system

Abstract

We provide the system. [Solution] A means for an information processing device to collect data generated from user activities, A means of analyzing data collected by an information processing device and evaluating the user's capabilities, A means by which an information processing device proposes the optimal role within its components based on an assessment of the user's capabilities, A means by which an information processing device updates its capability evaluation model using user feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, when reorganizing an organization or arranging personnel within a company, the individual's work skills have often been judged subjectively. For this reason, the right person is not always placed in the right position, and the individual's abilities may not be fully exerted. In addition, there is a problem that efficient utilization of human resources cannot be achieved due to the mismatch between the skills required by the company and the skills possessed by individuals. As a result, there is a problem that the productivity of the organization and the job satisfaction of individuals decrease.

Means for Solving the Problems

[0005] This invention provides a system that objectively evaluates users' work skills by collecting data generated from their work activities using an information processing device and analyzing that data. Based on this evaluation, it proposes the most suitable job for the user, thereby maximizing the use of individual skills. Furthermore, it continuously updates the skill evaluation model based on user feedback, enabling more accurate job recommendations. In addition, by using natural language processing technology for analysis, it can efficiently extract detailed skill information from text data and provide real-time recommendations that respond to job requirements within the organization.

[0006] An "information processing device" is a device that collects and analyzes data generated from the user's work activities and has the function of performing skill evaluations and making job recommendations.

[0007] "Users" refers to individuals who use this system to receive evaluations of their work skills and job recommendations.

[0008] "Business activities" refer to the tasks and work processes that users perform on a daily basis, and are the activities that are subject to data collection.

[0009] "Data" refers to information generated from users' work activities, and the materials used for skill assessments based on this data.

[0010] "Analysis" refers to the calculation process used to quantify and evaluate users' work skills based on collected data.

[0011] "Business skills" refer to the abilities and knowledge that users demonstrate when performing their duties.

[0012] "Skill evaluation" refers to an index that quantifies the results of an analysis in order to objectively judge a user's work skills.

[0013] "Job suggestion" refers to the act of presenting appropriate job roles within an organization based on an assessment of the user's skills.

[0014] "Feedback" refers to information provided by users as a response to job proposals, which is useful for improving the accuracy of the system.

[0015] "Skill evaluation model" refers to a collection of criteria and algorithms for evaluating the business skills of users.

[0016] "Natural language processing technology" refers to the technology used to analyze text data, which enables the extraction of characteristic information.

[0017] "Text data" refers to character information collected in the form of emails, chats, etc.

[0018] "Job requirements within an organization" refers to indicators showing the skills and personnel requirements sought by the organization.

[0019] "Real-time" means that the processing is executed immediately and the results are reflected immediately.

Brief Description of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0022] First, the terms used in the following description will be described.

[0023] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units 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), and the like.

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0033] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0040] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0041] As an embodiment of the present invention, a system is provided in which an information processing device, i.e., a server, plays a central role. This system begins with a terminal collecting various digital data generated from the user's daily work activities. Specifically, this includes data from emails, chat tools, and task management applications. This data is collected with the user's consent and transmitted to the server.

[0042] The server analyzes the received data and extracts important features from the text data using natural language processing techniques. This analysis quantifies the skills that users demonstrate in their work, and a skill evaluation is conducted. The skill evaluation is expressed as scores for various work skills, such as communication skills, problem-solving skills, and project management skills.

[0043] Subsequently, the server, based on the user's skill assessment, proposes job positions that match the company's job requirements. This increases the likelihood that the user will be placed in a position where they can make the most of their skills. The proposed job is notified to the user's terminal and provided along with detailed information.

[0044] Furthermore, users can provide feedback on the proposed job roles. This feedback is stored on the server and used to improve the skill assessment model, thereby enhancing the accuracy of future proposals. This cycle is designed to respond in real time to structural changes within the organization.

[0045] As a concrete example, suppose a new project is launched within a company. A terminal captures the deliverables and conversations the user has performed in the course of their work, and the server analyzes this data. The server then reveals that the user has high project management skills. Based on this information, the server can propose a project manager position to the user. If the user accepts this proposal, their feedback will be used to improve the accuracy of future analyses.

[0046] Thus, the embodiments of the present invention realize a system that provides the greatest benefit to both the user and the organization through a series of processes including data collection, analysis, evaluation, and proposal.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The device collects data generated from the user's work activities. Specifically, it captures data from various platforms used by the user, such as email, chat tools, and task management applications.

[0050] Step 2:

[0051] The device sends the collected data to the server. The data is transferred to the server using a secure protocol.

[0052] Step 3:

[0053] The server uses natural language processing techniques to extract keywords and characteristic phrases from the text data for the purpose of analyzing the received data.

[0054] Step 4:

[0055] The server evaluates the user's work skills based on the information obtained through analysis. The evaluation is quantified as scores, for example, for problem-solving ability and communication skills.

[0056] Step 5:

[0057] The server uses the user's skill assessment to propose job opportunities within the organization. These job proposals, along with a job overview and required skills, are sent to the user's device.

[0058] Step 6:

[0059] Users evaluate the proposed job and provide feedback. This feedback is sent to the server.

[0060] Step 7:

[0061] The server analyzes the feedback provided and updates the skills assessment model. This update will be reflected in improving the accuracy of future job proposals.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In recent years, organizations have been required to make optimal use of human resources. However, traditional methods have not adequately provided means to quantitatively evaluate the work capabilities of users and propose appropriate job assignments based on those evaluations. This has resulted in situations where users are unable to make the most of their abilities, leading to a decline in the overall efficiency and productivity of the organization.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for an information processing device to collect digital information generated from the user's work activities, means for the information processing device to analyze the collected digital information and evaluate the user's work capabilities using natural language processing technology, and means for the information processing device to propose job placement within the organization based on the evaluation of the user's work capabilities. This makes it possible to accurately evaluate the user's capabilities and propose the optimal job placement in real time based on that evaluation.

[0067] An "information processing device" is a system consisting of hardware or software for receiving, analyzing, and evaluating digital information.

[0068] "User's work activities" refers to actions and operations related to work tasks and communication that users perform on a daily basis.

[0069] "Digital information" refers to electronically recorded data generated from users' work activities, including information obtained from emails, chats, task management tools, etc.

[0070] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand and analyze the language that humans use on a daily basis.

[0071] "Job competence" refers to the collection of skills, knowledge, and know-how that a user demonstrates in their job, and is subject to quantitative evaluation.

[0072] "Job placement suggestion" refers to a process that recommends jobs that best utilize the user's abilities, based on their work capabilities.

[0073] A "generative AI model" refers to a model that uses artificial intelligence technology to learn how to extract useful patterns and information from large amounts of data.

[0074] "Feedback" refers to opinions, impressions, and evaluations provided by users, and is information that can be used to improve the system.

[0075] Modes for carrying out the invention

[0076] This system, centered around an information processing device, processes digital information related to users' work activities to propose optimal job assignments. Specifically, servers and terminals each play their respective roles, and processes are executed accordingly.

[0077] The server functions as an information processing device, receiving digital information about work activities sent by users. This received digital information includes work-related data obtained from email clients, chat applications, task management tools, etc. The server can analyze this data using generative AI models and natural language processing techniques to evaluate the user's work capabilities. In this process, the generative AI model uses a comprehensive data analysis algorithm to extract effective patterns and features from the text information and score the work capabilities.

[0078] The device is responsible for collecting digital information generated from the user's daily work activities. This is done with the user's confirmation and consent, thus protecting privacy. The information collected from the device is then transmitted to the server in accordance with security protocols.

[0079] Users can also receive job placement suggestions from this system. Based on the job skills analyzed by the server, appropriate jobs are presented to the user. These suggestions can effectively utilize the user's skills by being compared with job requirements within the organization in real time. Users send their opinions on the presented jobs as feedback to the server, and this feedback helps improve the accuracy of the skill assessment model.

[0080] As a concrete example, in a company seeking a leader for a new project, a terminal collects the user's work data, and a server analyzes that data. If the user is deemed to have strong project management skills, the server can propose a project leader position. If this proposal is accepted, the user's feedback plays a role in improving the accuracy of future proposals.

[0081] An example of a prompt message to be input into the generating AI model is, "Analyze the user's work data and suggest the most suitable job position." This allows the user to make the most of their abilities and find the most suitable job.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The terminal collects digital information generated from the user's daily work activities. Input data comes from software such as email, chat, and task management tools. This data includes the user's work-related messages, task completion status, and meeting notes. The terminal securely collects this data and prepares it for transmission to the server.

[0085] Step 2:

[0086] The terminal sends the collected digital information to the server. The input is the digital information obtained in step 1, and the output is the transfer of that information to the server using a secure communication protocol. Here, the terminal uses SSL / TLS to transmit the data while protecting its confidentiality.

[0087] Step 3:

[0088] The server analyzes the received digital information. The input is unanalyzed digital information received from the terminal, and the server uses a generative AI model to preprocess the data. First, the server tokenizes the data, removes unnecessary words, and then extracts semantic information from the text using natural language processing techniques. The output is a set of features that represent the user's business capabilities.

[0089] Step 4:

[0090] The server evaluates the user's work capabilities based on the analysis results. The input is the features extracted by the analysis in step 3, and the output is a score for multiple work capabilities evaluated based on those features. The server quantifies these scores and evaluates the user's communication skills, problem-solving abilities, etc.

[0091] Step 5:

[0092] The server proposes job placements based on the user's work capabilities. The input is the work capability score obtained in step 4, and the output is information suggesting the most suitable job position for the user. Using a generative AI model, the system compares job requirements with the score in real time and notifies the user's terminal of the suggestions.

[0093] Step 6:

[0094] Users send feedback on proposed job roles to the server. Input is the user's opinions and impressions, and output is feedback data stored on the server. Users provide feedback via their terminals, and this information is used to improve the skill assessment model for future projects.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In local communities, there is a challenge in achieving more efficient and smoother community activities by ensuring that each member takes on the most appropriate role. However, accurately understanding the diverse skills and abilities of each member and proposing the most suitable role is difficult.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for an information processing device to collect data generated from the user's activities, means for the information processing device to analyze the collected data and evaluate the user's capabilities, and means for the information processing device to propose the optimal role within the components based on the evaluation of the user's capabilities. This makes it possible to propose the optimal role according to the individual skills and abilities of each member.

[0100] An "information processing device" is a device such as a computer or server used to receive and analyze electronic data.

[0101] "User" refers to an individual or member whose data is collected by an information processing device.

[0102] "Data generated from activities" refers to textual and numerical information obtained from users' daily actions and communications.

[0103] "Ability" refers to the practical skills and experience that a user possesses.

[0104] "Optimal role within a component" refers to the role or position within an organization or community that best suits an individual's abilities.

[0105] "Response" refers to the feedback that a user provides to an information processing device regarding the role it proposes.

[0106] "Methods for updating competency assessment models using user responses" refers to the process of improving the accuracy and effectiveness of suggestions displayed based on user responses.

[0107] In a mode for carrying out the invention, the system that implements this application functions as follows: First, the terminal collects digital data generated from the user's daily activities. This data includes emails, chat tools, and community activity logs. The collected data is automatically sent to the server.

[0108] The server uses natural language processing techniques to analyze the received data. This analysis process utilizes Python's natural language processing libraries, such as spaCy and NLTK. This allows for the quantification of the user's abilities and skills. This skill assessment uses machine learning algorithms, such as K-means clustering, to determine skill similarity and grouping. Furthermore, the Flask framework is used to build the server-side application logic.

[0109] The server then suggests the most suitable role within the organization or community based on the user's skill assessment. For example, if a user is assessed as having strong communication skills in a local cleanup activity, they may be suggested to take on a leadership role. Feedback from users who accept the suggestion is used to improve the accuracy of future suggestions.

[0110] Furthermore, this system has the functionality to instantly receive user feedback and update its competency assessment model. This update is managed by a SQLite-based database, resulting in more appropriate role suggestions.

