system

Abstract

We provide the system. [Solution] A successor selection support system comprising means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of the proposed successor candidates, and means for obtaining approval from the selected successor.

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] Successor selection is an important decision that greatly affects the survival of an organization. However, in order to select an appropriate successor, it is necessary to deeply understand the thinking patterns of candidates and select the most suitable person from them. This is difficult to do only by humans and may be influenced by subjective judgments.

Means for Solving the Problems

[0005] This invention provides means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval from the selected successor. This consistently supports the successor selection process and enables the selection of a more appropriate successor. In particular, by using artificial intelligence to analyze human thought patterns and exploring individuals with similar thinking patterns or entirely new patterns, it is possible to propose a diverse range of successor candidates. [Brief explanation of the drawing]

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12]This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] This is a sequence diagram showing the processing flow of the data processing system in Example 1 of the Form 1 when an emotion engine is combined. [Figure 18] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of the Form 2 when an emotion engine is combined. [Figure 20] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] This is a sequence diagram showing the processing flow of the data processing system in Example 3 of the Form 3 when an emotion engine is combined. [Figure 22] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. [Figure 23] This is a sequence diagram showing the processing flow of a data processing system in another embodiment. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0012] 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).

[0013] 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."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0029] "Example of form 2"

[0030] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0031] "Example of form 3"

[0032] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: The artificial intelligence learns and analyzes the candidate's thought patterns from their past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold.

[0036] Step 2: Based on the analysis results, the artificial intelligence proposes potential successors by exploring individuals with similar thinking patterns or entirely new ones. Specifically, it considers factors such as how closely the candidate's thinking pattern matches that of the current leader, or whether they have the potential to bring a new perspective.

[0037] Step 3: Provide information to help select the most suitable person from the proposed candidates. Specifically, evaluate each candidate's strengths, weaknesses, and suitability, and use this information to support the selection process.

[0038] Step 4: Support the selected successor in gaining approval. Specifically, provide information demonstrating the suitability and abilities of the selected successor to help them gain approval.

[0039] (Example 1)

[0040] Next, we will describe Example 1 of Form 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."

[0041] Traditional succession selection processes often relied on subjective judgments, making it difficult to accurately assess candidates' thought patterns and aptitudes. As a result, there was a risk of overlooking suitable candidates when selecting successors to lead the organization into the future.

[0042] 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.

[0043] In this invention, the server includes means for collecting data, means for analyzing thought patterns using the collected data, and means for proposing successor candidates based on the analysis results. This makes it possible to objectively and efficiently select successor candidates and find suitable personnel to lead the organization into the future.

[0044] "Means of collecting data" refers to devices or methods for obtaining information such as a candidate's past actions, statements, and evaluations.

[0045] "Means for analyzing thought patterns" refers to a device or method for evaluating a candidate's decision-making patterns and values ​​using collected data.

[0046] "Means of proposing successor candidates" refers to a device or method for selecting and presenting individuals who have the potential to lead the organization in the future, based on the results of an analysis.

[0047] A "machine learning framework" is a software library used to train models with data and perform pattern recognition and prediction.

[0048] "Calculating similarity" is the process of comparing the thinking patterns of current leaders and candidates and quantifying the degree of similarity.

[0049] "Evaluating the potential to bring a new perspective" is the process of determining whether a candidate has the ability to bring innovation and transformation to the organization.

[0050] The embodiment of this invention provides a specific method for constructing a successor selection support system. The server first collects data on candidates' past behavior, statements, and evaluations. This data is obtained via APIs from corporate databases and publicly available sources. Next, the server uses the collected data to train an artificial intelligence (AI) model and analyze the candidates' thought patterns. Machine learning frameworks such as TENSORFLOW® and PyTorch are used in this process. The AI ​​models the candidates' decision-making patterns and values, and evaluates their past project success rates and leadership style.

[0051] Subsequently, the server uses the analysis results to have AI suggest potential successors. The AI ​​compares the current leader's thought patterns with those of the candidates and calculates the degree of similarity. It also evaluates candidates who may bring a new perspective. This information is provided to the user through their device. The user can then select the most suitable successor based on the strengths, weaknesses, and suitability of each candidate displayed on their device.

[0052] As a concrete example, by inputting the following prompt into the generative AI model, you can receive suggestions for potential successors.

[0053] Prompt example:

[0054] "Based on the current leader's thought patterns, please propose a suitable candidate for the next leader. Consider the candidate's past project success rate and team evaluations."

[0055] By using this prompt, the AI ​​can suggest suitable successor candidates, and the user can make a selection based on that information.

[0056] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0057] Step 1:

[0058] The server collects data on candidates' past behavior, statements, and evaluations. It retrieves necessary information from company databases and publicly available sources via APIs as input. Specifically, the server sends API requests and stores the data received as responses in storage. The output is a detailed dataset about the candidates.

[0059] Step 2:

[0060] The server uses the collected data to train an artificial intelligence (AI) model and analyze the candidates' thought patterns. The dataset obtained in Step 1 is used as input. Specifically, the server preprocesses the data using machine learning frameworks such as TensorFlow or PyTorch and applies it to the model. The output is an analysis showing each candidate's thought pattern.

[0061] Step 3:

[0062] The server uses the analysis results to propose successor candidates using AI. The input is the analysis results of the thought patterns obtained in Step 2. Specifically, the server compares the current leader's thought patterns with those of the candidates and calculates the similarity. It also evaluates candidates who may bring new perspectives. The output is a list of proposed successor candidates.

[0063] Step 4:

[0064] The terminal presents the user with information on proposed successor candidates and assists in the selection process. The input is the list of successor candidates obtained in step 3. Specifically, the terminal visually displays each candidate's strengths, weaknesses, and suitability. Based on this information, the user can select the most suitable successor. The output is the successor selected by the user.

[0065] Step 5:

[0066] The server provides the necessary information to ensure the selected successor receives approval. The successor selected by the user in step 4 is used as input. Specifically, the server generates a detailed report detailing the selected successor's experience and skill set, and sends it to the relevant parties. The output is a detailed report to support the approval process.

[0067] (Application Example 1)

[0068] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0069] Selecting successors for robot maintenance personnel in factories often relies on experience and intuition, making it difficult to choose the right person. Furthermore, the inability to effectively utilize on-site work history and evaluations reduces the efficiency of successor selection.

[0070] 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.

[0071] In this invention, the server includes means for analyzing human thought patterns, means for suggesting successor candidates based on the analysis results, and means for suggesting the most suitable successor based on work history and evaluation. This makes it possible to efficiently and appropriately select successors for robot maintenance personnel in factories.

[0072] "Methods for analyzing human thought patterns" refer to techniques for analyzing a person's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[0073] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting and presenting appropriate successor candidates by utilizing the results of an analysis of thought patterns.

[0074] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide the necessary information to choose the most suitable person from among the presented candidates and assist in the selection process.

[0075] "Means of obtaining approval for selected successors" refers to technologies that provide information demonstrating the suitability and abilities of selected successors in order to obtain approval.

[0076] "A method for proposing the most suitable successor based on work history and evaluations" refers to a technology that utilizes a candidate's past work history and evaluation data to select the most suitable successor.

[0077] "Methods for recording and analyzing on-site work" refers to techniques for recording activities at the work site and analyzing that data to identify areas for improvement and streamline operations.

[0078] The system for implementing this invention operates through the collaboration of a server, a terminal, and a user. The server runs a program that uses artificial intelligence to analyze human thought patterns. Specifically, it uses machine learning libraries such as TensorFlow to analyze the candidate's past behavior, statements, and evaluation data, and learns their thought patterns. Based on the analysis results, the server can then propose successor candidates.

[0079] The device functions as smart glasses or other wearable devices to record work performed on-site. It uses a camera and microphone to capture the work in real time and transmits the data to a server. The server analyzes this data, reviewing work history and performance evaluations, and suggests the most suitable successor.

[0080] The user receives information on proposed successor candidates through their device and makes a selection. Based on the information displayed on the device's screen, the user can evaluate each candidate's strengths and weaknesses and choose the most suitable person. To obtain approval for the selected successor, the server provides information indicating their suitability and abilities.

[0081] One concrete example is analyzing how maintenance personnel at a particular factory have solved problems in the past and learning their thought patterns. An example of a prompt for a generative AI model would be, "Based on past maintenance history and evaluation data, please suggest the most suitable candidate for the next maintenance personnel."

[0082] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0083] Step 1:

[0084] The terminal records on-site work using a camera and microphone. It acquires video and audio data of the work as input and transmits this data to the server in real time. The output is the work data transmitted to the server.

[0085] Step 2:

[0086] The server analyzes the received work data. It receives video and audio data transmitted from the terminal as input and analyzes the data using a machine learning model. Specifically, it uses TensorFlow to evaluate work efficiency and problem-solving methods. The output consists of work history and evaluation data as analysis results.

[0087] Step 3:

[0088] The server analyzes human thought patterns based on the analysis results. Using work history and evaluation data as input, the artificial intelligence learns the candidate's thought patterns. The output is the analysis result of the thought patterns.

[0089] Step 4:

[0090] The server proposes successor candidates based on the analysis results. It uses the analysis results of thought patterns as input and selects the optimal successor candidate using a generative AI model. The output is a list of proposed successor candidates.

[0091] Step 5:

[0092] The user receives information about proposed successor candidates through their device. The device receives a list of candidates sent from the server as input and displays it on the device's screen. The output is candidate information for the user to review.

[0093] Step 6:

[0094] The user selects the most suitable successor based on candidate information. The input involves evaluating and selecting candidate information displayed on the terminal. The output is information about the selected successor.

[0095] Step 7:

[0096] The server provides the information necessary to obtain approval for the selected successor. It receives information about the selected successor as input and generates data indicating their suitability and abilities. The output is the information required for approval.

[0097] (Example 2)

[0098] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0099] Traditional successor selection processes have faced challenges in selecting a suitable successor because they fail to adequately analyze the thought patterns of candidates. Furthermore, a lack of information necessary to obtain approval for the selected successor hinders the smooth approval process.

[0100] 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.

[0101] In this invention, the server includes means for collecting and pre-processing information, means for analyzing thought patterns using the pre-processed information, and means for selecting and proposing candidates based on the analysis results. This makes it possible to analyze the thought patterns of candidates in detail and select an appropriate successor. Furthermore, by providing information for obtaining approval from the selected successor, the approval process can be facilitated.

[0102] "Means for collecting and pre-processing information" refers to technologies for collecting data about candidates and processing it to convert it into a format suitable for analysis.

[0103] "Methods for analyzing thought patterns" refer to techniques that analyze candidates' values ​​and decision-making tendencies based on collected data.

[0104] "Methods for selecting and proposing candidates" refers to the techniques for selecting and proposing appropriate successor candidates based on analysis results.

[0105] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and learn patterns.

[0106] "Similarity" is an indicator that shows how closely the thinking patterns of the current leader and the candidate match.

[0107] A "candidate who can offer a new perspective" is someone who possesses different values ​​and approaches from traditional leaders and has the potential to bring a new direction to the organization.

[0108] This invention takes the form of a system in which a server, terminal, and user cooperate to implement a successor selection support system.

[0109] The server first collects information about the candidates. Specifically, it retrieves data such as the candidates' past behavior, statements, and evaluations from company databases and publicly available information. The server preprocesses this data, cleaning text data and normalizing numerical data. This transforms the data into a format suitable for analysis.

[0110] Next, the server inputs the pre-processed data into a generative AI model to analyze the candidates' thought patterns. The generative AI model uses natural language processing techniques to extract values ​​and decision-making tendencies from the text data. The results of this analysis are quantified for each candidate's thought pattern and stored in a database.

[0111] The server selects successor candidates based on the analysis results. Specifically, it compares the current leader's thinking patterns with those of the candidates and lists candidates with high similarity or those who can offer new perspectives. This list of selected candidates is then sent to the terminal.

[0112] The terminal displays a list of candidates received from the server to the user. It provides a dashboard that visually shows each candidate's strengths, weaknesses, and suitability, helping the user select the most suitable successor.

[0113] The user selects the most suitable successor based on information provided through the device. To obtain approval for the selected successor, the user creates a presentation for the approval meeting. Using information on the candidate's achievements and abilities provided through the device, the user explains the suitability of the selected successor at the approval meeting and persuades the approval committee to approve it.

[0114] For example, if a candidate has a track record of successfully completing innovative projects in the past, the server will use that track record to suggest the candidate as a successor who could bring a fresh perspective. An example of a prompt to the generative AI model would be: "Based on the candidate's past behavioral data, analyze their leadership style and suggest successor candidates who are a good match for the current leader or who can offer a fresh perspective."

