of the information provided according to the user's emotions, it is possible to provide information that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the information provided.

[0089] The providing unit determines the priority of provision based on the submission time of the analysis results at the time of provision. For example, the providing unit provides the latest analysis results preferentially. The providing unit can also provide analysis results that were submitted earlier later. The providing unit can also dynamically adjust the priority of provision according to the submission time of the analysis results. This enables efficient information provision by determining the priority of provision based on the submission time of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the submission time of the analysis results to the generation AI and have the generation AI determine the priority of provision.

[0090] The providing unit adjusts the order of provision based on the relevance of the analysis results when providing the results. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit can also provide analysis results with low relevance later. The providing unit can also dynamically adjust the order of provision according to the relevance of the analysis results. This enables efficient information provision by adjusting the order of provision based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0091] The providing unit adjusts the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit provides a method of providing information that makes extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide a method of providing information in simple language. The providing unit can also dynamically adjust the expression of the method of providing information according to the user's level of expertise. This makes it possible to provide information that is easy for the user to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terminology.

[0092] The feedback unit estimates the user's emotions and adjusts the feedback collection method based on the estimated user emotions. For example, if the user is nervous, the feedback unit collects feedback in the form of a simple question. Furthermore, if the user is relaxed, the feedback unit can also request detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide a feedback format that allows for a quick response. This enables efficient feedback collection by adjusting the feedback collection method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0093] When collecting feedback, the feedback unit selects the optimal collection method by referring to the user's past feedback history. The feedback unit selects the optimal collection method, for example, based on the feedback format provided by the user in the past. The feedback unit can also preferentially use a specific question format based on the user's past feedback history. The feedback unit can also analyze the user's past feedback history and suggest the optimal collection method. This enables efficient feedback collection by selecting the optimal collection method by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select the optimal collection method.

[0094] The feedback unit customizes the content of the feedback based on the user's current situation when collecting the feedback. For example, if the user is busy, the feedback unit collects feedback in the form of a simple question. The feedback unit can also request detailed feedback if the user is relaxed. The feedback unit can also dynamically adjust the content of the feedback according to the user's current situation. This enables efficient feedback collection by customizing the content of the feedback based on the user's current situation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input data of the user's current situation to a generation AI and cause the generation AI to customize the content of the feedback.

[0095] The feedback unit improves the collection method by reflecting user feedback when collecting feedback. For example, the feedback unit adjusts the collection method based on feedback provided by the user. The feedback unit can also improve a specific question format based on user feedback. The feedback unit can also dynamically improve the collection method by reflecting user feedback. This enables efficient feedback collection by improving the collection method by reflecting user feedback. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input user feedback data into a generation AI and cause the generation AI to improve the collection method.

[0096] The feedback unit estimates the user's emotions and determines the priority of feedback based on the estimated user emotions. For example, if the user is nervous, the feedback unit prioritizes collecting feedback with high importance. Furthermore, if the user is relaxed, the feedback unit can also collect all feedback equally. Furthermore, if the user is busy, the feedback unit can prioritize collecting work-related feedback. This enables efficient feedback collection by determining the priority of feedback according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0097] When collecting feedback, the feedback unit selects the optimal collection method taking into account the user's geographical location information. For example, when the user is in a specific area, the feedback unit prioritizes collecting feedback related to that area. Furthermore, when the user is traveling, the feedback unit can also prioritize collecting feedback related to the user's destination. Furthermore, when the user is staying in a specific location, the feedback unit can also prioritize collecting feedback related to that location. This enables efficient feedback collection by selecting the optimal collection method taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal collection method.

[0098] When collecting feedback, the feedback unit analyzes the user's social media activity and suggests feedback content. The feedback unit, for example, suggests relevant feedback based on information shared by the user on social media. The feedback unit can also analyze the user's social media activity content and suggest relevant feedback. The feedback unit can also suggest relevant feedback based on the activity of the user's friends on social media. This enables efficient feedback collection by analyzing the user's social media activity and suggesting feedback content. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest feedback content.