[0111] The generative AI model is input using the following prompts:

[0112] "Analyze the user's communication skills and suggest the next recommended community activity role. Considering the user's past experiences of acceptance and successful leadership, suggest the next most suitable role."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The device collects digital data about the user's daily activities. It accepts data such as emails, chat tools, and local activity logs as input. This data provides information about the user's activities and is sent to a database.

[0116] Step 2:

[0117] The server receives digital data transmitted from the terminal and analyzes it using natural language processing technology. The input is user activity data, and the output is a numerical evaluation of the user's skills and abilities. The spaCy library is used for the analysis to extract important information from the text.

[0118] Step 3:

[0119] The server analyzes the similarity of skills using K-means clustering based on the user's skill evaluation obtained through analysis. The input is numerical skill evaluation data, and the output is the skill category to which the user belongs. This visualizes the characteristics of the abilities that the user possesses.

[0120] Step 4:

[0121] The server suggests the most suitable role within an organization or community based on skill categories. The input is skill category information, and the output is suggested roles or positions. This suggestion is communicated to the user via the Flask framework.

[0122] Step 5:

[0123] The user provides feedback on whether they accept the proposed role. The input is the user's selection and feedback information, and the output is sent to the server as feedback data.

[0124] Step 6:

[0125] The server updates its capability assessment model based on user feedback. The input is feedback data, and the output is the updated capability assessment model. This improves the model's accuracy and optimizes future suggestions.

[0126] Step 7:

[0127] The server generates new prompt sentences using a generative AI model, continuously learning the model. The input is past feedback and role suggestion data, and the output is an improved prompt sentence for the next suggestion.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] As an embodiment of the present invention, a system is constructed in an information processing device that utilizes business activity data and emotion data to evaluate the user's business skills and propose the most suitable job. This system consists of a terminal, a server, and an emotion engine.

[0130] The device simultaneously collects digital data from the user's daily work activities and emotional data derived from their interactions. This includes, for example, text data from emails, chat tools, and task management tools used by the user, as well as metadata such as voice and facial expressions.

[0131] The server receives data from the terminal and analyzes the text data using natural language processing technology. This analysis quantifies the user's skills in performing their tasks, and a skill evaluation is conducted. The skills evaluated cover a wide range, including problem-solving ability, communication skills, and project management skills.

[0132] In parallel, the emotion engine analyzes the collected emotion data to understand the user's emotional state. The server integrates this emotion assessment with job skills assessment to create a more refined profile. This profile makes it possible to suggest jobs and work environments that allow the user to perform their duties comfortably and effectively.

[0133] Proposed job assignments are notified to users via their devices. These notifications include an overview of the required skills and work environment, allowing users to accept or reject the assignment based on their own judgment. Users can also provide feedback on the proposals, which is stored on the server and used to improve the accuracy of skill evaluation and sentiment analysis models.

[0134] For example, if a user exhibits a calm emotional state after a team meeting and the content of the conversation indicates that they demonstrated advanced problem-solving abilities, the server can suggest a project leader position that would allow them to utilize their management skills. In this way, the present invention provides a system that enables job suggestions that take into account both the user's skills and emotions, thereby maximizing the mutual benefit of both the organization and the user.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The device collects text data and emotional data obtained from audio and video generated during the user's work activities. For example, it records email and chat history, as well as audio and facial expressions during video conferences.

[0138] Step 2:

[0139] The terminal transmits the collected data to the server using a secure communication protocol. This data includes information about the work performed and metadata that reflects emotions.

[0140] Step 3:

[0141] The server uses natural language processing techniques to analyze the text data. This allows it to quantify the skills that users demonstrate in their work, such as communication and problem-solving abilities.

[0142] Step 4:

[0143] The server uses an emotion engine to analyze emotional data and evaluate the user's emotional state. This includes information such as whether the user is stressed or relaxed.

[0144] Step 5:

[0145] The server integrates skill assessment results and emotional assessment results to generate a user profile. Based on this profile, it identifies jobs in which the user can perform well.

[0146] Step 6:

[0147] The server suggests appropriate job roles to the user. These suggestions include job descriptions, required skills, and work environment, and are communicated to the user via their terminal.

[0148] Step 7:

[0149] Users decide whether to accept the proposed job and provide feedback. Feedback is optional and should convey the user's preferences and concerns.

[0150] Step 8:

[0151] The server receives user feedback and uses it to improve the accuracy of its skill assessment and sentiment analysis models. This contributes to improving the quality of future assessments and suggestions.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0154] In modern organizations, there is a need to accurately understand the work performance capabilities and emotional state of individual users and propose the most suitable job based on that understanding. However, conventional systems have fragmented evaluations of skills and emotions derived from work activities, making it difficult to improve the accuracy of job proposals and user satisfaction.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for a data collection terminal to collect electronic and emotional information generated from the user's daily work activities, means for analyzing the collected information and quantifying and evaluating the user's work performance ability using natural language processing technology, and means for an emotion analysis device to grasp the user's emotional state and create a profile by integrating it with the work performance ability evaluation. This makes it possible to comprehensively evaluate the user's work skills and emotional state and propose individually optimized job roles.

[0157] A "data collection terminal" is a device used to acquire electronic and emotional information generated from users' daily work activities.

[0158] A "server" is a central computing device that analyzes information obtained from data collection terminals and quantifies and evaluates the user's ability to perform their duties.

[0159] "Natural language processing technology" is a technology that analyzes text data generated by users to understand its meaning and context.

[0160] An "emotion analysis device" is a device that analyzes data such as voice and facial expressions to evaluate the emotional state of a user.

[0161] A "profile" is evaluation data that integrates a user's work performance capabilities and emotional state, and includes information to provide optimal job recommendations.

[0162] "Job suggestion" is the act of presenting an appropriate job within an organization based on the user's work skills and emotional state.

[0163] "Feedback" refers to the act of providing information about whether a user will accept a job proposal, or providing opinions on the proposal.

[0164] To implement this invention, a system is constructed that combines a data collection terminal, a server, and an emotion analysis device. The data collection terminal collects electronic and emotional information from the user's daily work activities. This includes text data from emails and chat tools, as well as metadata including voice and facial expressions. Specifically, the terminal acquires voice and facial expression data via a camera and microphone and transmits it to the server.

[0165] The server analyzes text data using an application that implements natural language processing technology. This makes it possible to evaluate users' work performance capabilities with concrete numerical values. Natural language processing libraries and machine learning models are used for the analysis.

[0166] Emotion analysis devices analyze changes in voice tone and facial expressions to quantify the user's emotional state. This analysis utilizes emotion recognition software and algorithms, enabling real-time evaluation.

[0167] The server creates a user profile based on the analysis results. This profile incorporates job performance capabilities and emotional evaluations, and is used as basic data when making job recommendations. Based on the generated profile, the server selects the most suitable job and notifies the user via the terminal.

[0168] For example, if a user is evaluated as having strong team management skills and a calm emotional state, they may be proposed for a project leader position. This proposal, in turn, can contribute to the efficient allocation of personnel within the organization.

[0169] An example of a prompt for a generative AI model would be: "Please explain in detail a system that suggests the most suitable job for a person based on information obtained from their daily work data."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The device collects text data and metadata from the user's daily work. Input comes from email, chat, and task management tools used by the user. The device acquires this data and collects metadata such as voice and facial expressions using its camera and microphone. This process yields a comprehensive dataset of the user's work activities.

[0173] Step 2:

[0174] The server receives data sent from the terminal and analyzes the text data using natural language processing technology. The input is the text data collected in step 1. Specifically, the analysis model extracts important keywords and phrases from the text and uses them to quantify and evaluate the user's work performance ability. The output of this process is numerical data of the evaluated skills.

[0175] Step 3:

[0176] The emotion analysis device analyzes voice and facial expression data to quantify the user's emotional state. The input is the voice and facial expression data collected in step 1. In this step, the emotion recognition algorithm analyzes the data in real time and graphs or quantifies the user's emotional state. As a result, the output is evaluation data indicating the user's emotional state.

[0177] Step 4:

[0178] The server integrates work performance evaluation data and emotional evaluation data to create a user profile. The input is the output data from steps 2 and 3. The server integrates this data to generate a profile that shows the user's strengths and job suitability. This output is detailed profile information of the user.

[0179] Step 5:

[0180] The server selects the most suitable job based on this information and notifies the user of the suggestion via the terminal. The input is the profile information generated in step 4. The server evaluates the profile, selects a job that the user can comfortably perform, and sends the suggestion to the terminal. The output is a notification message containing detailed information about the job.

[0181] Step 6:

[0182] The user accepts or rejects the job offer notified from the terminal. The input is the job offer received from the server in step 5. The user can review the job offer and make a selection. This selection is sent from the terminal to the server as feedback. As output, the feedback data is stored on the server and used to improve future job offers.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] In modern living environments, there is a need for systems that can suggest activities that take into account individual abilities and emotional states. However, existing technologies struggle to efficiently analyze users' abilities and emotional states and suggest optimal activities. Furthermore, there are challenges in appropriately suggesting activities within the home and continuously improving the system using feedback.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for an information processing device to collect data generated from the user's activities, means for the information processing device to analyze the collected data and evaluate the user's abilities, and means for the information processing device to suggest activities within the home. This makes it possible to suggest optimal activities for each individual user, taking into account their emotional state.

[0188] An "information processing device" is a technological device used to collect data generated from user activities and to analyze that data.

[0189] "Data collection" is the process of gathering information generated from users' daily activities.

[0190] "Ability assessment" is the process of quantifying a user's abilities based on collected data and analyzing the results.

[0191] "Activity proposal" refers to the act of recommending the most suitable activity for the user based on an analyzed ability assessment.

[0192] "Emotional state" refers to the user's mental health and emotional condition, determined by analyzing information from the user's facial expressions and voice.

[0193] "Feedback" is a method of collecting responses and reactions from users and using them to improve the system.

[0194] An "evaluation model" is a set of algorithms and methodologies used to analyze a user's abilities and emotional state.

[0195] This invention provides a system that utilizes an information processing device, particularly a robot used in the home, to analyze data collected from a user's activities and, based on that analysis, suggests the most suitable activities. This system uses a terminal installed in the home, a server, and, if necessary, an emotion analysis engine.

[0196] The device collects various types of data from the user's daily life. This includes text data, voice data, and facial expression data. For example, it can collect recordings of conversations the user has on a daily basis and data from sensors in smart home devices.

[0197] The server processes the data acquired from the terminal. This processing utilizes natural language processing techniques to evaluate the user's abilities through the analysis of text and audio information contained in the data. Possible software to be used includes, for example, Python and its library, TextBlob.

[0198] The emotion analysis engine analyzes acquired voice and facial expression data to determine the user's emotional state. This allows the system to understand whether the user is stressed or relaxed, enabling it to suggest more appropriate activities.

[0199] For example, if it is determined from a conversation within the home that the user is tired, relaxation activities can be suggested. This suggestion generates appropriate activities based on a generative AI model and notifies the user. An example of a prompt sentence to input into the generative AI model is, "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members who are feeling stressed or tired." Following this prompt sentence, the system automatically selects appropriate activities.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The device collects data about the user's daily activities within the home. Inputs include the user's conversations, voice, and sensor data from smart devices. This data is stored in a local database.

[0203] Step 2:

[0204] The terminal sends this collected data to the server. The input is the data collected in step 1. The transmitted data is received by the server's receiving module for analysis.

[0205] Step 3:

[0206] The server analyzes the received data using natural language processing techniques to process both text and audio data. The input is the data received in step 2. For text data, it extracts emotions and important keywords using libraries such as TextBlob, and for audio data, it uses a speech analysis engine to estimate speech tone and emotions. The analysis results are output as skill and emotion assessments.

[0207] Step 4:

[0208] The server uses a generating AI model to suggest the most suitable activities for the user based on the analysis results. The input is the analysis results from step 3. Using the prompt "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members experiencing stress or fatigue," the AI ​​model generates specific activity suggestions. The generated suggestions are then output.