[0115] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0116] Step 1:

[0117] The server collects information about candidates. As input, it retrieves data such as candidates' past behavior, statements, and evaluations from company databases and publicly available information. Specifically, the server collects data from social media posting history and internal evaluation systems via APIs. The output is the collected raw data.

[0118] Step 2:

[0119] The server preprocesses the collected data. It uses the raw data obtained in step 1 as input. Specifically, the server cleans text data and normalizes numerical data, converting it into a format suitable for analysis. The output is the preprocessed data.

[0120] Step 3:

[0121] The server inputs pre-processed data into a generating AI model to analyze the candidates' thought patterns. The pre-processed data obtained in step 2 is used as input. Specifically, the server uses natural language processing techniques to extract values ​​and decision-making tendencies from the text data. The output is numerical data representing each candidate's thought pattern.

[0122] Step 4:

[0123] The server selects successor candidates based on the analysis results. The input is the quantified data of thought patterns obtained in step 3. Specifically, the server compares the current leader's thought patterns with those of the candidates, listing candidates with high similarity or those who can offer new perspectives. The output is a list of selected candidates.

[0124] Step 5:

[0125] The server sends the selected candidate list to the terminal. The candidate list obtained in step 4 is used as input. Specifically, the server uses a communication protocol to transfer the data to the terminal. The candidate list is displayed on the terminal as output.

[0126] Step 6:

[0127] The terminal displays the candidate list received from the server to the user. The candidate list submitted in step 5 is used as input. Specifically, the terminal provides a dashboard that visually displays each candidate's strengths, weaknesses, and suitability. The output is a screen that allows the user to visually review the information.

[0128] Step 7:

[0129] The user selects the most suitable successor based on the information provided through the device. The candidate information displayed in step 6 is used as input. Specifically, the user compares the candidates' evaluation points and makes a decision. The selected successor is obtained as output.

[0130] Step 8:

[0131] The user creates materials to obtain approval for the selected successor. The input uses the information of the successor selected in step 7. Specifically, the user uses information about the candidate's achievements and abilities provided by the terminal to create a presentation for the approval meeting. The output is the presentation materials to be used at the approval meeting.

[0132] (Application Example 2)

[0133] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0134] In selecting managers for machinery and equipment within a factory, there is a need to efficiently identify suitable candidates and ensure a smooth selection process. However, traditional methods make it difficult to adequately evaluate candidates' thought patterns and aptitudes, posing a challenge in selecting appropriate managers.

[0135] 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.

[0136] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for assisting in the selection of proposed successor candidates, means for obtaining approval from the selected successor, means for proposing candidates for managers of machinery and equipment within the factory, means for assisting in the selection of proposed manager candidates, and means for obtaining approval from the selected manager. This makes it possible to efficiently and accurately select managers for machinery and equipment within the factory.

[0137] "Human thought patterns" refer to the unique ways of thinking and decision-making tendencies that each individual possesses.

[0138] "Analysis results" refer to the information and data obtained after artificial intelligence analyzes human thought patterns.

[0139] A "successor candidate" refers to a person who has the potential to take over a specific role or position.

[0140] A "proposed successor candidate" refers to a person who has been selected based on the analysis results and is deemed suitable to be the successor.

[0141] The term "selected successor" refers to the person ultimately chosen from among the proposed candidates.

[0142] "Means of obtaining approval" refers to the methods and processes by which a selected successor obtains the necessary consent or permission to formally assume that role.

[0143] "Candidate for manager of machinery and equipment within the factory" refers to a person who may be responsible for managing the machinery and equipment used within the factory.

[0144] A "proposed manager candidate" is a person selected based on the analysis results as being suitable to manage the machinery and equipment.

[0145] The term "selected administrator" refers to the person ultimately chosen from among the proposed administrator candidates.

[0146] The system for implementing this invention is designed to efficiently select managers for machinery and equipment within a factory. The server uses artificial intelligence to analyze human thought patterns and proposes manager candidates based on past behavioral history and evaluation data. Specifically, it uses Python and leverages machine learning libraries such as TensorFlow and PyTorch to analyze candidate data.

[0147] Smart glasses are used as the terminal, displaying real-time information on suggested administrator candidates and support for selection. This allows users to visually confirm the strengths and weaknesses of candidates and make the best choice.

[0148] As a concrete example, maintenance history and troubleshooting records of robots within a factory are used as data. Based on this, the server inputs a prompt message into the AI ​​model saying, "Please suggest the most suitable robot administrator based on past maintenance history and troubleshooting records," and then suggests the most suitable administrator candidates.

[0149] This system is expected to enable efficient and accurate selection of managers within the factory, and to facilitate a smooth transition of leadership.

[0150] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0151] Step 1:

[0152] The server collects past behavioral history and evaluation data of management candidates within the factory. This data is retrieved from a database and used as input to analyze the candidates' thought patterns.

[0153] Step 2:

[0154] The server runs an artificial intelligence model using the collected data. Specifically, a machine learning model built using TensorFlow or PyTorch analyzes the candidates' thinking patterns. This analysis outputs the characteristics and tendencies of each candidate.

[0155] Step 3:

[0156] The server generates a list of administrator candidates based on the analysis results. Using the generation AI model, it receives the prompt message, "Please suggest the most suitable robot administrator based on past maintenance history and troubleshooting experience," and proposes the most suitable candidates. This list takes into account the candidates' aptitudes and strengths.

[0157] Step 4:

[0158] The smart glasses, acting as the terminal, receive a list of administrator candidates sent from the server and display it to the user. The user can view detailed information about the candidates and make a selection through the smart glasses.

[0159] Step 5:

[0160] The user selects the most suitable administrator candidate based on the information displayed through their smart glasses. The selected information is sent to the server, and the final approval process begins.

[0161] Step 6:

[0162] The server generates the necessary information to obtain approval for the selected administrator candidate and notifies the relevant parties. This allows the process for the selected administrator to officially assume their role to proceed.

[0163] (Example 3)

[0164] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0165] Traditional successor selection processes have made it difficult to accurately analyze candidates' thought patterns and propose suitable successors. Furthermore, the selection and approval processes for proposed candidates lacked support based on objective information, resulting in decreased efficiency and accuracy.

[0166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0167] In this invention, the server includes means for collecting information, means for pre-processing the collected information, and means for analyzing thought patterns using the pre-processed information. This makes it possible to accurately analyze the thought patterns of candidates and propose appropriate successors. It also enables the efficient process of assisting in the selection of proposed candidates and obtaining approval from the selected candidates.

[0168] "Means of collecting information" refers to methods and devices for obtaining data on a candidate's past actions, statements, and evaluations.

[0169] "Means for preprocessing collected information" refers to methods or devices for converting acquired data into a format that is easily processed by AI models.

[0170] "Means for analyzing thought patterns" refers to methods or devices that use pre-processed data to analyze candidates' thought patterns using machine learning algorithms.

[0171] "Means of proposing candidates" refers to methods or devices for selecting and presenting the most suitable successor candidate based on the analysis results.

[0172] "Means of supporting selection" refer to methods or devices that provide information to help select the most suitable person from among the proposed candidates.

[0173] "Means of obtaining approval" refers to methods and devices that provide the necessary information to selected candidates to enable them to obtain approval and support the approval process.

[0174] To implement this invention, a server plays a central role. The server collects data about candidates, preprocesses it, and inputs it into the AI ​​model. Specifically, the server uses a database management system to obtain information such as candidates' past actions, statements, and evaluations. For data preprocessing, NLTK, a Python natural language processing library, is used to tokenize the text data and remove unnecessary words.

[0175] The server inputs pre-processed data into an AI model using machine learning frameworks such as TensorFlow and PyTorch to analyze the candidates' thought patterns. This analysis models the candidates' past behavior and values, and proposes the most suitable successor candidate.

[0176] The user receives candidate suggestions from the server and makes a selection. The server evaluates each candidate's strengths and weaknesses and provides the user with this information in HTML format. This allows the user to choose the best candidate based on objective information.

[0177] Furthermore, the server generates presentation materials to obtain approval from the selected candidates. Specifically, it creates slides in PowerPoint format that demonstrate the suitability of the selected candidates and provides the user with a download link.

[0178] As a concrete example, by inputting the following prompt into the generative AI model, you can obtain suggestions for successor candidates.

[0179] Prompt: "Please suggest a successor candidate whose thinking pattern most closely matches that of the current leader. Please consider the candidate's past project choices and values."

[0180] By using this prompt, the AI ​​can suggest a suitable successor candidate. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0181] Step 1:

[0182] The server collects data about the candidates. As input, it retrieves information about the candidates' past actions, statements, and evaluations from a database. Specifically, the server executes SQL queries to extract the necessary data. The output is the collected raw data.

[0183] Step 2:

[0184] The server preprocesses the collected data. It uses the raw data obtained in step 1 as input. Specifically, the server tokenizes the text data using the Python NLTK library and removes stop words. The output is the preprocessed, clean data.

[0185] Step 3:

[0186] The server analyzes thought patterns using pre-processed data. The clean data obtained in step 2 is input to the AI ​​model. Specifically, the server uses TensorFlow to execute machine learning algorithms and extract the candidates' thought patterns. The output is the analyzed thought pattern data.

[0187] Step 4:

[0188] The server proposes successor candidates based on the analysis results. It uses the thought pattern data obtained in step 3 as input. Specifically, the server performs similarity calculations and selects the most suitable candidate. The output is a list of proposed successor candidates.

[0189] Step 5:

[0190] The user receives information to select the most suitable person from the proposed candidates. The candidate list obtained in step 4 is used as input. Specifically, the server evaluates the strengths and weaknesses of each candidate and provides this information to the user in HTML format. The output is detailed candidate information that the user can view.

[0191] Step 6:

[0192] The server generates materials to obtain approval for the selected successor. It uses information about the candidate selected by the user as input. Specifically, the server creates a presentation in PowerPoint format and provides the user with a download link. The output is materials to support the approval process.

[0193] (Application Example 3)

[0194] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0195] Traditional successor selection processes have made it difficult to accurately analyze candidates' thought patterns and select the appropriate successor. Furthermore, the lack of real-time information during the selection process has resulted in reduced efficiency and accuracy.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0197] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, and means for presenting the analysis results using a visual display device. This enables accurate analysis of candidates' thought patterns and efficient and accurate successor selection through real-time information provision.

[0198] "Methods for analyzing human thought patterns" refer to technologies that use artificial intelligence to analyze a candidate's thinking tendencies based on data such as their past actions, statements, and evaluations.

[0199] "A method for proposing successor candidates based on analysis results" is a technique that, based on analyzed thought patterns, presents individuals who have a similar way of thinking to the current leader or who can offer new perspectives as potential successors.

[0200] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide information on each candidate's strengths, weaknesses, and suitability in order to select the most suitable person from among the presented candidates.

[0201] "Means of obtaining approval for selected successors" refers to technologies that provide information demonstrating the suitability and abilities of a selected successor, thereby supporting the approval process and enabling them to gain approval.

[0202] "Means of presenting analysis results using a visual display device" refers to a technology that uses visual devices such as smart glasses to display the analyzed information to the user in real time.

[0203] "Methods for collecting candidates' past behavioral data and analyzing it with artificial intelligence models" refers to technologies that collect candidates' past behavioral history and evaluation data, input this data into an artificial intelligence model, and analyze it.

[0204] "Means of displaying analysis results in real time" refers to technology that instantly displays the analyzed information on a visual display device, allowing users to check that information in real time.

[0205] The system for implementing this invention operates through the coordinated efforts of a server, a terminal, and a user. The server collects past behavioral data of candidates and analyzes this data using an artificial intelligence model. Specifically, it uses an AI model built with Python (e.g., TensorFlow) to analyze the candidates' thought patterns. The analyzed data is presented to the user in real time through a visual display device, such as smart glasses (e.g., Google Glass®).

[0206] The terminal receives analysis results sent from the server and displays them on a visual display device. The user wears smart glasses and selects a successor based on the presented information. This allows the user to check the candidate's thought patterns in real time and select the appropriate successor.

[0207] As a concrete example, during leadership training within a factory, the server uses AI to analyze the candidate's past project management skills and teamwork evaluations, and displays a message on smart glasses via a terminal saying, "Candidate A has strong project management skills and excellent teamwork abilities."

[0208] An example of a prompt to input into the generating AI model is: "Based on candidate A's past project management data, analyze their thinking patterns and evaluate their leadership aptitude."

[0209] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0210] Step 1:

[0211] The server collects past behavioral data of candidates. It uses candidate behavioral history and evaluation data obtained from the factory's management system as input. This data is stored in a database in preparation for subsequent analysis.