[0099] When collecting feedback, the feedback unit customizes the collection method by reflecting the user's past feedback. The feedback unit adjusts the collection method based on, for example, feedback provided by the user in the past. The feedback unit can also select the optimal collection method based on the user's past feedback. The feedback unit can also customize the collection timing and method by reflecting the user's feedback. This enables efficient feedback collection by customizing the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0100] The learning unit estimates the user's emotions and selects training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed training data. Furthermore, if the user is in a hurry, the learning unit can select training data that focuses on the main points. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This enables efficient learning by selecting training data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit may input the user's emotion data into the generation AI and have the generation AI select the training data.

[0101] During learning, the learning unit adjusts the learning algorithm by referring to past learning data. The learning unit adjusts the learning algorithm based on, for example, past learning data. The learning unit can also learn specific patterns from past learning data and optimize the algorithm. The learning unit can also dynamically optimize the learning algorithm by referring to past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to adjust the learning algorithm.

[0102] During learning, the learning unit updates the learning data to reflect user feedback. The learning unit updates the learning data based on, for example, feedback provided by the user. The learning unit can also learn specific patterns from user feedback and update the data. The learning unit can also dynamically update the learning data to reflect user feedback. This enables efficient learning by updating the learning data to reflect user feedback. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user feedback data into the generation AI and cause the generation AI to update the learning data.

[0103] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. For example, the learning unit increases the learning frequency when the user is relaxed. The learning unit can also decrease the learning frequency when the user is in a hurry. The learning unit can also dynamically adjust the learning frequency when the user is excited. This enables efficient learning by adjusting the learning frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the learning frequency.

[0104] During learning, the learning unit weights the learning data based on the time of data submission. For example, the learning unit assigns a higher weight to the most recent data. The learning unit can also assign a lower weight to data that was submitted earlier. The learning unit can also dynamically adjust the weighting of the learning data depending on the time of data submission. This enables efficient learning by weighting the learning data based on the time of data submission. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of data submission to the generation AI and have the generation AI perform weighting of the learning data.

[0105] During learning, the learning unit integrates information from different data sources to enrich the learning data. For example, the learning unit integrates email data and voice data to enrich the learning data. The learning unit can also integrate social media data and call history to enrich the learning data. The learning unit can also integrate information from different data sources to dynamically enrich the learning data. This enables efficient learning by integrating information from different data sources to enrich the learning data. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit may input information from different data sources into the generation AI and cause the generation AI to integrate the information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, feedback unit, and learning 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 collection unit can collect emails, meeting records, and call histories using the camera 42 and microphone 38B of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and quantifies the risk of harassment. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the analysis results to the user. The feedback unit, realized, for example, by the control unit 46A of the smart device 14, accepts user feedback. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, allows the AI ​​to learn based on the feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, feedback unit, and learning unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect emails, meeting records, and call histories using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and quantifies the risk of harassment. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the analysis results to the user. The feedback unit, realized, for example, by the control unit 46A of the smart glasses 214, accepts user feedback. The learning unit, realized, for example, by the specific processing unit 290 of the data processing device 12, allows the AI ​​to learn based on the feedback. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, feedback unit, and learning unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect emails, meeting records, and call histories using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and quantifies the risk of harassment. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the analysis results to the user. The feedback unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and accepts user feedback. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the AI ​​learns based on the feedback. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, feedback unit, and learning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect emails, meeting records, and call histories using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data and quantifies the risk of harassment. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the analysis results to the user. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and accepts user feedback. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the AI ​​learns based on the feedback.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can also estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analysis of data with high importance. Also, if the user is relaxed, it can analyze all data equally. Furthermore, if the user is busy, it can prioritize analysis of data related to work. In this way, by determining the analysis priority according to the user's emotions, it is possible to prioritize analysis of important data.

[0108] The providing unit can also estimate the user's emotions and adjust the timing of providing information based on the estimated user emotions. For example, if the user is nervous, the timing of providing information can be delayed to reduce the burden on the user. Also, if the user is relaxed, the information can be provided immediately, enabling a real-time response. Furthermore, if the user is busy, the timing of providing information can be adjusted to avoid interfering with the user's work. In this way, the burden on the user can be reduced by adjusting the timing of providing information according to the user's emotions.

[0109] The feedback unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can collect feedback in the form of simple questions. If the user is relaxed, the feedback unit can request detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide a feedback format that can be answered in a short time. This allows for efficient feedback collection by adjusting the feedback collection method according to the user's emotions.