[0209] Step 5:

[0210] The terminal notifies the user of activity suggestions sent from the server. The input is the activity suggestions generated in step 4. The suggestions are delivered to the user visually or audibly, and the user can choose an action based on the suggestions.

[0211] Step 6:

[0212] Users provide feedback on suggested activities. Input includes data on user choices and satisfaction levels. The device collects this feedback and sends it to the server. This feedback data is used to further improve the system.

[0213] Step 7:

[0214] The server uses the collected feedback data to improve the evaluation model. The input is the feedback data received in step 6. This data is used to adjust the parameters of the skill evaluation model and the sentiment analysis model, aiming to improve the accuracy of suggestions in subsequent attempts. The output is the generated updated model.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] As an embodiment of the present invention, a system is provided in which an information processing device, i.e., a server, plays a central role. This system begins with a terminal collecting various digital data generated from the user's daily work activities. Specifically, this includes data from emails, chat tools, and task management applications. This data is collected with the user's consent and transmitted to the server.

[0232] The server analyzes the received data and extracts important features from the text data using natural language processing techniques. This analysis quantifies the skills that users demonstrate in their work, and a skill evaluation is conducted. The skill evaluation is expressed as scores for various work skills, such as communication skills, problem-solving skills, and project management skills.

[0233] Subsequently, the server, based on the user's skill assessment, proposes job positions that match the company's job requirements. This increases the likelihood that the user will be placed in a position where they can make the most of their skills. The proposed job is notified to the user's terminal and provided along with detailed information.

[0234] Furthermore, users can provide feedback on the proposed job roles. This feedback is stored on the server and used to improve the skill assessment model, thereby enhancing the accuracy of future proposals. This cycle is designed to respond in real time to structural changes within the organization.

[0235] As a concrete example, suppose a new project is launched within a company. A terminal captures the deliverables and conversations the user has performed in the course of their work, and the server analyzes this data. The server then reveals that the user has high project management skills. Based on this information, the server can propose a project manager position to the user. If the user accepts this proposal, their feedback will be used to improve the accuracy of future analyses.

[0236] Thus, the embodiments of the present invention realize a system that provides the greatest benefit to both the user and the organization through a series of processes including data collection, analysis, evaluation, and proposal.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The device collects data generated from the user's work activities. Specifically, it captures data from various platforms used by the user, such as email, chat tools, and task management applications.

[0240] Step 2:

[0241] The device sends the collected data to the server. The data is transferred to the server using a secure protocol.

[0242] Step 3:

[0243] The server uses natural language processing techniques to extract keywords and characteristic phrases from the text data for the purpose of analyzing the received data.

[0244] Step 4:

[0245] The server evaluates the user's work skills based on the information obtained through analysis. The evaluation is quantified as scores, for example, for problem-solving ability and communication skills.

[0246] Step 5:

[0247] The server uses the user's skill assessment to propose job opportunities within the organization. These job proposals, along with a job overview and required skills, are sent to the user's device.

[0248] Step 6:

[0249] Users evaluate the proposed job and provide feedback. This feedback is sent to the server.

[0250] Step 7:

[0251] The server analyzes the feedback provided and updates the skills assessment model. This update will be reflected in improving the accuracy of future job proposals.

[0252] (Example 1)

[0253] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0254] In recent years, organizations have been required to make optimal use of human resources. However, traditional methods have not adequately provided means to quantitatively evaluate the work capabilities of users and propose appropriate job assignments based on those evaluations. This has resulted in situations where users are unable to make the most of their abilities, leading to a decline in the overall efficiency and productivity of the organization.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for an information processing device to collect digital information generated from the user's work activities, means for the information processing device to analyze the collected digital information and evaluate the user's work capabilities using natural language processing technology, and means for the information processing device to propose job placement within the organization based on the evaluation of the user's work capabilities. This makes it possible to accurately evaluate the user's capabilities and propose the optimal job placement in real time based on that evaluation.

[0257] An "information processing device" is a system consisting of hardware or software for receiving, analyzing, and evaluating digital information.

[0258] "User's work activities" refers to actions and operations related to work tasks and communication that users perform on a daily basis.

[0259] "Digital information" refers to electronically recorded data generated from users' work activities, including information obtained from emails, chats, task management tools, etc.

[0260] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand and analyze the language that humans use on a daily basis.

[0261] "Job competence" refers to the collection of skills, knowledge, and know-how that a user demonstrates in their job, and is subject to quantitative evaluation.

[0262] "Job placement suggestion" refers to a process that recommends jobs that best utilize the user's abilities, based on their work capabilities.

[0263] A "generative AI model" refers to a model that uses artificial intelligence technology to learn how to extract useful patterns and information from large amounts of data.

[0264] "Feedback" refers to opinions, impressions, and evaluations provided by users, and is information that can be used to improve the system.

[0265] Modes for carrying out the invention

[0266] This system, centered around an information processing device, processes digital information related to users' work activities to propose optimal job assignments. Specifically, servers and terminals each play their respective roles, and processes are executed accordingly.

[0267] The server functions as an information processing device, receiving digital information about work activities sent by users. This received digital information includes work-related data obtained from email clients, chat applications, task management tools, etc. The server can analyze this data using generative AI models and natural language processing techniques to evaluate the user's work capabilities. In this process, the generative AI model uses a comprehensive data analysis algorithm to extract effective patterns and features from the text information and score the work capabilities.

[0268] The device is responsible for collecting digital information generated from the user's daily work activities. This is done with the user's confirmation and consent, thus protecting privacy. The information collected from the device is then transmitted to the server in accordance with security protocols.

[0269] Users can also receive job placement suggestions from this system. Based on the job skills analyzed by the server, appropriate jobs are presented to the user. These suggestions can effectively utilize the user's skills by being compared with job requirements within the organization in real time. Users send their opinions on the presented jobs as feedback to the server, and this feedback helps improve the accuracy of the skill assessment model.

[0270] As a concrete example, in a company seeking a leader for a new project, a terminal collects the user's work data, and a server analyzes that data. If the user is deemed to have strong project management skills, the server can propose a project leader position. If this proposal is accepted, the user's feedback plays a role in improving the accuracy of future proposals.

[0271] An example of a prompt message to be input into the generating AI model is, "Analyze the user's work data and suggest the most suitable job position." This allows the user to make the most of their abilities and find the most suitable job.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The terminal collects digital information generated from the user's daily work activities. Input data comes from software such as email, chat, and task management tools. This data includes the user's work-related messages, task completion status, and meeting notes. The terminal securely collects this data and prepares it for transmission to the server.

[0275] Step 2:

[0276] The terminal sends the collected digital information to the server. The input is the digital information obtained in step 1, and the output is the transfer of that information to the server using a secure communication protocol. Here, the terminal uses SSL / TLS to transmit the data while protecting its confidentiality.

[0277] Step 3:

[0278] The server analyzes the received digital information. The input is unanalyzed digital information received from the terminal, and the server uses a generative AI model to preprocess the data. First, the server tokenizes the data, removes unnecessary words, and then extracts semantic information from the text using natural language processing techniques. The output is a set of features that represent the user's business capabilities.

[0279] Step 4:

[0280] The server evaluates the user's work capabilities based on the analysis results. The input is the features extracted by the analysis in step 3, and the output is a score for multiple work capabilities evaluated based on those features. The server quantifies these scores and evaluates the user's communication skills, problem-solving abilities, etc.

[0281] Step 5:

[0282] The server proposes job placements based on the user's work capabilities. The input is the score of the work capabilities obtained in Step 4, and the output is the proposed information on the optimal job position for the user. Using the generative AI model, the job requirements and scores are compared in real time, and the proposal is notified to the user's terminal.

[0283] Step 6:

[0284] The user sends feedback on the proposed job to the server. The input is the user's opinions and feelings, and the output is the feedback data stored in the server. The user provides feedback through the terminal, and this information is used to improve the next skill evaluation model.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In a local community, there is an issue of realizing more efficient and smooth community activities by each member playing an optimal role. However, it is difficult to accurately grasp the diverse skills and abilities of each individual member and propose an optimal role.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for collecting data generated by the information processing device from the user's activities, means for analyzing the data collected by the information processing device and evaluating the user's capabilities, and means for proposing an optimal role within the component based on the user's capability evaluation. This enables the proposal of an optimal role according to the skills and abilities of each individual member.

[0290] The "information processing device" is a device such as a computer or a server for receiving and analyzing electronic data.

[0291] "User" refers to an individual or member whose data is collected by an information processing device.

[0292] "Data generated from activities" refers to textual and numerical information obtained from users' daily actions and communications.

[0293] "Ability" refers to the practical skills and experience that a user possesses.

[0294] "Optimal role within a component" refers to the role or position within an organization or community that best suits an individual's abilities.

[0295] "Response" refers to the feedback that a user provides to an information processing device regarding the role it proposes.

[0296] "Methods for updating competency assessment models using user responses" refers to the process of improving the accuracy and effectiveness of suggestions displayed based on user responses.

[0297] In a mode for carrying out the invention, the system that implements this application functions as follows: First, the terminal collects digital data generated from the user's daily activities. This data includes emails, chat tools, and community activity logs. The collected data is automatically sent to the server.

[0298] The server uses natural language processing techniques to analyze the received data. This analysis process utilizes Python's natural language processing libraries, such as spaCy and NLTK. This allows for the quantification of the user's abilities and skills. This skill assessment uses machine learning algorithms, such as K-means clustering, to determine skill similarity and grouping. Furthermore, the Flask framework is used to build the server-side application logic.

[0299] The server then suggests the most suitable role within the organization or community based on the user's skill assessment. For example, if a user is assessed as having strong communication skills in a local cleanup activity, they may be suggested to take on a leadership role. Feedback from users who accept the suggestion is used to improve the accuracy of future suggestions.

[0300] Furthermore, this system has the functionality to instantly receive user feedback and update its competency assessment model. This update is managed by a SQLite-based database, resulting in more appropriate role suggestions.

[0301] The generative AI model is input using the following prompts:

[0302] "Analyze the user's communication skills and suggest the next recommended community activity role. Considering the user's past experiences of acceptance and successful leadership, suggest the next most suitable role."

[0303] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0304] Step 1:

[0305] The device collects digital data about the user's daily activities. It accepts data such as emails, chat tools, and local activity logs as input. This data provides information about the user's activities and is sent to a database.

[0306] Step 2:

[0307] The server receives digital data transmitted from the terminal and analyzes it using natural language processing technology. The input is user activity data, and the output is a numerical evaluation of the user's skills and abilities. The spaCy library is used for the analysis to extract important information from the text.

[0308] Step 3:

[0309] Based on the user's skill evaluation obtained through analysis, the server analyzes the skill similarity using K-means clustering. The input is the digitized skill evaluation data, and the output is the skill category to which the user belongs. This visualizes the characteristics of the user's capabilities.

[0310] Step 4:

[0311] The server proposes the optimal role within the organization or community based on the skill category. The input is the skill category information, and the output includes the proposed role and position. This proposal is notified to the user via the Flask framework.

[0312] Step 5:

[0313] The user provides feedback on whether to accept the proposed role. The input is the user's selection and feedback information, and the output is sent to the server as feedback data.

[0314] Step 6:

[0315] Upon receiving the user's feedback, the server updates the ability evaluation model. The input is the feedback data, and the output is the updated ability evaluation model. This improves the model's accuracy and optimizes subsequent proposals.

[0316] Step 7:

[0317] The server uses the generative AI model to generate new prompt texts and continues to train the model. The input is the past feedback and role proposal data, and the output is the improved prompt text for the next proposal.

[0318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0319] As an embodiment of the present invention, a system is constructed in an information processing device that utilizes business activity data and emotion data to evaluate the user's business skills and propose the most suitable job. This system consists of a terminal, a server, and an emotion engine.

[0320] The device simultaneously collects digital data from the user's daily work activities and emotional data derived from their interactions. This includes, for example, text data from emails, chat tools, and task management tools used by the user, as well as metadata such as voice and facial expressions.