[0212] Step 2:

[0213] The server inputs the collected data into an artificial intelligence model to analyze the candidates' thinking patterns. The behavioral data collected in Step 1 is used as input. Using an AI model (e.g., TensorFlow), the candidate's thinking tendencies and leadership aptitude are analyzed, and the analysis results are generated. The output is the analyzed thinking pattern data.

[0214] Step 3:

[0215] The server sends the analysis results to the terminal. The thought pattern data obtained in step 2 is used as input. The terminal prepares to display the received data on a visual display device. The output generates data in a displayable format.

[0216] Step 4:

[0217] The terminal presents the analysis results to the user through a visual display device. The displayable data prepared in step 3 is used as input. Information such as "Candidate A has strong project management skills and excellent teamwork abilities" is displayed in real time on the smart glasses. The output provides information that the user can visually confirm.

[0218] Step 5:

[0219] The user selects a successor based on the information presented. The input is the analysis results displayed on smart glasses. The user evaluates the suitability and abilities of the candidates and selects the most suitable successor. The output is information about the selected successor.

[0220] 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.

[0221] "Example of form 1"

[0222] One embodiment of the present invention provides a successor selection support system that incorporates an emotion engine. This system uses an emotion engine that recognizes the user's emotions as a means of analyzing human thought patterns. The emotion engine recognizes emotions from the user's facial expressions, tone of voice, and choice of words, and uses the results to analyze thought patterns. For example, if a user shows joy or excitement, it is determined that the user is likely to have a positive thought pattern. Conversely, if a user shows anger or dissatisfaction, it is determined that the user is likely to have a negative thought pattern.

[0223] "Example of form 2"

[0224] Furthermore, the results of the emotion recognition process by the emotion engine will also be taken into consideration when proposing successor candidates. Specifically, the emotional state of the candidates will be analyzed, and successor candidates will be proposed based on the results. For example, if a candidate can consistently maintain a calm and composed emotional state, it will be judged that the candidate is likely to be able to make calm judgments even under pressure, and will be proposed as a successor.

[0225] "Example of form 3"

[0226] Furthermore, as a means of obtaining approval from the selected successor, the system predicts the approver's reaction based on the emotion recognition results from the emotion engine. Specifically, it analyzes the approver's emotional state and predicts the likelihood that the approver will approve the successor based on the results. For example, if the approver shows joy or excitement, it is predicted that the approver is likely to approve the successor.

[0227] The following describes the processing flow for each example of the form.

[0228] "Example of form 1"

[0229] Step 1: Activate the emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and word choice.

[0230] Step 2: The emotion engine recognizes the user's emotions and uses the results to analyze their thought patterns.

[0231] Step 3: If a user shows joy or excitement, it is likely that they have a positive thought pattern.

[0232] Step 4: If a user expresses anger or dissatisfaction, it is likely that the user has a negative thought pattern.

[0233] "Example of form 2"

[0234] Step 1: Analyze the candidate's emotional state using the emotion engine.

[0235] Step 2: Propose successor candidates based on the analysis results.

[0236] Step 3: If a candidate can consistently maintain a calm and composed emotional state, they are likely to be able to make calm decisions even under pressure, and should be proposed as a successor.

[0237] "Example of form 3"

[0238] Step 1: Analyze the approver's emotional state using the emotion engine.

[0239] Step 2: Based on the analysis results, predict the likelihood that the approver will approve the successor.

[0240] Step 3: If the approver shows joy or excitement, predict that they are likely to approve the successor.

[0241] (Example 1)

[0242] Next, we will describe Example 1 of Form 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."

[0243] Traditional successor selection processes often fail to adequately consider candidates' thought patterns and emotions, making it difficult to select a suitable successor. Furthermore, these processes often lack objective data-based evaluations and rely heavily on subjective judgments. Therefore, improving the accuracy of successor selection is crucial.

[0244] 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.

[0245] In this invention, the server includes means for collecting human behavioral data, means for analyzing thought patterns using the collected data, and means for analyzing the user's emotions using emotion recognition technology and reflecting them in the thought patterns. This makes it possible to comprehensively evaluate the thought patterns and emotions of candidates and objectively select an appropriate successor.

[0246] "Human behavioral data" refers to information including an individual's past actions, statements, and evaluations, and is used to analyze thought patterns.

[0247] "Thinking patterns" are characteristics that indicate an individual's values ​​and judgment criteria, and are analyzed from their past actions and statements.

[0248] "Emotion recognition technology" is a technology that analyzes emotions from a user's facial expressions, tone of voice, and word choice, and uses the results to analyze their thought patterns.

[0249] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate results that meet specific objectives.

[0250] A "potential successor" refers to a person who has the potential to take on the next leadership role within an organization or group.

[0251] "Aptitude" refers to characteristics that indicate an individual's ability or suitability for a particular role or job.

[0252] This invention provides a concrete method for realizing a successor selection support system. First, the server collects human behavior data. This data is obtained from corporate databases and publicly available information and extracted using SQL queries. Next, the server analyzes thought patterns using the collected data. For the analysis, machine learning frameworks such as TensorFlow and PyTorch are used to construct a neural network model. This allows the system to analyze the candidate's past behavior and values ​​and learn their thought patterns.

[0253] Furthermore, the server analyzes the user's emotions using emotion recognition technology. The emotion engine recognizes emotions from the user's facial expressions, tone of voice, and word choice, and uses the results to analyze thought patterns. For example, if a user shows joy or excitement, the system determines that the user is likely to have positive thought patterns.

[0254] The device provides users with information on proposed successor candidates. Specifically, it displays a dashboard that visualizes each candidate's strengths, weaknesses, and suitability, helping users select the most suitable successor.

[0255] Using a generative AI model, the server evaluates the suitability of candidates. An example of a prompt is: "Based on Candidate A's past behavioral data, analyze their thinking patterns suitable for leadership and evaluate their suitability as a successor candidate." In response to this prompt, the generative AI model analyzes Candidate A's data and evaluates their suitability as a successor.

[0256] In this way, servers, terminals, and users can cooperate to efficiently carry out the successor selection process.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0258] Step 1:

[0259] The server collects candidate behavior data from the company's database. Inputs include candidate IDs and queries to the relevant database. The server executes SQL queries to extract data such as the candidate's past behavior, statements, and evaluations. The output is a dataset formatted for analysis. Specifically, a Python script is used to connect to the database and retrieve the necessary data.

[0260] Step 2:

[0261] The server analyzes thought patterns using the collected data. The input is the dataset obtained in Step 1. The server builds a neural network model using TensorFlow and analyzes the data. This allows it to learn the candidates' thought patterns and generate analysis results. The output is a feature vector representing each candidate's thought pattern. Specifically, the data is preprocessed, input into the model, and then analyzed.

[0262] Step 3:

[0263] The server analyzes the user's emotions using emotion recognition technology. The input consists of emotional data such as the user's facial expressions, tone of voice, and word choice. The server uses an emotion engine to analyze this data and determine the user's emotional state. The output is an indicator of the user's emotions. Specifically, it analyzes data acquired from cameras and microphones in real time.

[0264] Step 4:

[0265] The server proposes successor candidates based on the analysis results. The input is the thought patterns and emotional indicators obtained in steps 2 and 3. The server uses a generative AI model to evaluate the suitability of the candidates and lists the most suitable successor candidates. The output is a list of successor candidates. Specifically, the AI ​​model is run using the prompt message "Based on the candidates' thought patterns, please propose individuals suitable for leadership."

[0266] Step 5:

[0267] The terminal provides the user with information on the proposed successor candidates. The input is the list of successor candidates obtained in step 4. The terminal displays a dashboard that visualizes each candidate's strengths, weaknesses, and suitability. The output is an interface for candidate information that the user can view. Specifically, a web application is used to generate the dashboard and present it to the user.

[0268] Step 6:

[0269] The user selects the most suitable successor based on the information provided from the terminal. The input is the candidate information displayed in step 5. The user compares the information of each candidate and selects the most suitable person. The output is the ID of the selected successor. Specifically, the user selects a candidate on the interface and sends the selection result to the server.

[0270] Step 7:

[0271] The server assists in the process of obtaining approval for the selected successor. The input is the ID of the successor selected in step 6. The server generates a report showing the suitability and abilities of the selected successor and distributes it to the relevant parties. The output is a report to support the approval process. Specifically, the report is generated in PDF format and sent to the relevant parties via email.

[0272] (Application Example 1)

[0273] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0274] In the conventional method of selecting a robot manager in a factory, there is a problem that it is difficult to fully consider the thinking patterns and emotions of candidates and select the optimal manager. In particular, there is a lack of analysis based on the emotions and past behaviors of candidates, making it difficult to inherit appropriate leadership.

[0275] 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.

[0276] In this invention, the server includes means for analyzing human thinking patterns, means for recognizing emotions, and means for utilizing the recognized emotions in the analysis of thinking patterns. Thereby, it becomes possible to propose candidate managers in the factory and select the optimal manager.

[0277] The "means for analyzing human thinking patterns" is a technology for analyzing the thinking tendencies and values of a person based on the past behaviors, speeches, and evaluations of the candidate.

[0278] The "means for proposing successor candidates based on the analysis results" is a technology for selecting optimal successor candidates using the analysis results of thinking patterns.

[0279] The "means for assisting in the selection of the proposed successor candidates" is a technology for providing the information necessary to select the optimal person from the proposed candidates.

[0280] The "means for obtaining approval of the selected successor" is a technology for providing information indicating the suitability and ability of the person in order for the selected successor to obtain approval.

[0281] The "means for recognizing emotions" is a technology for analyzing emotions from the user's facial expressions, voice tones, word choices, etc.

[0282] The "means for utilizing the recognized emotions in the analysis of thinking patterns" is a technology for using the results of emotion analysis to perform more refined analysis of thinking patterns.

[0283] "A method for proposing candidates for factory managers" refers to a technology that uses the results of thought pattern and emotion analysis to select candidates for robot managers within a factory.

[0284] To implement this invention, a system is built in which a server plays a central role. The server runs a program that integrates an artificial intelligence model and an emotion engine. Specifically, it uses Python combined with TensorFlow and OpenCV to collect and analyze the candidate's past behavior logs, voice data, and facial expression data. This makes it possible to analyze the candidate's thought patterns and emotions in detail.

[0285] The server first retrieves the candidate's past actions and statements from a database and analyzes their thought patterns using an artificial intelligence model. Next, it uses an emotion engine to recognize emotions from voice and facial expression data and utilizes the results in the analysis of thought patterns. This provides the basic data needed to propose candidates for management positions within the factory.

[0286] As a concrete example, the server evaluates candidate A's suitability as the optimal robot administrator based on their past behavioral logs and emotional data. An example of a prompt to be input to the generating AI model in this case would be, "Please evaluate candidate A's suitability as the optimal robot administrator based on their past behavioral logs and emotional data."

[0287] This system allows users to obtain information to select the most suitable robot manager within the factory, enabling them to build a more efficient management system.

[0288] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0289] Step 1:

[0290] The server retrieves past behavior logs, voice data, and facial expression data of candidates from the database. The input is the candidate's ID, and the output is the corresponding dataset. This collects information about the candidate's past behavior and statements.

[0291] Step 2:

[0292] The server inputs the acquired behavioral logs into an artificial intelligence model and analyzes the thought patterns. The input is behavioral log data, and the output is the result of the thought pattern analysis. As part of the data processing, the behavioral logs are converted into features and then analyzed by the AI ​​model.

[0293] Step 3:

[0294] The server inputs voice data and facial expression data into the emotion engine to recognize emotions. The input is voice data and facial expression data, and the output is the result of emotion recognition. It analyzes changes in voice tone and facial expression to identify emotions.

[0295] Step 4:

[0296] The server integrates the results of thought pattern analysis and emotion recognition to evaluate the suitability of management candidates. The input is data on thought patterns and emotions, and the output is the result of the suitability evaluation. The data calculation involves comparing the two and quantifying the suitability.

[0297] Step 5:

[0298] The server uses a generative AI model to generate prompt messages and suggest the most suitable administrator candidates. The input is the result of the aptitude assessment, and the output is the prompt message and candidate suggestions. It performs the operation of generating the prompt message, "Please evaluate candidate A's suitability as the most suitable robot administrator based on their past behavior logs and sentiment data."

[0299] Step 6:

[0300] Based on the information provided by the server, the user selects the optimal administrator candidate. The input is the proposal from the server, and the output is the selected administrator candidate. The user examines the provided data and performs the operation of determining the optimal candidate.

[0301] (Embodiment 2)

[0302] Next, Embodiment 2 of Form Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0303] [ In the conventional successor selection process, it is difficult to fully consider an individual's decision-making pattern and emotional state, and there is a problem that it is difficult to select an appropriate successor. Also, in the selection of successor candidates, there is a problem that it is impossible to evaluate the degree of consistency with the current leader's thinking pattern and the possibility of providing a new perspective.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Embodiment 2 is realized by the following means.