[0110] The learning unit can also estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is relaxed, detailed learning data can be selected. If the user is in a hurry, learning data that focuses on the main points can be selected. Furthermore, if the user is excited, visually stimulating learning data can be selected. This allows for efficient learning by selecting learning data according to the user's emotions.

[0111] The analysis unit can also estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that are concise. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0112] The collection unit can also analyze the user's past communication history and select a collection method. For example, it can prioritize collection of communication methods (email, telephone, etc.) that the user has frequently used in the past. The collection unit can also concentrate collection during specific time periods based on the user's past communication history. Furthermore, the collection unit can analyze the user's past communication patterns and suggest the optimal collection method. This enables efficient data collection by selecting the optimal collection method based on the user's past communication history.

[0113] The providing unit can also adjust the level of detail to be provided based on the importance of the analysis result when providing the information. For example, detailed information is provided for analysis results with high importance. The providing unit can also provide simplified information for analysis results with low importance. Furthermore, the providing unit can dynamically adjust the level of detail to be provided depending on the importance of the analysis result. This allows for efficient information provision by adjusting the level of detail to be provided based on the importance of the analysis result.

[0114] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to email data. The analysis unit can also apply a voice recognition algorithm to voice data. Furthermore, the analysis unit can also apply an image analysis algorithm to image data. This allows for efficient analysis by applying different analysis algorithms depending on the data category.

[0115] When collecting feedback, the feedback unit can also select the optimal collection method by referring to the user's past feedback history. For example, the optimal collection method can be selected based on the feedback format provided by the user in the past. The feedback unit can also preferentially use a specific question format based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest the optimal collection method. In this way, efficient feedback collection is possible by selecting the optimal collection method by referring to the user's past feedback history.

[0116] During learning, the learning unit can also adjust the learning algorithm by referring to past learning data. For example, the learning algorithm is adjusted based on past learning data. The learning unit can also learn specific patterns from past learning data and optimize the algorithm. Furthermore, the learning unit can dynamically optimize the learning algorithm by referring to past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects emails, meeting records, and call histories. For example, email data is collected when a user enters email content into the system. The collection unit can also collect recording data of online meetings and telephone call histories. For example, meeting data is collected when a user uploads recording data of a meeting. Step 2: The analysis unit analyzes the data collected by the collection unit and quantifies the risk of harassment. For example, it can use natural language processing technology to analyze the content of emails and speech recognition technology to analyze recorded meeting data. It can also analyze phone call histories to detect specific keywords and phrases and quantify the risk of harassment. Step 3: The providing unit provides the analysis results obtained by the analysis unit to the user. For example, the analysis results can be displayed as numerical values ​​or as graphs or charts. Furthermore, the analysis results can be sent to the user by email or notification. For example, the providing unit displays the analysis results on a dashboard so that the user can check them in real time. Step 4: The feedback unit accepts user feedback. For example, the feedback unit accepts feedback by allowing the user to enter feedback, evaluation, or comments on the analysis results. For example, the feedback unit accepts feedback by allowing the user to say, "This comment is not harassment" on the analysis results. Step 5: The learning unit allows the AI ​​to learn based on the feedback received by the feedback unit. For example, based on user feedback, the AI ​​can adjust the analysis algorithm, update the analysis model, and improve analysis accuracy. For example, it can adjust the importance of specific keywords or phrases.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects email or meeting records and call history; an analysis unit that analyzes the data collected by the collection unit and quantifies the risk of harassment; a providing unit that provides a user with the analysis results obtained by the analysis unit; a feedback unit for receiving user feedback; A learning unit that causes the AI ​​to learn based on the feedback received by the feedback unit. A system characterized by:

2. The collecting unit Estimate user emotions and adjust data collection timing based on the estimated user emotions The system of claim 1 .

3. The collecting unit Analyze the user's past communication history and select the collection method The system of claim 1 .

4. The collecting unit As data is collected, it is filtered based on the user's current projects and areas of interest. The system of claim 1 .

5. The collecting unit When collecting data, select the collection method according to the user's input method The system of claim 1 .

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions The system of claim 1 .

7. The collecting unit When collecting data, prioritize collecting the most relevant data by taking into account the user's geographic location. The system of claim 1 .

8. The collecting unit At the time of data collection, analyze your social media activity and collect relevant data The system of claim 1 .

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