[0321] The server receives data from the terminal and analyzes the text data using natural language processing technology. This analysis quantifies the user's skills in performing their tasks, and a skill evaluation is conducted. The skills evaluated cover a wide range, including problem-solving ability, communication skills, and project management skills.

[0322] In parallel, the emotion engine analyzes the collected emotion data to understand the user's emotional state. The server integrates this emotion assessment with job skills assessment to create a more refined profile. This profile makes it possible to suggest jobs and work environments that allow the user to perform their duties comfortably and effectively.

[0323] Proposed job assignments are notified to users via their devices. These notifications include an overview of the required skills and work environment, allowing users to accept or reject the assignment based on their own judgment. Users can also provide feedback on the proposals, which is stored on the server and used to improve the accuracy of skill evaluation and sentiment analysis models.

[0324] For example, if a user exhibits a calm emotional state after a team meeting and the content of the conversation indicates that they demonstrated advanced problem-solving abilities, the server can suggest a project leader position that would allow them to utilize their management skills. In this way, the present invention provides a system that enables job suggestions that take into account both the user's skills and emotions, thereby maximizing the mutual benefit of both the organization and the user.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The device collects text data and emotional data obtained from audio and video generated during the user's work activities. For example, it records email and chat history, as well as audio and facial expressions during video conferences.

[0328] Step 2:

[0329] The terminal transmits the collected data to the server using a secure communication protocol. This data includes information about the work performed and metadata that reflects emotions.

[0330] Step 3:

[0331] The server uses natural language processing techniques to analyze the text data. This allows it to quantify the skills that users demonstrate in their work, such as communication and problem-solving abilities.

[0332] Step 4:

[0333] The server uses an emotion engine to analyze emotional data and evaluate the user's emotional state. This includes information such as whether the user is stressed or relaxed.

[0334] Step 5:

[0335] The server integrates skill assessment results and emotional assessment results to generate a user profile. Based on this profile, it identifies jobs in which the user can perform well.

[0336] Step 6:

[0337] The server suggests appropriate job roles to the user. These suggestions include job descriptions, required skills, and work environment, and are communicated to the user via their terminal.

[0338] Step 7:

[0339] Users decide whether to accept the proposed job and provide feedback. Feedback is optional and should convey the user's preferences and concerns.

[0340] Step 8:

[0341] The server receives user feedback and uses it to improve the accuracy of its skill assessment and sentiment analysis models. This contributes to improving the quality of future assessments and suggestions.

[0342] (Example 2)

[0343] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0344] In modern organizations, there is a need to accurately understand the work performance capabilities and emotional state of individual users and propose the most suitable job based on that understanding. However, conventional systems have fragmented evaluations of skills and emotions derived from work activities, making it difficult to improve the accuracy of job proposals and user satisfaction.

[0345] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0346] In this invention, the server includes means for a data collection terminal to collect electronic and emotional information generated from the user's daily work activities, means for analyzing the collected information and quantifying and evaluating the user's work performance ability using natural language processing technology, and means for an emotion analysis device to grasp the user's emotional state and create a profile by integrating it with the work performance ability evaluation. This makes it possible to comprehensively evaluate the user's work skills and emotional state and propose individually optimized job roles.

[0347] A "data collection terminal" is a device used to acquire electronic and emotional information generated from users' daily work activities.

[0348] A "server" is a central computing device that analyzes information obtained from data collection terminals and quantifies and evaluates the user's ability to perform their duties.

[0349] "Natural language processing technology" is a technology that analyzes text data generated by users to understand its meaning and context.

[0350] An "emotion analysis device" is a device that analyzes data such as voice and facial expressions to evaluate the emotional state of a user.

[0351] A "profile" is evaluation data that integrates a user's work performance capabilities and emotional state, and includes information to provide optimal job recommendations.

[0352] "Job suggestion" is the act of presenting an appropriate job within an organization based on the user's work skills and emotional state.

[0353] "Feedback" refers to the act of providing information about whether a user will accept a job proposal, or providing opinions on the proposal.

[0354] To implement this invention, a system is constructed that combines a data collection terminal, a server, and an emotion analysis device. The data collection terminal collects electronic and emotional information from the user's daily work activities. This includes text data from emails and chat tools, as well as metadata including voice and facial expressions. Specifically, the terminal acquires voice and facial expression data via a camera and microphone and transmits it to the server.

[0355] The server analyzes text data using an application that implements natural language processing technology. This makes it possible to evaluate users' work performance capabilities with concrete numerical values. Natural language processing libraries and machine learning models are used for the analysis.

[0356] Emotion analysis devices analyze changes in voice tone and facial expressions to quantify the user's emotional state. This analysis utilizes emotion recognition software and algorithms, enabling real-time evaluation.

[0357] The server creates a user profile based on the analysis results. This profile incorporates job performance capabilities and emotional evaluations, and is used as basic data when making job recommendations. Based on the generated profile, the server selects the most suitable job and notifies the user via the terminal.

[0358] For example, if a user is evaluated as having strong team management skills and a calm emotional state, they may be proposed for a project leader position. This proposal, in turn, can contribute to the efficient allocation of personnel within the organization.

[0359] An example of a prompt for a generative AI model would be: "Please explain in detail a system that suggests the most suitable job for a person based on information obtained from their daily work data."

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The device collects text data and metadata from the user's daily work. Input comes from email, chat, and task management tools used by the user. The device acquires this data and collects metadata such as voice and facial expressions using its camera and microphone. This process yields a comprehensive dataset of the user's work activities.

[0363] Step 2:

[0364] The server receives data sent from the terminal and analyzes the text data using natural language processing technology. The input is the text data collected in step 1. Specifically, the analysis model extracts important keywords and phrases from the text and uses them to quantify and evaluate the user's work performance ability. The output of this process is numerical data of the evaluated skills.

[0365] Step 3:

[0366] The emotion analysis device analyzes voice and facial expression data to quantify the user's emotional state. The input is the voice and facial expression data collected in step 1. In this step, the emotion recognition algorithm analyzes the data in real time and graphs or quantifies the user's emotional state. As a result, the output is evaluation data indicating the user's emotional state.

[0367] Step 4:

[0368] The server integrates work performance evaluation data and emotional evaluation data to create a user profile. The input is the output data from steps 2 and 3. The server integrates this data to generate a profile that shows the user's strengths and job suitability. This output is detailed profile information of the user.

[0369] Step 5:

[0370] The server selects the most suitable job based on this information and notifies the user of the suggestion via the terminal. The input is the profile information generated in step 4. The server evaluates the profile, selects a job that the user can comfortably perform, and sends the suggestion to the terminal. The output is a notification message containing detailed information about the job.

[0371] Step 6:

[0372] The user accepts or rejects the job offer notified from the terminal. The input is the job offer received from the server in step 5. The user can review the job offer and make a selection. This selection is sent from the terminal to the server as feedback. As output, the feedback data is stored on the server and used to improve future job offers.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] In modern living environments, there is a need for systems that can suggest activities that take into account individual abilities and emotional states. However, existing technologies struggle to efficiently analyze users' abilities and emotional states and suggest optimal activities. Furthermore, there are challenges in appropriately suggesting activities within the home and continuously improving the system using feedback.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0377] In this invention, the server includes means for an information processing device to collect data generated from the user's activities, means for the information processing device to analyze the collected data and evaluate the user's abilities, and means for the information processing device to suggest activities within the home. This makes it possible to suggest optimal activities for each individual user, taking into account their emotional state.

[0378] An "information processing device" is a technological device used to collect data generated from user activities and to analyze that data.

[0379] "Data collection" is the process of gathering information generated from users' daily activities.

[0380] "Ability assessment" is the process of quantifying a user's abilities based on collected data and analyzing the results.

[0381] "Activity proposal" refers to the act of recommending the most suitable activity for the user based on an analyzed ability assessment.

[0382] "Emotional state" refers to the user's mental health and emotional condition, determined by analyzing information from the user's facial expressions and voice.

[0383] "Feedback" is a method of collecting responses and reactions from users and using them to improve the system.

[0384] An "evaluation model" is a set of algorithms and methodologies used to analyze a user's abilities and emotional state.

[0385] This invention provides a system that utilizes an information processing device, particularly a robot used in the home, to analyze data collected from a user's activities and, based on that analysis, suggests the most suitable activities. This system uses a terminal installed in the home, a server, and, if necessary, an emotion analysis engine.

[0386] The device collects various types of data from the user's daily life. This includes text data, voice data, and facial expression data. For example, it can collect recordings of conversations the user has on a daily basis and data from sensors in smart home devices.

[0387] The server processes the data acquired from the terminal. This processing utilizes natural language processing techniques to evaluate the user's abilities through the analysis of text and audio information contained in the data. Possible software to be used includes, for example, Python and its library, TextBlob.

[0388] The emotion analysis engine analyzes acquired voice and facial expression data to determine the user's emotional state. This allows the system to understand whether the user is stressed or relaxed, enabling it to suggest more appropriate activities.

[0389] For example, if it is determined from a conversation within the home that the user is tired, relaxation activities can be suggested. This suggestion generates appropriate activities based on a generative AI model and notifies the user. An example of a prompt sentence to input into the generative AI model is, "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members who are feeling stressed or tired." Following this prompt sentence, the system automatically selects appropriate activities.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The device collects data about the user's daily activities within the home. Inputs include the user's conversations, voice, and sensor data from smart devices. This data is stored in a local database.

[0393] Step 2:

[0394] The terminal sends this collected data to the server. The input is the data collected in step 1. The transmitted data is received by the server's receiving module for analysis.

[0395] Step 3:

[0396] The server analyzes the received data using natural language processing techniques to process both text and audio data. The input is the data received in step 2. For text data, it extracts emotions and important keywords using libraries such as TextBlob, and for audio data, it uses a speech analysis engine to estimate speech tone and emotions. The analysis results are output as skill and emotion assessments.

[0397] Step 4:

[0398] The server uses a generating AI model to suggest the most suitable activities for the user based on the analysis results. The input is the analysis results from step 3. Using the prompt "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members experiencing stress or fatigue," the AI ​​model generates specific activity suggestions. The generated suggestions are then output.

[0399] Step 5:

[0400] The terminal notifies the user of activity suggestions sent from the server. The input is the activity suggestions generated in step 4. The suggestions are delivered to the user visually or audibly, and the user can choose an action based on the suggestions.

[0401] Step 6:

[0402] Users provide feedback on suggested activities. Input includes data on user choices and satisfaction levels. The device collects this feedback and sends it to the server. This feedback data is used to further improve the system.

[0403] Step 7:

[0404] The server uses the collected feedback data to improve the evaluation model. The input is the feedback data received in step 6. This data is used to adjust the parameters of the skill evaluation model and the sentiment analysis model, aiming to improve the accuracy of suggestions in subsequent attempts. The output is the generated updated model.

[0405] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0406] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0407] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0411] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0412] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0413] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0414] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0415] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0416] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0417] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0418] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0419] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0420] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0421] As an embodiment of the present invention, a system is provided in which an information processing device, i.e., a server, plays a central role. This system begins with a terminal collecting various digital data generated from the user's daily work activities. Specifically, this includes data from emails, chat tools, and task management applications. This data is collected with the user's consent and transmitted to the server.

[0422] The server analyzes the received data and extracts important features from the text data using natural language processing techniques. This analysis quantifies the skills that users demonstrate in their work, and a skill evaluation is conducted. The skill evaluation is expressed as scores for various work skills, such as communication skills, problem-solving skills, and project management skills.

[0423] Subsequently, the server, based on the user's skill assessment, proposes job positions that match the company's job requirements. This increases the likelihood that the user will be placed in a position where they can make the most of their skills. The proposed job is notified to the user's terminal and provided along with detailed information.

[0424] Furthermore, users can provide feedback on the proposed job roles. This feedback is stored on the server and used to improve the skill assessment model, thereby enhancing the accuracy of future proposals. This cycle is designed to respond in real time to structural changes within the organization.