[0305] In this invention, the server includes means for analyzing an individual's decision-making pattern, means for presenting successor candidates based on the analysis result, and means for recognizing an emotional state and presenting successor candidates based on the result. As a result, it becomes possible to select a successor considering an individual's decision-making pattern and emotional state, and it becomes possible to efficiently select an appropriate successor.

[0306] An "information processing device" is a computer system for collecting, analyzing, and processing data.

[0307] An "individual's decision-making pattern" indicates the tendency of a specific individual's past choices and judgments.

[0308] "Analysis" is a process of investigating data in detail and understanding its structure and meaning. <A

[0309] A "potential successor" is someone who has the potential to take over the role of the current leader.

[0310] "Presentation" refers to the act of showing specific information or options to others.

[0311] "Emotional state" refers to the emotional state an individual is experiencing at a particular moment.

[0312] "Machine learning technology" is a technique that allows computers to learn patterns from data and make predictions and decisions.

[0313] A "similar individual" is a person who shares common characteristics with other individuals based on specific criteria.

[0314] A "new perspective" refers to a new viewpoint or approach that differs from conventional ways of thinking or methods.

[0315] This invention provides a specific model for implementing a successor selection support system. The server collects data from a database, including candidates' past actions, statements, and evaluations, in order to analyze their individual decision-making patterns. This is achieved by using SQL queries to extract information from the database.

[0316] The server uses the collected data to build a machine learning model using the Python TensorFlow library, and learns and analyzes the candidates' decision-making patterns. This model uses a neural network to analyze the candidates' thinking patterns from their past actions and statements.

[0317] Furthermore, the server uses an emotion engine to recognize the emotional state of the candidates and presents potential successors based on the results. Emotion recognition uses algorithms to analyze emotional states.

[0318] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "Based on Candidate A's past leadership evaluation data, analyze their thinking patterns and evaluate their suitability as a successor." By using this prompt sentence, the AI ​​model can analyze Candidate A's data and evaluate their suitability as a successor.

[0319] In this way, the server can select a successor by taking into account an individual's decision-making patterns and emotional state, enabling it to efficiently select an appropriate successor.

[0320] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0321] Step 1:

[0322] The server collects data from the database, including the candidate's past actions, statements, and evaluations. Input requires the candidate's ID and related queries. The server uses SQL queries to access the database and extract the necessary information. Output includes the candidate's past behavioral history and evaluation data.

[0323] Step 2:

[0324] The server uses the TensorFlow library in Python to build a machine learning model based on the collected data. The input requires candidate data obtained in Step 1. The server uses this data to train a neural network and learn the candidates' decision-making patterns. The output is a model representing the candidates' thinking patterns.

[0325] Step 3:

[0326] The server uses a machine learning model to analyze the candidates' thought patterns. The input requires the model built in Step 2 and the candidates' data. The server applies the model to analyze the candidates' decision-making tendencies and values. The output provides the analysis results, which are then used to suggest potential successors.

[0327] Step 4:

[0328] The server uses an emotion engine to recognize the candidate's emotional state. Input requires the candidate's voice data and facial expression data. The server applies an emotion recognition algorithm to analyze the candidate's emotional state. The output provides information about the candidate's emotional state.

[0329] Step 5:

[0330] The server presents potential successors based on the analysis results and emotional state. The input requires the data obtained in steps 3 and 4. The server integrates this information and presents individuals with similar thought patterns to the current leader, as well as individuals who may offer new perspectives. The output is a list of potential successors.

[0331] Step 6:

[0332] The server provides information to select the most suitable person from the presented successor candidates. The input is the candidate list obtained in step 5. The server evaluates each candidate's strengths, weaknesses, and suitability, and generates information to support the selection process. The output is selection support information.

[0333] Step 7:

[0334] The server assists in obtaining approval for the selected successor. The input requires information on the candidate selected in step 6. The server generates a report outlining the suitability and capabilities of the selected successor, supporting the approval process. The output is an approval support report.

[0335] (Application Example 2)

[0336] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0337] In selecting successors, there is a need for a more objective and efficient selection process by appropriately analyzing candidates' thought patterns and emotional states and presenting the information visually. However, traditional methods make it difficult to comprehensively consider these factors, and the selection process tends to be subjective.

[0338] 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.

[0339] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, means for visually presenting candidate information using a visual display device, and means for analyzing emotional states. This enables a more objective and efficient successor selection by comprehensively analyzing the candidates' thought patterns and emotional states and visually presenting the information.

[0340] "Methods for analyzing human thought patterns" refer to techniques that analyze a person's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[0341] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting appropriate successor candidates using the results of analyzing thought patterns.

[0342] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide the information necessary to select the most suitable person from among the presented candidates.

[0343] "Means of obtaining approval for selected successors" refers to technologies that support the information and procedures necessary for a selected successor to be formally approved.

[0344] "Means of visually presenting candidate information using a visual display device" refers to a technology that displays information about a candidate through a visual device such as a display, making it easy for users to understand.

[0345] "Methods for analyzing emotional states" refer to techniques that analyze the emotional state of candidates and utilize the results in selecting a successor.

[0346] The system for implementing this invention operates through the coordinated efforts of a server, a terminal, and a user. The server uses artificial intelligence to analyze human thought patterns. Specifically, it collects data on candidates' past actions, statements, and evaluations, and analyzes this data using a generative AI model. Based on the analysis results, it generates data to propose successor candidates.

[0347] The terminal acts as a visual display device, visually presenting analysis results transmitted from the server to the user. For example, it may use smart glasses or a tablet device to display information such as the candidate's strengths, weaknesses, and aptitudes. Furthermore, it may use an emotion engine to analyze the candidate's emotional state in real time and visually present the results.

[0348] The user selects successor candidates based on information presented through their device. The server then assists in obtaining approval for the selected candidates. Specifically, it provides information indicating the suitability and abilities of the selected candidates, facilitating a smooth approval process.

[0349] As a concrete example, when selecting a robot manager in a factory, the current manager, wearing smart glasses, reviews the candidates' past project decisions and evaluations, and then selects a candidate capable of making calm judgments based on the analysis results from the emotion engine. An example of a prompt to the generated AI model would be: "Analyze candidate A's past project decisions and evaluate their thought patterns. Also, based on the analysis results from the emotion engine, evaluate their ability to make decisions under pressure."

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0351] Step 1:

[0352] The server collects data on candidates' past behavior, statements, and evaluations. As input, it retrieves databases and historical information related to the candidates and prepares this as a dataset for analysis. As output, it generates a dataset formatted for analysis.

[0353] Step 2:

[0354] The server uses a generative AI model to analyze candidates' thought patterns from the collected dataset. The dataset formatted in Step 1 is used as input, and prompt sentences are fed into the AI ​​model. As data processing, the AI ​​model analyzes the data and extracts candidates' thought patterns. The output is the analysis results of the thought patterns.

[0355] Step 3:

[0356] The server proposes successor candidates based on the analysis results. It uses the analysis results of the thought patterns obtained in step 2 as input. As data calculation, it applies an algorithm to evaluate the analysis results and select the optimal successor candidate. As output, it generates a list of proposed successor candidates.

[0357] Step 4:

[0358] The terminal uses a visual display device to visually present information about proposed successor candidates to the user. It receives a list of successor candidates and their detailed information from a server as input. Specifically, it displays the candidates' strengths, weaknesses, and suitability on the display of smart glasses or a tablet device. As output, it provides information that the user can visually confirm.

[0359] Step 5:

[0360] The device uses an emotion engine to analyze the candidate's emotional state in real time and presents the results to the user. The input is real-time emotional data of the candidate. The data processing involves the emotion engine analyzing the data and evaluating the emotional state. The output is a visual display of the emotional state analysis results.

[0361] Step 6:

[0362] The user selects a successor candidate based on information presented through the terminal. The input consists of candidate information and an analysis of their emotional state, both provided by the terminal. The user makes a decision based on the presented information and selects the most suitable successor. The output is the transmission of the selected successor's information to the server.

[0363] Step 7:

[0364] The server assists in obtaining approval for the selected successor. It receives selection results submitted by users as input. As a data processing tool, it organizes information indicating the suitability and abilities of the selected successor and creates materials to support the approval process. As output, it provides the necessary information for approval to the relevant parties.

[0365] (Example 3)

[0366] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0367] Traditional successor selection processes have challenges in accurately evaluating candidates' thought patterns and aptitudes, and they lack an approval process that takes into account the emotional state of approvers, making it difficult to select a suitable successor.

[0368] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0369] In this invention, the server includes means for collecting and pre-processing information, means for analyzing thought patterns using artificial intelligence, and means for analyzing the emotional state of approvers using an emotion engine and obtaining approval. This makes it possible to accurately analyze the thought patterns of candidates, propose appropriate successors, and realize an approval process that takes into account the emotional state of approvers.

[0370] "Means of collecting and pre-processing information" refers to the process of collecting data about candidates and preparing it in a format suitable for analysis.

[0371] "Methods for analyzing thought patterns using artificial intelligence" refers to the process of using machine learning techniques to extract thinking tendencies and values ​​from a candidate's past actions and statements.

[0372] "Methods for proposing successor candidates" refers to the process of selecting and presenting appropriate successor candidates based on the analysis results.

[0373] "Means of supporting the selection of proposed successor candidates" refers to a process of evaluating the strengths, weaknesses, and aptitudes of candidates and providing information to select the most suitable successor.

[0374] "A means of obtaining approval by analyzing the emotional state of approvers using an emotion engine" refers to a process that analyzes the emotions of approvers, predicts the likelihood of approval based on the results, and supports the approval process.

[0375] A description of embodiments for carrying out this invention will be given.

[0376] The server first collects and preprocesses data about candidates. This data collection utilizes company databases and publicly available information sources. Preprocessing involves cleaning text data and normalizing numerical data to prepare the data for analysis. This process uses database management systems (DBMS) and data processing software.

[0377] Next, the server uses artificial intelligence to analyze thought patterns. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to extract thinking tendencies and values ​​from the candidate's past actions and statements. This makes it possible to clearly identify the candidate's characteristics.

[0378] The server then proposes successor candidates based on the analysis results. The artificial intelligence selects appropriate candidates by considering similarities to the current leader's thought patterns and new perspectives. The list of proposed candidates is sent to the terminal.

[0379] The device displays detailed information about the proposed successor candidates to the user. Based on the information provided through the device, the user selects the most suitable successor. To assist in the selection process, each candidate's strengths, weaknesses, and suitability are evaluated and displayed.

[0380] Finally, the server uses an emotion engine to analyze the approver's emotional state and obtain approval. Emotion recognition software is used to analyze the approver's emotions and predict the likelihood of approval based on the results. This ensures a smooth approval process.

[0381] As a concrete example, when a user inputs the prompt "Analyze candidate A's past leadership style and values, and evaluate their suitability as a successor by comparing them to the current leader" into the generating AI model, the server analyzes candidate A's data, evaluates their suitability, and displays the results on the terminal. The specific processing flow in Example 3 is explained using Figure 21.

[0382] Step 1:

[0383] The server collects data about candidates. It uses data from company databases and publicly available sources as input. Specifically, it accesses the database via an API to retrieve data on candidates' past behavior, statements, and evaluations. The output is the collected raw data.

[0384] Step 2:

[0385] The server preprocesses the collected data. The input is the raw data collected in step 1. Specifically, it cleans text data (removes unnecessary characters and noise) and normalizes numerical data. This prepares the data for analysis. The output is the preprocessed, clean data.

[0386] Step 3:

[0387] The server analyzes thought patterns using artificial intelligence. The input is the data preprocessed in step 2. Specifically, it trains machine learning models using TensorFlow or PyTorch to extract the thought patterns of the candidates. The output is the analysis results showing the thought patterns of each candidate.

[0388] Step 4:

[0389] The server proposes successor candidates based on the analysis results. The input is the analysis results obtained in step 3. Specifically, it runs an algorithm that selects appropriate candidates by considering similarities to the current leader's thinking patterns and new perspectives. The output is a list of proposed successor candidates.

[0390] Step 5:

[0391] The terminal displays detailed information about the proposed successor candidates to the user. The input is the candidate list generated in step 4. Specifically, it evaluates each candidate's strengths, weaknesses, and suitability, and displays the information in a user-friendly format. The output is the candidate information that the user can view.

[0392] Step 6:

[0393] The server uses an emotion engine to analyze the approver's emotional state and obtain approval. The input is the successor information selected by the user. Specifically, it uses emotion recognition software to analyze the approver's emotions and predict the likelihood of approval. The output is the predicted result regarding the likelihood of approval.