[0425] As a concrete example, suppose a new project is launched within a company. A terminal captures the deliverables and conversations the user has performed in the course of their work, and the server analyzes this data. The server then reveals that the user has high project management skills. Based on this information, the server can propose a project manager position to the user. If the user accepts this proposal, their feedback will be used to improve the accuracy of future analyses.

[0426] Thus, the embodiments of the present invention realize a system that provides the greatest benefit to both the user and the organization through a series of processes including data collection, analysis, evaluation, and proposal.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The device collects data generated from the user's work activities. Specifically, it captures data from various platforms used by the user, such as email, chat tools, and task management applications.

[0430] Step 2:

[0431] The device sends the collected data to the server. The data is transferred to the server using a secure protocol.

[0432] Step 3:

[0433] The server uses natural language processing techniques to extract keywords and characteristic phrases from the text data for the purpose of analyzing the received data.

[0434] Step 4:

[0435] The server evaluates the user's work skills based on the information obtained through analysis. The evaluation is quantified as scores, for example, for problem-solving ability and communication skills.

[0436] Step 5:

[0437] The server uses the user's skill assessment to propose job opportunities within the organization. These job proposals, along with a job overview and required skills, are sent to the user's device.

[0438] Step 6:

[0439] Users evaluate the proposed job and provide feedback. This feedback is sent to the server.

[0440] Step 7:

[0441] The server analyzes the feedback provided and updates the skills assessment model. This update will be reflected in improving the accuracy of future job proposals.

[0442] (Example 1)

[0443] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0444] In recent years, organizations have been required to make optimal use of human resources. However, traditional methods have not adequately provided means to quantitatively evaluate the work capabilities of users and propose appropriate job assignments based on those evaluations. This has resulted in situations where users are unable to make the most of their abilities, leading to a decline in the overall efficiency and productivity of the organization.

[0445] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0446] In this invention, the server includes means for an information processing device to collect digital information generated from the user's work activities, means for the information processing device to analyze the collected digital information and evaluate the user's work capabilities using natural language processing technology, and means for the information processing device to propose job placement within the organization based on the evaluation of the user's work capabilities. This makes it possible to accurately evaluate the user's capabilities and propose the optimal job placement in real time based on that evaluation.

[0447] An "information processing device" is a system consisting of hardware or software for receiving, analyzing, and evaluating digital information.

[0448] "User's work activities" refers to actions and operations related to work tasks and communication that users perform on a daily basis.

[0449] "Digital information" refers to electronically recorded data generated from users' work activities, including information obtained from emails, chats, task management tools, etc.

[0450] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand and analyze the language that humans use on a daily basis.

[0451] "Job competence" refers to the collection of skills, knowledge, and know-how that a user demonstrates in their job, and is subject to quantitative evaluation.

[0452] "Job placement suggestion" refers to a process that recommends jobs that best utilize the user's abilities, based on their work capabilities.

[0453] A "generative AI model" refers to a model that uses artificial intelligence technology to learn how to extract useful patterns and information from large amounts of data.

[0454] "Feedback" refers to opinions, impressions, and evaluations provided by users, and is information that can be used to improve the system.

[0455] Modes for carrying out the invention

[0456] This system, centered around an information processing device, processes digital information related to users' work activities to propose optimal job assignments. Specifically, servers and terminals each play their respective roles, and processes are executed accordingly.

[0457] The server functions as an information processing device, receiving digital information about work activities sent by users. This received digital information includes work-related data obtained from email clients, chat applications, task management tools, etc. The server can analyze this data using generative AI models and natural language processing techniques to evaluate the user's work capabilities. In this process, the generative AI model uses a comprehensive data analysis algorithm to extract effective patterns and features from the text information and score the work capabilities.

[0458] The device is responsible for collecting digital information generated from the user's daily work activities. This is done with the user's confirmation and consent, thus protecting privacy. The information collected from the device is then transmitted to the server in accordance with security protocols.

[0459] Users can also receive job placement suggestions from this system. Based on the job skills analyzed by the server, appropriate jobs are presented to the user. These suggestions can effectively utilize the user's skills by being compared with job requirements within the organization in real time. Users send their opinions on the presented jobs as feedback to the server, and this feedback helps improve the accuracy of the skill assessment model.

[0460] As a concrete example, in a company seeking a leader for a new project, a terminal collects the user's work data, and a server analyzes that data. If the user is deemed to have strong project management skills, the server can propose a project leader position. If this proposal is accepted, the user's feedback plays a role in improving the accuracy of future proposals.

[0461] An example of a prompt message to be input into the generating AI model is, "Analyze the user's work data and suggest the most suitable job position." This allows the user to make the most of their abilities and find the most suitable job.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The terminal collects digital information generated from the user's daily work activities. Input data comes from software such as email, chat, and task management tools. This data includes the user's work-related messages, task completion status, and meeting notes. The terminal securely collects this data and prepares it for transmission to the server.

[0465] Step 2:

[0466] The terminal sends the collected digital information to the server. The input is the digital information obtained in step 1, and the output is the transfer of that information to the server using a secure communication protocol. Here, the terminal uses SSL / TLS to transmit the data while protecting its confidentiality.

[0467] Step 3:

[0468] The server analyzes the received digital information. The input is unanalyzed digital information received from the terminal, and the server uses a generative AI model to preprocess the data. First, the server tokenizes the data, removes unnecessary words, and then extracts semantic information from the text using natural language processing techniques. The output is a set of features that represent the user's business capabilities.

[0469] Step 4:

[0470] The server evaluates the user's work capabilities based on the analysis results. The input is the features extracted by the analysis in step 3, and the output is a score for multiple work capabilities evaluated based on those features. The server quantifies these scores and evaluates the user's communication skills, problem-solving abilities, etc.

[0471] Step 5:

[0472] The server proposes job placements based on the user's work capabilities. The input is the work capability score obtained in step 4, and the output is information suggesting the most suitable job position for the user. Using a generative AI model, the system compares job requirements with the score in real time and notifies the user's terminal of the suggestions.

[0473] Step 6:

[0474] Users send feedback on proposed job roles to the server. Input is the user's opinions and impressions, and output is feedback data stored on the server. Users provide feedback via their terminals, and this information is used to improve the skill assessment model for future projects.

[0475] (Application Example 1)

[0476] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0477] In local communities, there is a challenge in achieving more efficient and smoother community activities by ensuring that each member takes on the most appropriate role. However, accurately understanding the diverse skills and abilities of each member and proposing the most suitable role is difficult.

[0478] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0479] In this invention, the server includes means for an information processing device to collect data generated from the user's activities, means for the information processing device to analyze the collected data and evaluate the user's capabilities, and means for the information processing device to propose the optimal role within the components based on the evaluation of the user's capabilities. This makes it possible to propose the optimal role according to the individual skills and abilities of each member.

[0480] An "information processing device" is a device such as a computer or server used to receive and analyze electronic data.

[0481] "User" refers to an individual or member whose data is collected by an information processing device.

[0482] "Data generated from activities" refers to textual and numerical information obtained from users' daily actions and communications.

[0483] "Ability" refers to the practical skills and experience that a user possesses.

[0484] "Optimal role within a component" refers to the role or position within an organization or community that best suits an individual's abilities.

[0485] "Response" refers to the feedback that a user provides to an information processing device regarding the role it proposes.

[0486] "Methods for updating competency assessment models using user responses" refers to the process of improving the accuracy and effectiveness of suggestions displayed based on user responses.

[0487] In a mode for carrying out the invention, the system that implements this application functions as follows: First, the terminal collects digital data generated from the user's daily activities. This data includes emails, chat tools, and community activity logs. The collected data is automatically sent to the server.

[0488] The server uses natural language processing techniques to analyze the received data. This analysis process utilizes Python's natural language processing libraries, such as spaCy and NLTK. This allows for the quantification of the user's abilities and skills. This skill assessment uses machine learning algorithms, such as K-means clustering, to determine skill similarity and grouping. Furthermore, the Flask framework is used to build the server-side application logic.

[0489] The server then suggests the most suitable role within the organization or community based on the user's skill assessment. For example, if a user is assessed as having strong communication skills in a local cleanup activity, they may be suggested to take on a leadership role. Feedback from users who accept the suggestion is used to improve the accuracy of future suggestions.

[0490] Furthermore, this system has the functionality to instantly receive user feedback and update its competency assessment model. This update is managed by a SQLite-based database, resulting in more appropriate role suggestions.

[0491] The generative AI model is input using the following prompts:

[0492] "Analyze the user's communication skills and suggest the next recommended community activity role. Considering the user's past experiences of acceptance and successful leadership, suggest the next most suitable role."

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] The device collects digital data about the user's daily activities. It accepts data such as emails, chat tools, and local activity logs as input. This data provides information about the user's activities and is sent to a database.

[0496] Step 2:

[0497] The server receives digital data transmitted from the terminal and analyzes it using natural language processing technology. The input is user activity data, and the output is a numerical evaluation of the user's skills and abilities. The spaCy library is used for the analysis to extract important information from the text.

[0498] Step 3:

[0499] The server analyzes the similarity of skills using K-means clustering based on the user's skill evaluation obtained through analysis. The input is numerical skill evaluation data, and the output is the skill category to which the user belongs. This visualizes the characteristics of the abilities that the user possesses.

[0500] Step 4:

[0501] The server suggests the most suitable role within an organization or community based on skill categories. The input is skill category information, and the output is suggested roles or positions. This suggestion is communicated to the user via the Flask framework.

[0502] Step 5:

[0503] The user provides feedback on whether they accept the proposed role. The input is the user's selection and feedback information, and the output is sent to the server as feedback data.

[0504] Step 6:

[0505] The server updates its capability assessment model based on user feedback. The input is feedback data, and the output is the updated capability assessment model. This improves the model's accuracy and optimizes future suggestions.

[0506] Step 7:

[0507] The server generates new prompt sentences using a generative AI model, continuously learning the model. The input is past feedback and role suggestion data, and the output is an improved prompt sentence for the next suggestion.

[0508] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0509] As an embodiment of the present invention, a system is constructed in an information processing device that utilizes business activity data and emotion data to evaluate the user's business skills and propose the most suitable job. This system consists of a terminal, a server, and an emotion engine.

[0510] The device simultaneously collects digital data from the user's daily work activities and emotional data derived from their interactions. This includes, for example, text data from emails, chat tools, and task management tools used by the user, as well as metadata such as voice and facial expressions.

[0511] The server receives data from the terminal and analyzes the text data using natural language processing technology. This analysis quantifies the user's skills in performing their tasks, and a skill evaluation is conducted. The skills evaluated cover a wide range, including problem-solving ability, communication skills, and project management skills.

[0512] In parallel, the emotion engine analyzes the collected emotion data to understand the user's emotional state. The server integrates this emotion assessment with job skills assessment to create a more refined profile. This profile makes it possible to suggest jobs and work environments that allow the user to perform their duties comfortably and effectively.

[0513] Proposed job assignments are notified to users via their devices. These notifications include an overview of the required skills and work environment, allowing users to accept or reject the assignment based on their own judgment. Users can also provide feedback on the proposals, which is stored on the server and used to improve the accuracy of skill evaluation and sentiment analysis models.

[0514] For example, if a user exhibits a calm emotional state after a team meeting and the content of the conversation indicates that they demonstrated advanced problem-solving abilities, the server can suggest a project leader position that would allow them to utilize their management skills. In this way, the present invention provides a system that enables job suggestions that take into account both the user's skills and emotions, thereby maximizing the mutual benefit of both the organization and the user.

[0515] The following describes the processing flow.

[0516] Step 1:

[0517] The device collects text data and emotional data obtained from audio and video generated during the user's work activities. For example, it records email and chat history, as well as audio and facial expressions during video conferences.

[0518] Step 2:

[0519] The terminal transmits the collected data to the server using a secure communication protocol. This data includes information about the work performed and metadata that reflects emotions.

[0520] Step 3:

[0521] The server uses natural language processing techniques to analyze the text data. This allows it to quantify the skills that users demonstrate in their work, such as communication and problem-solving abilities.

[0522] Step 4:

[0523] The server uses an emotion engine to analyze emotional data and evaluate the user's emotional state. This includes information such as whether the user is stressed or relaxed.

[0524] Step 5:

[0525] The server integrates skill assessment results and emotional assessment results to generate a user profile. Based on this profile, it identifies jobs in which the user can perform well.

[0526] Step 6:

[0527] The server suggests appropriate job roles to the user. These suggestions include job descriptions, required skills, and work environment, and are communicated to the user via their terminal.

[0528] Step 7:

[0529] Users decide whether to accept the proposed job and provide feedback. Feedback is optional and should convey the user's preferences and concerns.

[0530] Step 8:

[0531] The server receives user feedback and uses it to improve the accuracy of its skill assessment and sentiment analysis models. This contributes to improving the quality of future assessments and suggestions.

[0532] (Example 2)

[0533] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0534] In modern organizations, there is a need to accurately understand the work performance capabilities and emotional state of individual users and propose the most suitable job based on that understanding. However, conventional systems have fragmented evaluations of skills and emotions derived from work activities, making it difficult to improve the accuracy of job proposals and user satisfaction.

[0535] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0536] In this invention, the server includes means for a data collection terminal to collect electronic and emotional information generated from the user's daily work activities, means for analyzing the collected information and quantifying and evaluating the user's work performance ability using natural language processing technology, and means for an emotion analysis device to grasp the user's emotional state and create a profile by integrating it with the work performance ability evaluation. This makes it possible to comprehensively evaluate the user's work skills and emotional state and propose individually optimized job roles.

[0537] A "data collection terminal" is a device used to acquire electronic and emotional information generated from users' daily work activities.

[0538] A "server" is a central computing device that analyzes information obtained from data collection terminals and quantifies and evaluates the user's ability to perform their duties.

[0539] "Natural language processing technology" is a technology that analyzes text data generated by users to understand its meaning and context.

[0540] An "emotion analysis device" is a device that analyzes data such as voice and facial expressions to evaluate the emotional state of a user.

[0541] A "profile" is evaluation data that integrates a user's work performance capabilities and emotional state, and includes information to provide optimal job recommendations.

[0542] "Job suggestion" is the act of presenting an appropriate job within an organization based on the user's work skills and emotional state.

[0543] "Feedback" refers to the act of providing information about whether a user will accept a job proposal, or providing opinions on the proposal.

[0544] To implement this invention, a system is constructed that combines a data collection terminal, a server, and an emotion analysis device. The data collection terminal collects electronic and emotional information from the user's daily work activities. This includes text data from emails and chat tools, as well as metadata including voice and facial expressions. Specifically, the terminal acquires voice and facial expression data via a camera and microphone and transmits it to the server.

[0545] The server analyzes text data using an application that implements natural language processing technology. This makes it possible to evaluate users' work performance capabilities with concrete numerical values. Natural language processing libraries and machine learning models are used for the analysis.

[0546] Emotion analysis devices analyze changes in voice tone and facial expressions to quantify the user's emotional state. This analysis utilizes emotion recognition software and algorithms, enabling real-time evaluation.

[0547] The server creates a user profile based on the analysis results. This profile incorporates job performance capabilities and emotional evaluations, and is used as basic data when making job recommendations. Based on the generated profile, the server selects the most suitable job and notifies the user via the terminal.

[0548] For example, if a user is evaluated as having strong team management skills and a calm emotional state, they may be proposed for a project leader position. This proposal, in turn, can contribute to the efficient allocation of personnel within the organization.

[0549] An example of a prompt for a generative AI model would be: "Please explain in detail a system that suggests the most suitable job for a person based on information obtained from their daily work data."

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] The device collects text data and metadata from the user's daily work. Input comes from email, chat, and task management tools used by the user. The device acquires this data and collects metadata such as voice and facial expressions using its camera and microphone. This process yields a comprehensive dataset of the user's work activities.

[0553] Step 2:

[0554] The server receives data sent from the terminal and analyzes the text data using natural language processing technology. The input is the text data collected in step 1. Specifically, the analysis model extracts important keywords and phrases from the text and uses them to quantify and evaluate the user's work performance ability. The output of this process is numerical data of the evaluated skills.

[0555] Step 3:

[0556] The emotion analysis device analyzes voice and facial expression data to quantify the user's emotional state. The input is the voice and facial expression data collected in step 1. In this step, the emotion recognition algorithm analyzes the data in real time and graphs or quantifies the user's emotional state. As a result, the output is evaluation data indicating the user's emotional state.

[0557] Step 4:

[0558] The server integrates work performance evaluation data and emotional evaluation data to create a user profile. The input is the output data from steps 2 and 3. The server integrates this data to generate a profile that shows the user's strengths and job suitability. This output is detailed profile information of the user.

[0559] Step 5:

[0560] The server selects the most suitable job based on this information and notifies the user of the suggestion via the terminal. The input is the profile information generated in step 4. The server evaluates the profile, selects a job that the user can comfortably perform, and sends the suggestion to the terminal. The output is a notification message containing detailed information about the job.

[0561] Step 6:

[0562] The user accepts or rejects the job offer notified from the terminal. The input is the job offer received from the server in step 5. The user can review the job offer and make a selection. This selection is sent from the terminal to the server as feedback. As output, the feedback data is stored on the server and used to improve future job offers.

[0563] (Application Example 2)

[0564] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0565] In modern living environments, there is a need for systems that can suggest activities that take into account individual abilities and emotional states. However, existing technologies struggle to efficiently analyze users' abilities and emotional states and suggest optimal activities. Furthermore, there are challenges in appropriately suggesting activities within the home and continuously improving the system using feedback.

[0566] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0567] In this invention, the server includes means for an information processing device to collect data generated from the user's activities, means for the information processing device to analyze the collected data and evaluate the user's abilities, and means for the information processing device to suggest activities within the home. This makes it possible to suggest optimal activities for each individual user, taking into account their emotional state.

[0568] An "information processing device" is a technological device used to collect data generated from user activities and to analyze that data.

[0569] "Data collection" is the process of gathering information generated from users' daily activities.

[0570] "Ability assessment" is the process of quantifying a user's abilities based on collected data and analyzing the results.

[0571] "Activity proposal" refers to the act of recommending the most suitable activity for the user based on an analyzed ability assessment.

[0572] "Emotional state" refers to the user's mental health and emotional condition, determined by analyzing information from the user's facial expressions and voice.

[0573] "Feedback" is a method of collecting responses and reactions from users and using them to improve the system.

[0574] An "evaluation model" is a set of algorithms and methodologies used to analyze a user's abilities and emotional state.

[0575] This invention provides a system that utilizes an information processing device, particularly a robot used in the home, to analyze data collected from a user's activities and, based on that analysis, suggests the most suitable activities. This system uses a terminal installed in the home, a server, and, if necessary, an emotion analysis engine.

[0576] The device collects various types of data from the user's daily life. This includes text data, voice data, and facial expression data. For example, it can collect recordings of conversations the user has on a daily basis and data from sensors in smart home devices.

[0577] The server processes the data acquired from the terminal. This processing utilizes natural language processing techniques to evaluate the user's abilities through the analysis of text and audio information contained in the data. Possible software to be used includes, for example, Python and its library, TextBlob.

[0578] The emotion analysis engine analyzes acquired voice and facial expression data to determine the user's emotional state. This allows the system to understand whether the user is stressed or relaxed, enabling it to suggest more appropriate activities.

[0579] For example, if it is determined from a conversation within the home that the user is tired, relaxation activities can be suggested. This suggestion generates appropriate activities based on a generative AI model and notifies the user. An example of a prompt sentence to input into the generative AI model is, "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members who are feeling stressed or tired." Following this prompt sentence, the system automatically selects appropriate activities.

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] The device collects data about the user's daily activities within the home. Inputs include the user's conversations, voice, and sensor data from smart devices. This data is stored in a local database.

[0583] Step 2:

[0584] The terminal sends this collected data to the server. The input is the data collected in step 1. The transmitted data is received by the server's receiving module for analysis.

[0585] Step 3:

[0586] The server analyzes the received data using natural language processing techniques to process both text and audio data. The input is the data received in step 2. For text data, it extracts emotions and important keywords using libraries such as TextBlob, and for audio data, it uses a speech analysis engine to estimate speech tone and emotions. The analysis results are output as skill and emotion assessments.

[0587] Step 4:

[0588] The server uses a generating AI model to suggest the most suitable activities for the user based on the analysis results. The input is the analysis results from step 3. Using the prompt "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members experiencing stress or fatigue," the AI ​​model generates specific activity suggestions. The generated suggestions are then output.

[0589] Step 5:

[0590] The terminal notifies the user of activity suggestions sent from the server. The input is the activity suggestions generated in step 4. The suggestions are delivered to the user visually or audibly, and the user can choose an action based on the suggestions.

[0591] Step 6:

[0592] Users provide feedback on suggested activities. Input includes data on user choices and satisfaction levels. The device collects this feedback and sends it to the server. This feedback data is used to further improve the system.

[0593] Step 7:

[0594] The server uses the collected feedback data to improve the evaluation model. The input is the feedback data received in step 6. This data is used to adjust the parameters of the skill evaluation model and the sentiment analysis model, aiming to improve the accuracy of suggestions in subsequent attempts. The output is the generated updated model.

[0595] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0596] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0597] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0601] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0602] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0603] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0604] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0605] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0606] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0607] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0608] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0609] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0610] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0611] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] As an embodiment of the present invention, a system is provided in which an information processing device, i.e., a server, plays a central role. This system begins with a terminal collecting various digital data generated from the user's daily work activities. Specifically, this includes data from emails, chat tools, and task management applications. This data is collected with the user's consent and transmitted to the server.

[0613] The server analyzes the received data and extracts important features from the text data using natural language processing techniques. This analysis quantifies the skills that users demonstrate in their work, and a skill evaluation is conducted. The skill evaluation is expressed as scores for various work skills, such as communication skills, problem-solving skills, and project management skills.

[0614] Subsequently, the server, based on the user's skill assessment, proposes job positions that match the company's job requirements. This increases the likelihood that the user will be placed in a position where they can make the most of their skills. The proposed job is notified to the user's terminal and provided along with detailed information.

[0615] Furthermore, users can provide feedback on the proposed job roles. This feedback is stored on the server and used to improve the skill assessment model, thereby enhancing the accuracy of future proposals. This cycle is designed to respond in real time to structural changes within the organization.

[0616] As a concrete example, suppose a new project is launched within a company. A terminal captures the deliverables and conversations the user has performed in the course of their work, and the server analyzes this data. The server then reveals that the user has high project management skills. Based on this information, the server can propose a project manager position to the user. If the user accepts this proposal, their feedback will be used to improve the accuracy of future analyses.

[0617] Thus, the embodiments of the present invention realize a system that provides the greatest benefit to both the user and the organization through a series of processes including data collection, analysis, evaluation, and proposal.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] The device collects data generated from the user's work activities. Specifically, it captures data from various platforms used by the user, such as email, chat tools, and task management applications.

[0621] Step 2:

[0622] The device sends the collected data to the server. The data is transferred to the server using a secure protocol.

[0623] Step 3:

[0624] The server uses natural language processing techniques to extract keywords and characteristic phrases from the text data for the purpose of analyzing the received data.

[0625] Step 4:

[0626] The server evaluates the user's work skills based on the information obtained through analysis. The evaluation is quantified as scores, for example, for problem-solving ability and communication skills.

[0627] Step 5:

[0628] The server uses the user's skill assessment to propose job opportunities within the organization. These job proposals, along with a job overview and required skills, are sent to the user's device.

[0629] Step 6:

[0630] Users evaluate the proposed job and provide feedback. This feedback is sent to the server.