[0394] (Application Example 3)

[0395] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0396] In factory settings, selecting successors for machinery and equipment managers often relies on subjective judgment, making it difficult to select suitable personnel. Furthermore, failing to consider the emotional state of the approvers during the selection process can hinder its smooth execution.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0398] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, and means for analyzing the emotional state of approvers and predicting the likelihood of approval. This enables objective and efficient selection of successors for managers of machinery and equipment in factories.

[0399] "Human thought patterns" refer to the unique tendencies and processes by which individual people act and make decisions based on their past experiences and values.

[0400] "Analysis results" refer to the data and information obtained after artificial intelligence analyzes human thought patterns.

[0401] A "successor candidate" refers to a person who has the potential to take over a specific role or position.

[0402] "Proposed successor candidates" refers to individuals selected by artificial intelligence and to be evaluated in the next stage.

[0403] An "approver" refers to a person or organization that has the authority to ultimately certify a successor candidate.

[0404] "Emotional state" refers to the psychological reactions and feelings that an approver exhibits in response to a particular situation or information.

[0405] A "machinery manager" refers to the person responsible for the operation and maintenance of machinery and equipment within a factory.

[0406] A "successor selection support system" refers to a system that uses artificial intelligence to support the process from selecting a successor candidate to approving them.

[0407] A server plays a central role in implementing this invention. The server runs a program that uses artificial intelligence to analyze human thought patterns. Specifically, it uses Python and the machine learning library scikit-learn to analyze candidates' past behavior and evaluation data. This makes it possible to cluster candidates' thought patterns and propose successor candidates.

[0408] Furthermore, the server uses Hugging Face's Transformers to analyze the approver's emotional state. This allows it to predict the likelihood that the approver will approve the successor candidate. The results of the emotional analysis provide crucial information for facilitating a smooth approval process.

[0409] The device functions as a smartphone or tablet, receiving information provided by the server. Through the device, the user can review the proposed successor candidates and evaluate the likelihood of approval based on the approver's emotional state.

[0410] As a concrete example, based on candidate A's past work history and evaluation data, the AI ​​proposes candidate A as a successor candidate. Furthermore, an analysis of the approver's emotional state predicts that the approver has favorable feelings towards candidate A, thus indicating a high probability of approval.

[0411] An example of a prompt would be: "Based on Candidate A's past work history and evaluation data, analyze their suitability as a successor candidate. Also, analyze the approver's emotional state and predict the likelihood of approval."

[0412] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0413] Step 1:

[0414] The server collects data on candidates' past behavior and evaluations. The input consists of the candidate's work history and evaluation information. This data is preprocessed, and features are extracted. Specifically, this involves data cleaning and normalization.

[0415] Step 2:

[0416] The server uses scikit-learn to cluster the candidates' thinking patterns based on preprocessed data. The input is the features extracted in step 1. As a data operation, a clustering algorithm is applied to classify the candidates' thinking patterns. The output is the cluster information for each candidate.

[0417] Step 3:

[0418] The server proposes successor candidates based on the clustering results. The input is the cluster information from step 2. As a data processing step, the candidates within the cluster are evaluated, and the most suitable successor candidate is selected. The output is a list of proposed successor candidates.

[0419] Step 4:

[0420] The server uses Hugging Face's Transformers to analyze the approver's emotional state. The input consists of the approver's past statements and behavioral data. The data is then processed by applying an emotion analysis model to evaluate the approver's emotional state. The output is the evaluation result of the approver's emotional state.

[0421] Step 5:

[0422] The server predicts the likelihood that approvers will approve successor candidates based on the sentiment analysis results. The input is the result of the emotional state evaluation in step 4. As a data processing step, the relationship between emotional state and approval is evaluated, and the likelihood of approval is calculated. The output is the predicted likelihood of approval.

[0423] Step 6:

[0424] The terminal presents the user with a list of potential successors provided by the server and a prediction of the likelihood of approval. The input is the output from steps 3 and 5. Specifically, the information is displayed through the user interface for the user to review.

[0425] (Other examples)

[0426] Next, other embodiments will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0427] In modern organizations, succession planning is a crucial issue, but traditional methods tend to rely on subjective judgment, making it difficult to find suitable candidates. Furthermore, while emotional factors must be considered in candidate selection, there is a lack of effective means to evaluate them.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in other embodiments is realized by the following means.

[0429] In this invention, the server includes means for receiving information from a user, means for inputting the received information as a prompt into a generating AI model and generating prompts to instruct the model to analyze thought patterns, and means for proposing successor candidates based on the analysis results by the generating AI model. This enables the objective and efficient selection of successor candidates and approval prediction that takes emotional factors into consideration.

[0430] "Means for receiving information from users" refers to the interface through which the server receives requests and conditions regarding successor selection entered by users.

[0431] A "prompt" is an input sentence used to instruct a generative AI model to perform a specific task.

[0432] A "generative AI model" is an artificial intelligence model that learns using historical datasets, analyzes thought patterns based on input prompts, and provides insights.

[0433] "Methods for analyzing thought patterns" refers to the process of using a generative AI model to analyze the thought patterns necessary for successor selection based on information received from the user.

[0434] The "method for proposing successor candidates" is a process that lists the successor candidates who best match the user's conditions, based on the analysis results of a generative AI model.

[0435] "Emotional analysis methods" are technologies that evaluate the emotional state of an approver and predict the likelihood of approval based on the results.

[0436] This invention is a system for efficiently and objectively selecting a successor. The system mainly consists of three elements: a server, a terminal, and a user.

[0437] The server provides an interface for receiving information from users. Users input their requests and conditions regarding successor selection through their terminals. For example, they can input information such as "skills required for the next leader" and "years of experience of the candidate."

[0438] The received information is generated by the server as a prompt. This prompt instructs the generating AI model to perform a specific task. An example of a specific prompt might be, "Analyze the skills required for the next leader and propose candidates."

[0439] The server inputs the generated prompt text into a generative AI model such as OpenAI's GPT-4®. The generative AI model is trained using historical datasets and analyzes thought patterns based on the input prompt. This provides insights into potential successors.

[0440] Based on the analysis results obtained from the generative AI model, the server proposes successor candidates. This proposal lists individuals who best match the user's criteria. The list includes information such as the candidates' skills, experience, and leadership style.

[0441] The terminal receives candidate proposals sent from the server and displays them to the user. The user can compare the characteristics of each candidate on the terminal screen. For example, graphs and tables can be displayed to visually compare candidates' skill sets and past performance.

[0442] Furthermore, the server evaluates the approver's emotional state in order to obtain approval from the selected successor. It uses Microsoft® Azure® sentiment analysis APIs to analyze the approver's emotional state and predicts the likelihood of approval based on the results. The analysis results are provided to the user as feedback.

[0443] This system allows users to make decisions based on objective data when selecting a successor, and enables approval predictions that also take emotional factors into account.

[0444] The flow of specific processing in other embodiments will be explained using Figure 23.

[0445] Step 1:

[0446] Users input information regarding successor selection through the terminal interface. Specifically, they input requirements and conditions such as "skills needed for the next leader" and "years of experience of the candidate." The entered information is sent to the server.

[0447] Step 2:

[0448] The server generates prompts based on the information received from the user. Specifically, it creates a prompt such as, "Analyze the skills required for the next leader and propose candidates," based on the input requests and conditions. This prompt functions as an instruction to the generating AI model.

[0449] Step 3:

[0450] The server inputs the generated prompt sentences into a generative AI model such as OpenAI's GPT-4. The generative AI model analyzes thought patterns based on the prompt sentences. It receives prompt sentences as input and provides insights into potential successors as output.

[0451] Step 4:

[0452] The server proposes successor candidates based on the analysis results obtained from the generated AI model. Specifically, it analyzes the results and lists the individuals who best match the user's criteria. As output, it generates a list that includes information such as the candidates' skills, experience, and leadership style.

[0453] Step 5:

[0454] The terminal receives candidate proposals sent from the server and displays them to the user. The user can compare the characteristics of each candidate on the terminal screen. Specifically, graphs and tables are displayed to visually compare candidates' skill sets and past performance.

[0455] Step 6:

[0456] The server evaluates the approver's emotional state to obtain approval from the selected successor. Specifically, it uses the Microsoft Azure Sentiment Analysis API to analyze the approver's emotional state. It receives the approver's emotional data as input and generates results predicting the likelihood of approval as output. The analysis results are provided to the user as feedback.

[0457] 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.

[0458] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and 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.

[0459] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0460] 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.

[0461] [Second Embodiment]

[0462] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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).

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0474] "Example of form 1"

[0475] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0476] "Example of form 2"

[0477] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0478] "Example of form 3"

[0479] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0480] The following describes the processing flow for each example of the form.

[0481] "Example of form 1"

[0482] Step 1: The artificial intelligence learns and analyzes the candidate's thought patterns from their past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold.

[0483] Step 2: Based on the analysis results, the artificial intelligence proposes potential successors by exploring individuals with similar thinking patterns or entirely new ones. Specifically, it considers factors such as how closely the candidate's thinking pattern matches that of the current leader, or whether they have the potential to bring a new perspective.

[0484] Step 3: Provide information to help select the most suitable person from the proposed candidates. Specifically, evaluate each candidate's strengths, weaknesses, and suitability, and use this information to support the selection process.

[0485] Step 4: Support the selected successor in gaining approval. Specifically, provide information demonstrating the suitability and abilities of the selected successor to help them gain approval.

[0486] (Example 1)

[0487] Next, we will describe Example 1 of Form 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".

[0488] Traditional succession selection processes often relied on subjective judgments, making it difficult to accurately assess candidates' thought patterns and aptitudes. As a result, there was a risk of overlooking suitable candidates when selecting successors to lead the organization into the future.

[0489] 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.

[0490] In this invention, the server includes means for collecting data, means for analyzing thought patterns using the collected data, and means for proposing successor candidates based on the analysis results. This makes it possible to objectively and efficiently select successor candidates and find suitable personnel to lead the organization into the future.

[0491] "Means of collecting data" refers to devices or methods for obtaining information such as a candidate's past actions, statements, and evaluations.

[0492] "Means for analyzing thought patterns" refers to a device or method for evaluating a candidate's decision-making patterns and values ​​using collected data.

[0493] "Means of proposing successor candidates" refers to a device or method for selecting and presenting individuals who have the potential to lead the organization in the future, based on the results of an analysis.

[0494] A "machine learning framework" is a software library used to train models with data and perform pattern recognition and prediction.

[0495] "Calculating similarity" is the process of comparing the thinking patterns of current leaders and candidates and quantifying the degree of similarity.

[0496] "Evaluating the potential to bring a new perspective" is the process of determining whether a candidate has the ability to bring innovation and transformation to the organization.

[0497] The embodiment of this invention provides a specific method for constructing a successor selection support system. The server first collects data on candidates' past behavior, statements, and evaluations. This data is obtained via APIs from corporate databases and publicly available information sources. Next, the server uses the collected data to train an artificial intelligence (AI) model and analyze the candidates' thought patterns. Machine learning frameworks such as TensorFlow and PyTorch are used in this process. The AI ​​models the candidates' decision-making patterns and values, and evaluates their past project success rates and leadership style.

[0498] Subsequently, the server uses the analysis results to have AI suggest potential successors. The AI ​​compares the current leader's thought patterns with those of the candidates and calculates the degree of similarity. It also evaluates candidates who may bring a new perspective. This information is provided to the user through their device. The user can then select the most suitable successor based on the strengths, weaknesses, and suitability of each candidate displayed on their device.

[0499] As a concrete example, by inputting the following prompt into the generative AI model, you can receive suggestions for potential successors.

[0500] Prompt example:

[0501] "Based on the current leader's thought patterns, please propose a suitable candidate for the next leader. Consider the candidate's past project success rate and team evaluations."

[0502] By using this prompt, the AI ​​can suggest suitable successor candidates, and the user can make a selection based on that information.

[0503] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0504] Step 1:

[0505] The server collects data on candidates' past behavior, statements, and evaluations. It retrieves necessary information from company databases and publicly available sources via APIs as input. Specifically, the server sends API requests and stores the data received as responses in storage. The output is a detailed dataset about the candidates.

[0506] Step 2:

[0507] The server uses the collected data to train an artificial intelligence (AI) model and analyze the candidates' thought patterns. The dataset obtained in Step 1 is used as input. Specifically, the server preprocesses the data using machine learning frameworks such as TensorFlow or PyTorch and applies it to the model. The output is an analysis showing each candidate's thought pattern.