[0631] Step 7:

[0632] The server analyzes the feedback provided and updates the skills assessment model. This update will be reflected in improving the accuracy of future job proposals.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In recent years, organizations have been required to make optimal use of human resources. However, traditional methods have not adequately provided means to quantitatively evaluate the work capabilities of users and propose appropriate job assignments based on those evaluations. This has resulted in situations where users are unable to make the most of their abilities, leading to a decline in the overall efficiency and productivity of the organization.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes means for an information processing device to collect digital information generated from the user's work activities, means for the information processing device to analyze the collected digital information and evaluate the user's work capabilities using natural language processing technology, and means for the information processing device to propose job placement within the organization based on the evaluation of the user's work capabilities. This makes it possible to accurately evaluate the user's capabilities and propose the optimal job placement in real time based on that evaluation.

[0638] An "information processing device" is a system consisting of hardware or software for receiving, analyzing, and evaluating digital information.

[0639] "User's work activities" refers to actions and operations related to work tasks and communication that users perform on a daily basis.

[0640] "Digital information" refers to electronically recorded data generated from users' work activities, including information obtained from emails, chats, task management tools, etc.

[0641] "Natural language processing technology" refers to a set of algorithms and techniques that enable computers to understand and analyze the language that humans use on a daily basis.

[0642] "Job competence" refers to the collection of skills, knowledge, and know-how that a user demonstrates in their job, and is subject to quantitative evaluation.

[0643] "Job placement suggestion" refers to a process that recommends jobs that best utilize the user's abilities, based on their work capabilities.

[0644] A "generative AI model" refers to a model that uses artificial intelligence technology to learn how to extract useful patterns and information from large amounts of data.

[0645] "Feedback" refers to opinions, impressions, and evaluations provided by users, and is information that can be used to improve the system.

[0646] Modes for carrying out the invention

[0647] This system, centered around an information processing device, processes digital information related to users' work activities to propose optimal job assignments. Specifically, servers and terminals each play their respective roles, and processes are executed accordingly.

[0648] The server functions as an information processing device, receiving digital information about work activities sent by users. This received digital information includes work-related data obtained from email clients, chat applications, task management tools, etc. The server can analyze this data using generative AI models and natural language processing techniques to evaluate the user's work capabilities. In this process, the generative AI model uses a comprehensive data analysis algorithm to extract effective patterns and features from the text information and score the work capabilities.

[0649] The device is responsible for collecting digital information generated from the user's daily work activities. This is done with the user's confirmation and consent, thus protecting privacy. The information collected from the device is then transmitted to the server in accordance with security protocols.

[0650] Users can also receive job placement suggestions from this system. Based on the job skills analyzed by the server, appropriate jobs are presented to the user. These suggestions can effectively utilize the user's skills by being compared with job requirements within the organization in real time. Users send their opinions on the presented jobs as feedback to the server, and this feedback helps improve the accuracy of the skill assessment model.

[0651] As a concrete example, in a company seeking a leader for a new project, a terminal collects the user's work data, and a server analyzes that data. If the user is deemed to have strong project management skills, the server can propose a project leader position. If this proposal is accepted, the user's feedback plays a role in improving the accuracy of future proposals.

[0652] An example of a prompt message to be input into the generating AI model is, "Analyze the user's work data and suggest the most suitable job position." This allows the user to make the most of their abilities and find the most suitable job.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The terminal collects digital information generated from the user's daily work activities. Input data comes from software such as email, chat, and task management tools. This data includes the user's work-related messages, task completion status, and meeting notes. The terminal securely collects this data and prepares it for transmission to the server.

[0656] Step 2:

[0657] The terminal sends the collected digital information to the server. The input is the digital information obtained in step 1, and the output is the transfer of that information to the server using a secure communication protocol. Here, the terminal uses SSL / TLS to transmit the data while protecting its confidentiality.

[0658] Step 3:

[0659] The server analyzes the received digital information. The input is unanalyzed digital information received from the terminal, and the server uses a generative AI model to preprocess the data. First, the server tokenizes the data, removes unnecessary words, and then extracts semantic information from the text using natural language processing techniques. The output is a set of features that represent the user's business capabilities.

[0660] Step 4:

[0661] The server evaluates the user's work capabilities based on the analysis results. The input is the features extracted by the analysis in step 3, and the output is a score for multiple work capabilities evaluated based on those features. The server quantifies these scores and evaluates the user's communication skills, problem-solving abilities, etc.

[0662] Step 5:

[0663] The server proposes job placements based on the user's work capabilities. The input is the work capability score obtained in step 4, and the output is information suggesting the most suitable job position for the user. Using a generative AI model, the system compares job requirements with the score in real time and notifies the user's terminal of the suggestions.

[0664] Step 6:

[0665] Users send feedback on proposed job roles to the server. Input is the user's opinions and impressions, and output is feedback data stored on the server. Users provide feedback via their terminals, and this information is used to improve the skill assessment model for future projects.

[0666] (Application Example 1)

[0667] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] In local communities, there is a challenge in achieving more efficient and smoother community activities by ensuring that each member takes on the most appropriate role. However, accurately understanding the diverse skills and abilities of each member and proposing the most suitable role is difficult.

[0669] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0670] In this invention, the server includes means for an information processing device to collect data generated from the user's activities, means for the information processing device to analyze the collected data and evaluate the user's capabilities, and means for the information processing device to propose the optimal role within the components based on the evaluation of the user's capabilities. This makes it possible to propose the optimal role according to the individual skills and abilities of each member.

[0671] An "information processing device" is a device such as a computer or server used to receive and analyze electronic data.

[0672] "User" refers to an individual or member whose data is collected by an information processing device.

[0673] "Data generated from activities" refers to textual and numerical information obtained from users' daily actions and communications.

[0674] "Ability" refers to the practical skills and experience that a user possesses.

[0675] "Optimal role within a component" refers to the role or position within an organization or community that best suits an individual's abilities.

[0676] "Response" refers to the feedback that a user provides to an information processing device regarding the role it proposes.

[0677] "Methods for updating competency assessment models using user responses" refers to the process of improving the accuracy and effectiveness of suggestions displayed based on user responses.

[0678] In a mode for carrying out the invention, the system that implements this application functions as follows: First, the terminal collects digital data generated from the user's daily activities. This data includes emails, chat tools, and community activity logs. The collected data is automatically sent to the server.

[0679] The server uses natural language processing techniques to analyze the received data. This analysis process utilizes Python's natural language processing libraries, such as spaCy and NLTK. This allows for the quantification of the user's abilities and skills. This skill assessment uses machine learning algorithms, such as K-means clustering, to determine skill similarity and grouping. Furthermore, the Flask framework is used to build the server-side application logic.

[0680] The server then suggests the most suitable role within the organization or community based on the user's skill assessment. For example, if a user is assessed as having strong communication skills in a local cleanup activity, they may be suggested to take on a leadership role. Feedback from users who accept the suggestion is used to improve the accuracy of future suggestions.

[0681] Furthermore, this system has the functionality to instantly receive user feedback and update its competency assessment model. This update is managed by a SQLite-based database, resulting in more appropriate role suggestions.

[0682] The generative AI model is input using the following prompts:

[0683] "Analyze the user's communication skills and suggest the next recommended community activity role. Considering the user's past experiences of acceptance and successful leadership, suggest the next most suitable role."

[0684] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0685] Step 1:

[0686] The device collects digital data about the user's daily activities. It accepts data such as emails, chat tools, and local activity logs as input. This data provides information about the user's activities and is sent to a database.

[0687] Step 2:

[0688] The server receives digital data transmitted from the terminal and analyzes it using natural language processing technology. The input is user activity data, and the output is a numerical evaluation of the user's skills and abilities. The spaCy library is used for the analysis to extract important information from the text.

[0689] Step 3:

[0690] The server analyzes the similarity of skills using K-means clustering based on the user's skill evaluation obtained through analysis. The input is numerical skill evaluation data, and the output is the skill category to which the user belongs. This visualizes the characteristics of the abilities that the user possesses.

[0691] Step 4:

[0692] The server suggests the most suitable role within an organization or community based on skill categories. The input is skill category information, and the output is suggested roles or positions. This suggestion is communicated to the user via the Flask framework.

[0693] Step 5:

[0694] The user provides feedback on whether they accept the proposed role. The input is the user's selection and feedback information, and the output is sent to the server as feedback data.

[0695] Step 6:

[0696] The server updates its capability assessment model based on user feedback. The input is feedback data, and the output is the updated capability assessment model. This improves the model's accuracy and optimizes future suggestions.

[0697] Step 7:

[0698] The server generates new prompt sentences using a generative AI model, continuously learning the model. The input is past feedback and role suggestion data, and the output is an improved prompt sentence for the next suggestion.

[0699] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0700] As an embodiment of the present invention, a system is constructed in an information processing device that utilizes business activity data and emotion data to evaluate the user's business skills and propose the most suitable job. This system consists of a terminal, a server, and an emotion engine.

[0701] The device simultaneously collects digital data from the user's daily work activities and emotional data derived from their interactions. This includes, for example, text data from emails, chat tools, and task management tools used by the user, as well as metadata such as voice and facial expressions.

[0702] The server receives data from the terminal and analyzes the text data using natural language processing technology. This analysis quantifies the user's skills in performing their tasks, and a skill evaluation is conducted. The skills evaluated cover a wide range, including problem-solving ability, communication skills, and project management skills.

[0703] In parallel, the emotion engine analyzes the collected emotion data to understand the user's emotional state. The server integrates this emotion assessment with job skills assessment to create a more refined profile. This profile makes it possible to suggest jobs and work environments that allow the user to perform their duties comfortably and effectively.

[0704] Proposed job assignments are notified to users via their devices. These notifications include an overview of the required skills and work environment, allowing users to accept or reject the assignment based on their own judgment. Users can also provide feedback on the proposals, which is stored on the server and used to improve the accuracy of skill evaluation and sentiment analysis models.

[0705] For example, if a user exhibits a calm emotional state after a team meeting and the content of the conversation indicates that they demonstrated advanced problem-solving abilities, the server can suggest a project leader position that would allow them to utilize their management skills. In this way, the present invention provides a system that enables job suggestions that take into account both the user's skills and emotions, thereby maximizing the mutual benefit of both the organization and the user.

[0706] The following describes the processing flow.

[0707] Step 1:

[0708] The device collects text data and emotional data obtained from audio and video generated during the user's work activities. For example, it records email and chat history, as well as audio and facial expressions during video conferences.

[0709] Step 2:

[0710] The terminal transmits the collected data to the server using a secure communication protocol. This data includes information about the work performed and metadata that reflects emotions.

[0711] Step 3:

[0712] The server uses natural language processing techniques to analyze the text data. This allows it to quantify the skills that users demonstrate in their work, such as communication and problem-solving abilities.

[0713] Step 4:

[0714] The server uses an emotion engine to analyze emotional data and evaluate the user's emotional state. This includes information such as whether the user is stressed or relaxed.

[0715] Step 5:

[0716] The server integrates skill assessment results and emotional assessment results to generate a user profile. Based on this profile, it identifies jobs in which the user can best utilize their skills.

[0717] Step 6:

[0718] The server suggests appropriate job roles to the user. These suggestions include job descriptions, required skills, and work environment, and are communicated to the user via their terminal.

[0719] Step 7:

[0720] Users decide whether to accept the proposed job and provide feedback. Feedback is optional and should convey the user's preferences and concerns.

[0721] Step 8:

[0722] The server receives user feedback and uses it to improve the accuracy of its skill assessment and sentiment analysis models. This contributes to improving the quality of future assessments and suggestions.

[0723] (Example 2)

[0724] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] In modern organizations, there is a need to accurately understand the work performance capabilities and emotional state of individual users and propose the most suitable job based on that understanding. However, conventional systems have fragmented evaluations of skills and emotions derived from work activities, making it difficult to improve the accuracy of job proposals and user satisfaction.

[0726] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0727] In this invention, the server includes means for a data collection terminal to collect electronic and emotional information generated from the user's daily work activities, means for analyzing the collected information and quantifying and evaluating the user's work performance ability using natural language processing technology, and means for an emotion analysis device to grasp the user's emotional state and create a profile by integrating it with the work performance ability evaluation. This makes it possible to comprehensively evaluate the user's work skills and emotional state and propose individually optimized job roles.