[0508] Step 3:

[0509] The server uses the analysis results to propose successor candidates using AI. The input is the analysis results of the thought patterns obtained in Step 2. Specifically, the server compares the current leader's thought patterns with those of the candidates and calculates the similarity. It also evaluates candidates who may bring new perspectives. The output is a list of proposed successor candidates.

[0510] Step 4:

[0511] The terminal presents the user with information on proposed successor candidates and assists in the selection process. The input is the list of successor candidates obtained in step 3. Specifically, the terminal visually displays each candidate's strengths, weaknesses, and suitability. Based on this information, the user can select the most suitable successor. The output is the successor selected by the user.

[0512] Step 5:

[0513] The server provides the necessary information to ensure the selected successor receives approval. The successor selected by the user in step 4 is used as input. Specifically, the server generates a detailed report detailing the selected successor's experience and skill set, and sends it to the relevant parties. The output is a detailed report to support the approval process.

[0514] (Application Example 1)

[0515] Next, we will describe Application Example 1 of Form 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."

[0516] Selecting successors for robot maintenance personnel in factories often relies on experience and intuition, making it difficult to choose the right person. Furthermore, the inability to effectively utilize on-site work history and evaluations reduces the efficiency of successor selection.

[0517] 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.

[0518] In this invention, the server includes means for analyzing human thought patterns, means for suggesting successor candidates based on the analysis results, and means for suggesting the most suitable successor based on work history and evaluation. This makes it possible to efficiently and appropriately select successors for robot maintenance personnel in factories.

[0519] "Methods for analyzing human thought patterns" refer to techniques for analyzing a person's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[0520] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting and presenting appropriate successor candidates by utilizing the results of an analysis of thought patterns.

[0521] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide the necessary information to choose the most suitable person from among the presented candidates and assist in the selection process.

[0522] "Means of obtaining approval for selected successors" refers to technologies that provide information demonstrating the suitability and abilities of selected successors in order to obtain approval.

[0523] "A method for proposing the most suitable successor based on work history and evaluations" refers to a technology that utilizes a candidate's past work history and evaluation data to select the most suitable successor.

[0524] "Methods for recording and analyzing on-site work" refers to techniques for recording activities at the work site and analyzing that data to identify areas for improvement and streamline operations.

[0525] The system for implementing this invention operates through the collaboration of a server, a terminal, and a user. The server runs a program that uses artificial intelligence to analyze human thought patterns. Specifically, it uses machine learning libraries such as TensorFlow to analyze the candidate's past behavior, statements, and evaluation data, and learns their thought patterns. Based on the analysis results, the server can then propose successor candidates.

[0526] The device functions as smart glasses or other wearable devices to record work performed on-site. It uses a camera and microphone to capture the work in real time and transmits the data to a server. The server analyzes this data, reviewing work history and performance evaluations, and suggests the most suitable successor.

[0527] The user receives information on proposed successor candidates through their device and makes a selection. Based on the information displayed on the device's screen, the user can evaluate each candidate's strengths and weaknesses and choose the most suitable person. To obtain approval for the selected successor, the server provides information indicating their suitability and abilities.

[0528] One concrete example is analyzing how maintenance personnel at a particular factory have solved problems in the past and learning their thought patterns. An example of a prompt for a generative AI model would be, "Based on past maintenance history and evaluation data, please suggest the most suitable candidate for the next maintenance personnel."

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] The terminal records on-site work using a camera and microphone. It acquires video and audio data of the work as input and transmits this data to the server in real time. The output is the work data transmitted to the server.

[0532] Step 2:

[0533] The server analyzes the received work data. It receives video and audio data transmitted from the terminal as input and analyzes the data using a machine learning model. Specifically, it uses TensorFlow to evaluate work efficiency and problem-solving methods. The output consists of work history and evaluation data as analysis results.

[0534] Step 3:

[0535] The server analyzes human thought patterns based on the analysis results. Using work history and evaluation data as input, the artificial intelligence learns the candidate's thought patterns. The output is the analysis result of the thought patterns.

[0536] Step 4:

[0537] The server proposes successor candidates based on the analysis results. It uses the analysis results of thought patterns as input and selects the optimal successor candidate using a generative AI model. The output is a list of proposed successor candidates.

[0538] Step 5:

[0539] The user receives information about proposed successor candidates through their device. The device receives a list of candidates sent from the server as input and displays it on the device's screen. The output is candidate information for the user to review.

[0540] Step 6:

[0541] The user selects the most suitable successor based on candidate information. The input involves evaluating and selecting candidate information displayed on the terminal. The output is information about the selected successor.

[0542] Step 7:

[0543] The server provides the information necessary to obtain approval for the selected successor. It receives information about the selected successor as input and generates data indicating their suitability and abilities. The output is the information required for approval.

[0544] (Example 2)

[0545] Next, we will describe Example 2 of Form 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".

[0546] Traditional successor selection processes have faced challenges in selecting a suitable successor because they fail to adequately analyze the thought patterns of candidates. Furthermore, a lack of information necessary to obtain approval for the selected successor hinders the smooth approval process.

[0547] 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.

[0548] In this invention, the server includes means for collecting and pre-processing information, means for analyzing thought patterns using the pre-processed information, and means for selecting and proposing candidates based on the analysis results. This makes it possible to analyze the thought patterns of candidates in detail and select an appropriate successor. Furthermore, by providing information for obtaining approval from the selected successor, the approval process can be facilitated.

[0549] "Means for collecting and pre-processing information" refers to technologies for collecting data about candidates and processing it to convert it into a format suitable for analysis.

[0550] "Methods for analyzing thought patterns" refer to techniques that analyze candidates' values ​​and decision-making tendencies based on collected data.

[0551] "Methods for selecting and proposing candidates" refers to the techniques for selecting and proposing appropriate successor candidates based on analysis results.

[0552] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and learn patterns.

[0553] "Similarity" is an indicator that shows how closely the thinking patterns of the current leader and the candidate match.

[0554] A "candidate who can offer a new perspective" is someone who possesses different values ​​and approaches from traditional leaders and has the potential to bring a new direction to the organization.

[0555] This invention takes the form of a system in which a server, terminal, and user cooperate to implement a successor selection support system.

[0556] The server first collects information about the candidates. Specifically, it retrieves data such as the candidates' past behavior, statements, and evaluations from company databases and publicly available information. The server preprocesses this data, cleaning text data and normalizing numerical data. This transforms the data into a format suitable for analysis.

[0557] Next, the server inputs the pre-processed data into a generative AI model to analyze the candidates' thought patterns. The generative AI model uses natural language processing techniques to extract values ​​and decision-making tendencies from the text data. The results of this analysis are quantified for each candidate's thought pattern and stored in a database.

[0558] The server selects successor candidates based on the analysis results. Specifically, it compares the current leader's thinking patterns with those of the candidates and lists candidates with high similarity or those who can offer new perspectives. This list of selected candidates is then sent to the terminal.

[0559] The terminal displays a list of candidates received from the server to the user. It provides a dashboard that visually shows each candidate's strengths, weaknesses, and suitability, helping the user select the most suitable successor.

[0560] The user selects the most suitable successor based on information provided through the device. To obtain approval for the selected successor, the user creates a presentation for the approval meeting. Using information on the candidate's achievements and abilities provided through the device, the user explains the suitability of the selected successor at the approval meeting and persuades the approval committee to approve it.

[0561] For example, if a candidate has a track record of successfully completing innovative projects in the past, the server will use that track record to suggest the candidate as a successor who could bring a fresh perspective. An example of a prompt to the generative AI model would be: "Based on the candidate's past behavioral data, analyze their leadership style and suggest successor candidates who are a good match for the current leader or who can offer a fresh perspective."

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] The server collects information about candidates. As input, it retrieves data such as candidates' past behavior, statements, and evaluations from company databases and publicly available information. Specifically, the server collects data from social media posting history and internal evaluation systems via APIs. The output is the collected raw data.

[0565] Step 2:

[0566] The server preprocesses the collected data. It uses the raw data obtained in step 1 as input. Specifically, the server cleans text data and normalizes numerical data, converting it into a format suitable for analysis. The output is the preprocessed data.

[0567] Step 3:

[0568] The server inputs pre-processed data into a generating AI model to analyze the candidates' thought patterns. The pre-processed data obtained in step 2 is used as input. Specifically, the server uses natural language processing techniques to extract values ​​and decision-making tendencies from the text data. The output is numerical data representing each candidate's thought pattern.

[0569] Step 4:

[0570] The server selects successor candidates based on the analysis results. The input is the quantified data of thought patterns obtained in step 3. Specifically, the server compares the current leader's thought patterns with those of the candidates, listing candidates with high similarity or those who can offer new perspectives. The output is a list of selected candidates.

[0571] Step 5:

[0572] The server sends the selected candidate list to the terminal. The candidate list obtained in step 4 is used as input. Specifically, the server uses a communication protocol to transfer the data to the terminal. The candidate list is displayed on the terminal as output.

[0573] Step 6:

[0574] The terminal displays the candidate list received from the server to the user. The candidate list submitted in step 5 is used as input. Specifically, the terminal provides a dashboard that visually displays each candidate's strengths, weaknesses, and suitability. The output is a screen that allows the user to visually review the information.

[0575] Step 7:

[0576] The user selects the most suitable successor based on the information provided through the device. The candidate information displayed in step 6 is used as input. Specifically, the user compares the candidates' evaluation points and makes a decision. The selected successor is obtained as output.

[0577] Step 8:

[0578] The user creates materials to obtain approval for the selected successor. The input uses the information of the successor selected in step 7. Specifically, the user uses information about the candidate's achievements and abilities provided by the terminal to create a presentation for the approval meeting. The output is the presentation materials to be used at the approval meeting.

[0579] (Application Example 2)

[0580] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0581] In selecting managers for machinery and equipment within a factory, there is a need to efficiently identify suitable candidates and ensure a smooth selection process. However, traditional methods make it difficult to adequately evaluate candidates' thought patterns and aptitudes, posing a challenge in selecting appropriate managers.

[0582] 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.

[0583] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for assisting in the selection of proposed successor candidates, means for obtaining approval from the selected successor, means for proposing candidates for managers of machinery and equipment within the factory, means for assisting in the selection of proposed manager candidates, and means for obtaining approval from the selected manager. This makes it possible to efficiently and accurately select managers for machinery and equipment within the factory.

[0584] "Human thought patterns" refer to the unique ways of thinking and decision-making tendencies that each individual possesses.

[0585] "Analysis results" refer to the information and data obtained after artificial intelligence analyzes human thought patterns.

[0586] A "successor candidate" refers to a person who has the potential to take over a specific role or position.

[0587] A "proposed successor candidate" refers to a person who has been selected based on the analysis results and is deemed suitable to be the successor.

[0588] The term "selected successor" refers to the person ultimately chosen from among the proposed candidates.

[0589] "Means of obtaining approval" refers to the methods and processes by which a selected successor obtains the necessary consent or permission to formally assume that role.

[0590] "Candidate for manager of machinery and equipment within the factory" refers to a person who may be responsible for managing the machinery and equipment used within the factory.

[0591] A "proposed manager candidate" is a person selected based on the analysis results as being suitable to manage the machinery and equipment.

[0592] The term "selected administrator" refers to the person ultimately chosen from among the proposed administrator candidates.

[0593] The system for implementing this invention is designed to efficiently select managers for machinery and equipment within a factory. The server uses artificial intelligence to analyze human thought patterns and proposes manager candidates based on past behavioral history and evaluation data. Specifically, it uses Python and leverages machine learning libraries such as TensorFlow and PyTorch to analyze candidate data.

[0594] Smart glasses are used as the terminal, displaying real-time information on suggested administrator candidates and support for selection. This allows users to visually confirm the strengths and weaknesses of candidates and make the best choice.

[0595] As a concrete example, maintenance history and troubleshooting records of robots within a factory are used as data. Based on this, the server inputs a prompt message into the AI ​​model saying, "Please suggest the most suitable robot administrator based on past maintenance history and troubleshooting records," and then suggests the most suitable administrator candidates.

[0596] This system is expected to enable efficient and accurate selection of managers within the factory, and to facilitate a smooth transition of leadership.

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The server collects past behavioral history and evaluation data of management candidates within the factory. This data is retrieved from a database and used as input to analyze the candidates' thought patterns.

[0600] Step 2:

[0601] The server runs an artificial intelligence model using the collected data. Specifically, a machine learning model built using TensorFlow or PyTorch analyzes the candidates' thinking patterns. This analysis outputs the characteristics and tendencies of each candidate.

[0602] Step 3:

[0603] The server generates a list of administrator candidates based on the analysis results. Using the generation AI model, it receives the prompt message, "Please suggest the most suitable robot administrator based on past maintenance history and troubleshooting experience," and proposes the most suitable candidates. This list takes into account the candidates' aptitudes and strengths.