[0728] A "data collection terminal" is a device used to acquire electronic and emotional information generated from users' daily work activities.

[0729] A "server" is a central computing device that analyzes information obtained from data collection terminals and quantifies and evaluates the user's ability to perform their duties.

[0730] "Natural language processing technology" is a technology that analyzes text data generated by users to understand its meaning and context.

[0731] An "emotion analysis device" is a device that analyzes data such as voice and facial expressions to evaluate the emotional state of a user.

[0732] A "profile" is evaluation data that integrates a user's work performance capabilities and emotional state, and includes information to provide optimal job recommendations.

[0733] "Job suggestion" is the act of presenting an appropriate job within an organization based on the user's work skills and emotional state.

[0734] "Feedback" refers to the act of providing information about whether a user will accept a job proposal, or providing opinions on the proposal.

[0735] To implement this invention, a system is constructed that combines a data collection terminal, a server, and an emotion analysis device. The data collection terminal collects electronic and emotional information from the user's daily work activities. This includes text data from emails and chat tools, as well as metadata including voice and facial expressions. Specifically, the terminal acquires voice and facial expression data via a camera and microphone and transmits it to the server.

[0736] The server analyzes text data using an application that implements natural language processing technology. This makes it possible to evaluate users' work performance capabilities with concrete numerical values. Natural language processing libraries and machine learning models are used for the analysis.

[0737] Emotion analysis devices analyze changes in voice tone and facial expressions to quantify the user's emotional state. This analysis utilizes emotion recognition software and algorithms, enabling real-time evaluation.

[0738] The server creates a user profile based on the analysis results. This profile incorporates job performance capabilities and emotional evaluations, and is used as basic data when making job recommendations. Based on the generated profile, the server selects the most suitable job and notifies the user via the terminal.

[0739] For example, if a user is evaluated as having strong team management skills and a calm emotional state, they may be proposed for a project leader position. This proposal, in turn, can contribute to the efficient allocation of personnel within the organization.

[0740] An example of a prompt for a generative AI model would be: "Please explain in detail a system that suggests the most suitable job for a person based on information obtained from their daily work data."

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The device collects text data and metadata from the user's daily work. Input comes from email, chat, and task management tools used by the user. The device acquires this data and collects metadata such as voice and facial expressions using its camera and microphone. This process yields a comprehensive dataset of the user's work activities.

[0744] Step 2:

[0745] The server receives data sent from the terminal and analyzes the text data using natural language processing technology. The input is the text data collected in step 1. Specifically, the analysis model extracts important keywords and phrases from the text and uses them to quantify and evaluate the user's work performance ability. The output of this process is numerical data of the evaluated skills.

[0746] Step 3:

[0747] The emotion analysis device analyzes voice and facial expression data to quantify the user's emotional state. The input is the voice and facial expression data collected in step 1. In this step, the emotion recognition algorithm analyzes the data in real time and graphs or quantifies the user's emotional state. As a result, the output is evaluation data indicating the user's emotional state.

[0748] Step 4:

[0749] The server integrates work performance evaluation data and emotional evaluation data to create a user profile. The input is the output data from steps 2 and 3. The server integrates this data to generate a profile that shows the user's strengths and job suitability. This output is detailed profile information of the user.

[0750] Step 5:

[0751] The server selects the most suitable job based on this information and notifies the user of the suggestion via the terminal. The input is the profile information generated in step 4. The server evaluates the profile, selects a job that the user can comfortably perform, and sends the suggestion to the terminal. The output is a notification message containing detailed information about the job.

[0752] Step 6:

[0753] The user accepts or rejects the job offer notified from the terminal. The input is the job offer received from the server in step 5. The user can review the job offer and make a selection. This selection is sent from the terminal to the server as feedback. As output, the feedback data is stored on the server and used to improve future job offers.

[0754] (Application Example 2)

[0755] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In modern living environments, there is a need for systems that can suggest activities that take into account individual abilities and emotional states. However, existing technologies struggle to efficiently analyze users' abilities and emotional states and suggest optimal activities. Furthermore, there are challenges in appropriately suggesting activities within the home and continuously improving the system using feedback.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0758] In this invention, the server includes means for an information processing device to collect data generated from the user's activities, means for the information processing device to analyze the collected data and evaluate the user's abilities, and means for the information processing device to suggest activities within the home. This makes it possible to suggest optimal activities for each individual user, taking into account their emotional state.

[0759] An "information processing device" is a technological device used to collect data generated from user activities and to analyze that data.

[0760] "Data collection" is the process of gathering information generated from users' daily activities.

[0761] "Ability assessment" is the process of quantifying a user's abilities based on collected data and analyzing the results.

[0762] "Activity proposal" refers to the act of recommending the most suitable activity for the user based on an analyzed ability assessment.

[0763] "Emotional state" refers to the user's mental health and emotional condition, determined by analyzing information from the user's facial expressions and voice.

[0764] "Feedback" is a method of collecting responses and reactions from users and using them to improve the system.

[0765] An "evaluation model" is a set of algorithms and methodologies used to analyze a user's abilities and emotional state.

[0766] This invention provides a system that utilizes an information processing device, particularly a robot used in the home, to analyze data collected from a user's activities and, based on that analysis, suggests the most suitable activities. This system uses a terminal installed in the home, a server, and, if necessary, an emotion analysis engine.

[0767] The device collects various types of data from the user's daily life. This includes text data, voice data, and facial expression data. For example, it can collect recordings of conversations the user has on a daily basis and data from sensors in smart home devices.

[0768] The server processes the data acquired from the terminal. This processing utilizes natural language processing techniques to evaluate the user's abilities through the analysis of text and audio information contained in the data. Possible software to be used includes, for example, Python and its library, TextBlob.

[0769] The emotion analysis engine analyzes acquired voice and facial expression data to determine the user's emotional state. This allows the system to understand whether the user is stressed or relaxed, enabling it to suggest more appropriate activities.

[0770] For example, if it is determined from a conversation within the home that the user is tired, relaxation activities can be suggested. This suggestion generates appropriate activities based on a generative AI model and notifies the user. An example of a prompt sentence to input into the generative AI model is, "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members who are feeling stressed or tired." Following this prompt sentence, the system automatically selects appropriate activities.

[0771] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0772] Step 1:

[0773] The device collects data about the user's daily activities within the home. Inputs include the user's conversations, voice, and sensor data from smart devices. This data is stored in a local database.

[0774] Step 2:

[0775] The terminal sends this collected data to the server. The input is the data collected in step 1. The transmitted data is received by the server's receiving module for analysis.

[0776] Step 3:

[0777] The server analyzes the received data using natural language processing techniques to process both text and audio data. The input is the data received in step 2. For text data, it extracts emotions and important keywords using libraries such as TextBlob, and for audio data, it uses a speech analysis engine to estimate speech tone and emotions. The analysis results are output as skill and emotion assessments.

[0778] Step 4:

[0779] The server uses a generating AI model to suggest the most suitable activities for the user based on the analysis results. The input is the analysis results from step 3. Using the prompt "Household robot, understand the emotional state of family members and suggest relaxation activities recommended for members experiencing stress or fatigue," the AI ​​model generates specific activity suggestions. The generated suggestions are then output.

[0780] Step 5:

[0781] The terminal notifies the user of activity suggestions sent from the server. The input is the activity suggestions generated in step 4. The suggestions are delivered to the user visually or audibly, and the user can choose an action based on the suggestions.

[0782] Step 6:

[0783] Users provide feedback on suggested activities. Input includes data on user choices and satisfaction levels. The device collects this feedback and sends it to the server. This feedback data is used to further improve the system.

[0784] Step 7:

[0785] The server uses the collected feedback data to improve the evaluation model. The input is the feedback data received in step 6. This data is used to adjust the parameters of the skill evaluation model and the sentiment analysis model, aiming to improve the accuracy of suggestions in subsequent attempts. The output is the generated updated model.

[0786] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0787] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0788] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0789] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0790] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0791] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0792] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0793] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0794] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0795] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0796] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0797] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0798] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0799] 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.

[0800] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0801] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0802] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0803] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0804] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0805] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0806] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0807] The following is further disclosed regarding the embodiments described above.

[0808] (Claim 1)

[0809] A means by which an information processing device collects data generated from the user's business activities,

[0810] A means of analyzing data collected by an information processing device and evaluating the user's work skills,

[0811] A means by which an information processing device suggests the most suitable job within an organization based on the user's skill assessment,

[0812] A means by which an information processing device updates its skill assessment model using user feedback,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the information processing device uses natural language processing technology to analyze text data relating to the user's business activities.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the information processing device updates job proposals in real time in response to job requirements within the organization.

[0818] "Example 1"

[0819] (Claim 1)

[0820] A means by which an information processing device collects digital information generated from the user's business activities,

[0821] A means of analyzing collected digital information using an information processing device and evaluating the user's work capabilities using natural language processing technology,

[0822] A means by which an information processing device proposes job assignments within an organization based on an evaluation of the user's work capabilities,

[0823] A means by which an information processing device improves its capability evaluation model using user feedback,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, wherein the information processing device uses a generated AI model to analyze text information relating to the user's business activities.

[0827] (Claim 3)

[0828] The system according to claim 1, wherein the information processing device improves job assignment suggestions in real time in response to job requirements within the organization.

[0829] "Application Example 1"

[0830] (Claim 1)

[0831] A means for an information processing device to collect data generated from user activities,

[0832] A means of analyzing data collected by an information processing device and evaluating the user's capabilities,

[0833] A means by which an information processing device proposes the optimal role within its components based on an assessment of the user's capabilities,

[0834] A means by which an information processing device updates its capability evaluation model using user feedback,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the information processing device uses natural language processing technology to analyze textual information relating to the user's activities.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the information processing device updates role proposals in response to requests within the components immediately.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A data collection terminal is a means for collecting electronic information and emotional information generated from the user's daily work activities,

[0843] A method for analyzing information collected by a server and quantifying and evaluating the user's work performance capabilities using natural language processing technology,

[0844] A means by which an emotion analysis device understands the user's emotional state and creates a profile by integrating it with an evaluation of their work performance ability,

[0845] A means by which the server suggests the most suitable job within the organization based on the user's profile,

[0846] A means by which the terminal notifies the user of proposed tasks, collects user feedback and sends it to the server, and updates the skill evaluation model,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, in which an emotion analysis device analyzes changes in voice and facial expressions to evaluate the user's emotions and refine the profile integration.

[0850] (Claim 3)

[0851] The system according to claim 1, wherein the server dynamically updates job proposals in response to job requirements that change in real time and re-evaluates the most suitable job.

[0852] "Application example 2 when combining with an emotional engine"

[0853] (Claim 1)

[0854] A means for an information processing device to collect data generated from user activities,

[0855] A means of analyzing data collected by an information processing device and evaluating the user's capabilities,

[0856] A means by which an information processing device proposes appropriate activities based on an assessment of the user's abilities,

[0857] An information processing device analyzes the user's emotional state and provides a means to optimize skill evaluation.

[0858] An information processing device is a means of suggesting activities within the home,

[0859] A means by which an information processing device improves its evaluation model using user feedback,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein the information processing device uses language processing technology to analyze data relating to user activities.

[0863] (Claim 3)

[0864] The system according to claim 1, wherein the information processing device updates the proposal in real time in response to requests in the environment. [Explanation of Symbols]

[0865] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for an information processing device to collect data generated from user activities, A means of analyzing data collected by an information processing device and evaluating the user's capabilities, A means by which an information processing device proposes the optimal role within its components based on an assessment of the user's capabilities, A means by which an information processing device updates its capability evaluation model using user feedback, A system that includes this.

2. The system according to claim 1, wherein the information processing device uses natural language processing technology to analyze textual information relating to the user's activities.

3. The system according to claim 1, wherein the information processing device updates the role proposals immediately in response to requests within the components.

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