[0604] Step 4:

[0605] The smart glasses, acting as the terminal, receive a list of administrator candidates sent from the server and display it to the user. The user can view detailed information about the candidates and make a selection through the smart glasses.

[0606] Step 5:

[0607] The user selects the most suitable administrator candidate based on the information displayed through their smart glasses. The selected information is sent to the server, and the final approval process begins.

[0608] Step 6:

[0609] The server generates the necessary information to obtain approval for the selected administrator candidate and notifies the relevant parties. This allows the process for the selected administrator to officially assume their role to proceed.

[0610] (Example 3)

[0611] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0612] Traditional successor selection processes have made it difficult to accurately analyze candidates' thought patterns and propose suitable successors. Furthermore, the selection and approval processes for proposed candidates lacked support based on objective information, resulting in decreased efficiency and accuracy.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0614] In this invention, the server includes means for collecting information, means for pre-processing the collected information, and means for analyzing thought patterns using the pre-processed information. This makes it possible to accurately analyze the thought patterns of candidates and propose appropriate successors. It also enables the efficient process of assisting in the selection of proposed candidates and obtaining approval from the selected candidates.

[0615] "Means of collecting information" refers to methods and devices for obtaining data on a candidate's past actions, statements, and evaluations.

[0616] "Means for preprocessing collected information" refers to methods or devices for converting acquired data into a format that is easily processed by AI models.

[0617] "Means for analyzing thought patterns" refers to methods or devices that use pre-processed data to analyze candidates' thought patterns using machine learning algorithms.

[0618] "Means of proposing candidates" refers to methods or devices for selecting and presenting the most suitable successor candidate based on the analysis results.

[0619] "Means of supporting selection" refer to methods or devices that provide information to help select the most suitable person from among the proposed candidates.

[0620] "Means of obtaining approval" refers to methods and devices that provide the necessary information to selected candidates to enable them to obtain approval and support the approval process.

[0621] To implement this invention, a server plays a central role. The server collects data about candidates, preprocesses it, and inputs it into the AI ​​model. Specifically, the server uses a database management system to obtain information such as candidates' past actions, statements, and evaluations. For data preprocessing, NLTK, a Python natural language processing library, is used to tokenize the text data and remove unnecessary words.

[0622] The server inputs pre-processed data into an AI model using machine learning frameworks such as TensorFlow and PyTorch to analyze the candidates' thought patterns. This analysis models the candidates' past behavior and values, and proposes the most suitable successor candidate.

[0623] The user receives candidate suggestions from the server and makes a selection. The server evaluates each candidate's strengths and weaknesses and provides the user with this information in HTML format. This allows the user to choose the best candidate based on objective information.

[0624] Furthermore, the server generates presentation materials to obtain approval from the selected candidates. Specifically, it creates slides in PowerPoint format that demonstrate the suitability of the selected candidates and provides the user with a download link.

[0625] As a concrete example, by inputting the following prompt into the generative AI model, you can obtain suggestions for successor candidates.

[0626] Prompt: "Please suggest a successor candidate whose thinking pattern most closely matches that of the current leader. Please consider the candidate's past project choices and values."

[0627] By using this prompt, the AI ​​can suggest a suitable successor candidate. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0628] Step 1:

[0629] The server collects data about the candidates. As input, it retrieves information about the candidates' past actions, statements, and evaluations from a database. Specifically, the server executes SQL queries to extract the necessary data. The output is the collected raw data.

[0630] Step 2:

[0631] The server preprocesses the collected data. It uses the raw data obtained in step 1 as input. Specifically, the server tokenizes the text data using the Python NLTK library and removes stop words. The output is the preprocessed, clean data.

[0632] Step 3:

[0633] The server analyzes thought patterns using pre-processed data. The clean data obtained in step 2 is input to the AI ​​model. Specifically, the server uses TensorFlow to execute machine learning algorithms and extract the candidates' thought patterns. The output is the analyzed thought pattern data.

[0634] Step 4:

[0635] The server proposes successor candidates based on the analysis results. It uses the thought pattern data obtained in step 3 as input. Specifically, the server performs similarity calculations and selects the most suitable candidate. The output is a list of proposed successor candidates.

[0636] Step 5:

[0637] The user receives information to select the most suitable person from the proposed candidates. The candidate list obtained in step 4 is used as input. Specifically, the server evaluates the strengths and weaknesses of each candidate and provides this information to the user in HTML format. The output is detailed candidate information that the user can view.

[0638] Step 6:

[0639] The server generates materials to obtain approval for the selected successor. It uses information about the candidate selected by the user as input. Specifically, the server creates a presentation in PowerPoint format and provides the user with a download link. The output is materials to support the approval process.

[0640] (Application Example 3)

[0641] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0642] Traditional successor selection processes have made it difficult to accurately analyze candidates' thought patterns and select the appropriate successor. Furthermore, the lack of real-time information during the selection process has resulted in reduced efficiency and accuracy.

[0643] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0644] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, and means for presenting the analysis results using a visual display device. This enables accurate analysis of candidates' thought patterns and efficient and accurate successor selection through real-time information provision.

[0645] "Methods for analyzing human thought patterns" refer to technologies that use artificial intelligence to analyze a candidate's thinking tendencies based on data such as their past actions, statements, and evaluations.

[0646] "A method for proposing successor candidates based on analysis results" is a technique that, based on analyzed thought patterns, presents individuals who have a similar way of thinking to the current leader or who can offer new perspectives as potential successors.

[0647] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide information on each candidate's strengths, weaknesses, and suitability in order to select the most suitable person from among the presented candidates.

[0648] "Means of obtaining approval for selected successors" refers to technologies that provide information demonstrating the suitability and abilities of a selected successor, thereby supporting the approval process and enabling them to gain approval.

[0649] "Means of presenting analysis results using a visual display device" refers to a technology that uses visual devices such as smart glasses to display the analyzed information to the user in real time.

[0650] "Methods for collecting candidates' past behavioral data and analyzing it with artificial intelligence models" refers to technologies that collect candidates' past behavioral history and evaluation data, input this data into an artificial intelligence model, and analyze it.

[0651] "Means of displaying analysis results in real time" refers to technology that instantly displays the analyzed information on a visual display device, allowing users to check that information in real time.

[0652] The system for implementing this invention operates through the coordinated efforts of a server, a terminal, and a user. The server collects past behavioral data of candidates and analyzes this data using an artificial intelligence model. Specifically, it uses an AI model built with Python (e.g., TensorFlow) to analyze the candidates' thought patterns. The analyzed data is presented to the user in real time through a visual display device, such as smart glasses (e.g., Google Glass).

[0653] The terminal receives analysis results sent from the server and displays them on a visual display device. The user wears smart glasses and selects a successor based on the presented information. This allows the user to check the candidate's thought patterns in real time and select the appropriate successor.

[0654] As a concrete example, during leadership training within a factory, the server uses AI to analyze the candidate's past project management skills and teamwork evaluations, and displays a message on smart glasses via a terminal saying, "Candidate A has strong project management skills and excellent teamwork abilities."

[0655] An example of a prompt to input into the generating AI model is: "Based on candidate A's past project management data, analyze their thinking patterns and evaluate their leadership aptitude."

[0656] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0657] Step 1:

[0658] The server collects past behavioral data of candidates. It uses candidate behavioral history and evaluation data obtained from the factory's management system as input. This data is stored in a database in preparation for subsequent analysis.

[0659] Step 2:

[0660] The server inputs the collected data into an artificial intelligence model to analyze the candidates' thinking patterns. The behavioral data collected in Step 1 is used as input. Using an AI model (e.g., TensorFlow), the candidate's thinking tendencies and leadership aptitude are analyzed, and the analysis results are generated. The output is the analyzed thinking pattern data.

[0661] Step 3:

[0662] The server sends the analysis results to the terminal. The thought pattern data obtained in step 2 is used as input. The terminal prepares to display the received data on a visual display device. The output generates data in a displayable format.

[0663] Step 4:

[0664] The terminal presents the analysis results to the user through a visual display device. The displayable data prepared in step 3 is used as input. Information such as "Candidate A has strong project management skills and excellent teamwork abilities" is displayed in real time on the smart glasses. The output provides information that the user can visually confirm.

[0665] Step 5:

[0666] The user selects a successor based on the information presented. The input is the analysis results displayed on smart glasses. The user evaluates the suitability and abilities of the candidates and selects the most suitable successor. The output is information about the selected successor.

[0667] 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.

[0668] "Example of form 1"

[0669] One embodiment of the present invention provides a successor selection support system that incorporates an emotion engine. This system uses an emotion engine that recognizes the user's emotions as a means of analyzing human thought patterns. The emotion engine recognizes emotions from the user's facial expressions, tone of voice, and choice of words, and uses the results to analyze thought patterns. For example, if a user shows joy or excitement, it is determined that the user is likely to have a positive thought pattern. Conversely, if a user shows anger or dissatisfaction, it is determined that the user is likely to have a negative thought pattern.

[0670] "Example of form 2"

[0671] Furthermore, the results of the emotion recognition process by the emotion engine will also be taken into consideration when proposing successor candidates. Specifically, the emotional state of the candidates will be analyzed, and successor candidates will be proposed based on the results. For example, if a candidate can consistently maintain a calm and composed emotional state, it will be judged that the candidate is likely to be able to make calm judgments even under pressure, and will be proposed as a successor.

[0672] "Example of form 3"

[0673] Furthermore, as a means of obtaining approval from the selected successor, the system predicts the approver's reaction based on the emotion recognition results from the emotion engine. Specifically, it analyzes the approver's emotional state and predicts the likelihood that the approver will approve the successor based on the results. For example, if the approver shows joy or excitement, it is predicted that the approver is likely to approve the successor.

[0674] The following describes the processing flow for each example of the form.

[0675] "Example of form 1"

[0676] Step 1: Activate the emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and word choice.

[0677] Step 2: The emotion engine recognizes the user's emotions and uses the results to analyze their thought patterns.

[0678] Step 3: If a user shows joy or excitement, it is likely that they have a positive thought pattern.

[0679] Step 4: If a user expresses anger or dissatisfaction, it is likely that the user has a negative thought pattern.

[0680] "Example of form 2"

[0681] Step 1: Analyze the candidate's emotional state using the emotion engine.

[0682] Step 2: Propose successor candidates based on the analysis results.

[0683] Step 3: If a candidate can consistently maintain a calm and composed emotional state, they are likely to be able to make calm decisions even under pressure, and should be proposed as a successor.

[0684] "Example of form 3"

[0685] Step 1: Analyze the approver's emotional state using the emotion engine.

[0686] Step 2: Based on the analysis results, predict the likelihood that the approver will approve the successor.

[0687] Step 3: If the approver shows joy or excitement, predict that they are likely to approve the successor.

[0688] (Example 1)

[0689] Next, we will describe Example 1 of Form 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".

[0690] Traditional successor selection processes often fail to adequately consider candidates' thought patterns and emotions, making it difficult to select a suitable successor. Furthermore, these processes often lack objective data-based evaluations and rely heavily on subjective judgments. Therefore, improving the accuracy of successor selection is crucial.

[0691] 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.

[0692] In this invention, the server includes means for collecting human behavioral data, means for analyzing thought patterns using the collected data, and means for analyzing the user's emotions using emotion recognition technology and reflecting them in the thought patterns. This makes it possible to comprehensively evaluate the thought patterns and emotions of candidates and objectively select an appropriate successor.

[0693] "Human behavioral data" refers to information including an individual's past actions, statements, and evaluations, and is used to analyze thought patterns.

[0694] "Thinking patterns" are characteristics that indicate an individual's values ​​and judgment criteria, and are analyzed from their past actions and statements.

[0695] "Emotion recognition technology" is a technology that analyzes emotions from a user's facial expressions, tone of voice, and word choice, and uses the results to analyze their thought patterns.

[0696] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate results that meet specific objectives.

[0697] A "potential successor" refers to a person who has the potential to take on the next leadership role within an organization or group.

[0698] "Aptitude" refers to characteristics that indicate an individual's ability or suitability for a particular role or job.

[0699] This invention provides a concrete method for realizing a successor selection support system. First, the server collects human behavior data. This data is obtained from corporate databases and publicly available information and extracted using SQL queries. Next, the server analyzes thought patterns using the collected data. For the analysis, machine learning frameworks such as TensorFlow and PyTorch are used to construct a neural network model. This allows the system to analyze the candidate's past behavior and values ​​and learn their thought patterns.

[0700] Furthermore, the server analyzes the user's emotions using emotion recognition technology. The emotion engine recognizes emotions from the user's facial expressions, tone of voice, and word choice, and uses the results to analyze thought patterns. For example, if a user shows joy or excitement, the system determines that the user is likely to have positive thought patterns.

[0701] The device provides users with information on proposed successor candidates. Specifically, it displays a dashboard that visualizes each candidate's strengths, weaknesses, and suitability, helping users select the most suitable successor.

[0702] Using a generative AI model, the server evaluates the suitability of candidates. An example of a prompt is: "Based on Candidate A's past behavioral data, analyze their thinking patterns suitable for leadership and evaluate their suitability as a successor candidate." In response to this prompt, the generative AI model analyzes Candidate A's data and evaluates their suitability as a successor.

[0703] In this way, servers, terminals, and users can cooperate to efficiently carry out the successor selection process.

[0704] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0705] Step 1:

[0706] The server collects candidate behavior data from the company's database. Inputs include candidate IDs and queries to the relevant database. The server executes SQL queries to extract data such as the candidate's past behavior, statements, and evaluations. The output is a dataset formatted for analysis. Specifically, a Python script is used to connect to the database and retrieve the necessary data.

[0707] Step 2:

[0708] The server analyzes thought patterns using the collected data. The input is the dataset obtained in Step 1. The server builds a neural network model using TensorFlow and analyzes the data. This allows it to learn the candidates' thought patterns and generate analysis results. The output is a feature vector representing each candidate's thought pattern. Specifically, the data is preprocessed, input into the model, and then analyzed.

[0709] Step 3:

[0710] The server analyzes the user's emotions using emotion recognition technology. The input consists of emotional data such as the user's facial expressions, tone of voice, and word choice. The server uses an emotion engine to analyze this data and determine the user's emotional state. The output is an indicator of the user's emotions. Specifically, it analyzes data acquired from cameras and microphones in real time.

[0711] Step 4:

[0712] The server proposes successor candidates based on the analysis results. The input is the thought patterns and emotional indicators obtained in steps 2 and 3. The server uses a generative AI model to evaluate the suitability of the candidates and lists the most suitable successor candidates. The output is a list of successor candidates. Specifically, the AI ​​model is run using the prompt message "Based on the candidates' thought patterns, please propose individuals suitable for leadership."

[0713] Step 5:

[0714] The terminal provides the user with information on the proposed successor candidates. The input is the list of successor candidates obtained in step 4. The terminal displays a dashboard that visualizes each candidate's strengths, weaknesses, and suitability. The output is an interface for candidate information that the user can view. Specifically, a web application is used to generate the dashboard and present it to the user.

[0715] Step 6:

[0716] The user selects the most suitable successor based on the information provided from the terminal. The input is the candidate information displayed in step 5. The user compares the information of each candidate and selects the most suitable person. The output is the ID of the selected successor. Specifically, the user selects a candidate on the interface and sends the selection result to the server.

[0717] Step 7:

[0718] The server assists in the process of obtaining approval for the selected successor. The input is the ID of the successor selected in step 6. The server generates a report showing the suitability and abilities of the selected successor and distributes it to the relevant parties. The output is a report to support the approval process. Specifically, the report is generated in PDF format and sent to the relevant parties via email.

[0719] (Application Example 1)

[0720] Next, we will describe Application Example 1 of Form 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."

[0721] Selecting robot managers within factories presents a challenge: traditional methods fail to adequately consider candidates' thought patterns and emotions, making it difficult to choose the most suitable manager. In particular, a lack of analysis based on candidates' emotions and past behavior makes effective leadership transfer difficult.

[0722] 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.

[0723] In this invention, the server includes means for analyzing human thought patterns, means for recognizing emotions, and means for utilizing the recognized emotions in the analysis of thought patterns. This makes it possible to propose candidates for managers within a factory and select the most suitable manager.

[0724] "Methods for analyzing human thought patterns" refer to techniques for analyzing a candidate's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[0725] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting the most suitable successor candidate by using the results of analyzing thought patterns.

[0726] "Means to support the selection of proposed successor candidates" refers to technologies that provide the information necessary to choose the most suitable person from among the proposed candidates.

[0727] "Means of obtaining approval for a selected successor" refers to technologies that provide information demonstrating the suitability and abilities of a selected successor in order to obtain approval.

[0728] "Means of recognizing emotions" refers to technologies that analyze emotions from a user's facial expressions, tone of voice, word choice, and other factors.

[0729] "Methods for utilizing recognized emotions in the analysis of thought patterns" refers to techniques that use the results of emotion analysis to perform a more precise analysis of thought patterns.

[0730] "A method for proposing candidates for factory managers" refers to a technology that uses the results of thought pattern and emotion analysis to select candidates for robot managers within a factory.

[0731] To implement this invention, a system is built in which a server plays a central role. The server runs a program that integrates an artificial intelligence model and an emotion engine. Specifically, it uses Python combined with TensorFlow and OpenCV to collect and analyze the candidate's past behavior logs, voice data, and facial expression data. This makes it possible to analyze the candidate's thought patterns and emotions in detail.

[0732] The server first retrieves the candidate's past actions and statements from a database and analyzes their thought patterns using an artificial intelligence model. Next, it uses an emotion engine to recognize emotions from voice and facial expression data and utilizes the results in the analysis of thought patterns. This provides the basic data needed to propose candidates for management positions within the factory.

[0733] As a concrete example, the server evaluates candidate A's suitability as the optimal robot administrator based on their past behavioral logs and emotional data. An example of a prompt to be input to the generating AI model in this case would be, "Please evaluate candidate A's suitability as the optimal robot administrator based on their past behavioral logs and emotional data."

[0734] This system allows users to obtain information to select the most suitable robot manager within the factory, enabling them to build a more efficient management system.

[0735] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0736] Step 1:

[0737] The server retrieves past behavior logs, voice data, and facial expression data of candidates from the database. The input is the candidate's ID, and the output is the corresponding dataset. This collects information about the candidate's past behavior and statements.

[0738] Step 2:

[0739] The server inputs the acquired behavioral logs into an artificial intelligence model and analyzes the thought patterns. The input is behavioral log data, and the output is the result of the thought pattern analysis. As part of the data processing, the behavioral logs are converted into features and then analyzed by the AI ​​model.

[0740] Step 3:

[0741] The server inputs voice data and facial expression data into the emotion engine to recognize emotions. The input is voice data and facial expression data, and the output is the result of emotion recognition. It analyzes changes in voice tone and facial expression to identify emotions.

[0742] Step 4:

[0743] The server integrates the results of thought pattern analysis and emotion recognition to evaluate the suitability of management candidates. The input is data on thought patterns and emotions, and the output is the result of the suitability evaluation. The data calculation involves comparing the two and quantifying the suitability.

[0744] Step 5:

[0745] The server uses a generative AI model to generate prompt messages and suggest the most suitable administrator candidates. The input is the result of the aptitude assessment, and the output is the prompt message and candidate suggestions. It performs the operation of generating the prompt message, "Please evaluate candidate A's suitability as the most suitable robot administrator based on their past behavior logs and sentiment data."

[0746] Step 6:

[0747] The user selects the most suitable administrator candidate based on information provided by the server. The input is the server's suggestions, and the output is the selected administrator candidate. The user reviews the provided data and determines the best candidate.

[0748] (Example 2)

[0749] Next, we will describe Example 2 of Form 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".

[0750] Traditional succession selection processes have faced challenges in selecting appropriate successors because they make it difficult to adequately consider an individual's decision-making patterns and emotional states. Furthermore, there was a problem in evaluating the degree of alignment with the current leader's thinking patterns and the potential to offer new perspectives when selecting successor candidates.

[0751] 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.

[0752] In this invention, the server includes means for analyzing an individual's decision-making patterns, means for presenting successor candidates based on the analysis results, and means for recognizing emotional states and presenting successor candidates based on those results. This makes it possible to select a successor that takes into account an individual's decision-making patterns and emotional states, and to efficiently select an appropriate successor.

[0753] An "information processing device" is a computer system used to collect, analyze, and process data.

[0754] An "individual decision-making pattern" refers to the tendencies of choices and judgments that a particular individual has made in the past.

[0755] "Analysis" is the process of examining data in detail and understanding its structure and meaning.

[0756] A "potential successor" is someone who has the potential to take over the role of the current leader.

[0757] "Presentation" refers to the act of showing specific information or options to others.

[0758] "Emotional state" refers to the emotional state an individual is experiencing at a particular moment.

[0759] "Machine learning technology" is a technique that allows computers to learn patterns from data and make predictions and decisions.

[0760] A "similar individual" is a person who shares common characteristics with other individuals based on specific criteria.

[0761] A "new perspective" refers to a new viewpoint or approach that differs from conventional ways of thinking or methods.

[0762] This invention provides a specific model for implementing a successor selection support system. The server collects data from a database, including candidates' past actions, statements, and evaluations, in order to analyze their individual decision-making patterns. This is achieved by using SQL queries to extract information from the database.

[0763] The server uses the collected data to build a machine learning model using the Python TensorFlow library, and learns and analyzes the candidates' decision-making patterns. This model uses a neural network to analyze the candidates' thinking patterns from their past actions and statements.

[0764] Furthermore, the server uses an emotion engine to recognize the emotional state of the candidates and presents potential successors based on the results. Emotion recognition uses algorithms to analyze emotional states.

[0765] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "Based on Candidate A's past leadership evaluation data, analyze their thinking patterns and evaluate their suitability as a successor." By using this prompt sentence, the AI ​​model can analyze Candidate A's data and evaluate their suitability as a successor.

[0766] In this way, the server can select a successor by taking into account an individual's decision-making patterns and emotional state, enabling it to efficiently select an appropriate successor.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0768] Step 1:

[0769] The server collects data from the database, including the candidate's past actions, statements, and evaluations. Input requires the candidate's ID and related queries. The server uses SQL queries to access the database and extract the necessary information. Output includes the candidate's past behavioral history and evaluation data.

[0770] Step 2:

[0771] The server uses the TensorFlow library in Python to build a machine learning model based on the collected data. The input requires candidate data obtained in Step 1. The server uses this data to train a neural network and learn the candidates' decision-making patterns. The output is a model representing the candidates' thinking patterns.

[0772] Step 3:

[0773] The server uses a machine learning model to analyze the candidates' thought patterns. The input requires the model built in Step 2 and the candidates' data. The server applies the model to analyze the candidates' decision-making tendencies and values. The output provides the analysis results, which are then used to suggest potential successors.

[0774] Step 4:

[0775] The server uses an emotion engine to recognize the candidate's emotional state. Input requires the candidate's voice data and facial expression data. The server applies an emotion recognition algorithm to analyze the candidate's emotional state. The output provides information about the candidate's emotional state.

[0776] Step 5:

[0777] The server presents potential successors based on the analysis results and emotional state. The input requires the data obtained in steps 3 and 4. The server integrates this information and presents individuals with similar thought patterns to the current leader, as well as individuals who may offer new perspectives. The output is a list of potential successors.

[0778] Step 6:

[0779] The server provides information to select the most suitable person from the presented successor candidates. The input is the candidate list obtained in step 5. The server evaluates each candidate's strengths, weaknesses, and suitability, and generates information to support the selection process. The output is selection support information.

[0780] Step 7:

[0781] The server assists in obtaining approval for the selected successor. The input requires information on the candidate selected in step 6. The server generates a report outlining the suitability and capabilities of the selected successor, supporting the approval process. The output is an approval support report.

[0782] (Application Example 2)

[0783] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0784] In selecting successors, there is a need for a more objective and efficient selection process by appropriately analyzing candidates' thought patterns and emotional states and presenting the information visually. However, traditional methods make it difficult to comprehensively consider these factors, and the selection process tends to be subjective.

[0785] 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.

[0786] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, means for visually presenting candidate information using a visual display device, and means for analyzing emotional states. This enables a more objective and efficient successor selection by comprehensively analyzing the candidates' thought patterns and emotional states and visually presenting the information.

[0787] "Methods for analyzing human thought patterns" refer to techniques that analyze a person's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[0788] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting appropriate successor candidates using the results of analyzing thought patterns.

[0789] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide the information necessary to select the most suitable person from among the presented candidates.

[0790] "Means of obtaining approval for selected successors" refers to technologies that support the information and procedures necessary for a selected successor to be formally approved.

[0791] "Means of visually presenting candidate information using a visual display device" refers to a technology that displays information about a candidate through a visual device such as a display, making it easy for users to understand.

[0792] "Methods for analyzing emotional states" refer to techniques that analyze the emotional state of candidates and utilize the results in selecting a successor.

[0793] The system for implementing this invention operates through the coordinated efforts of a server, a terminal, and a user. The server uses artificial intelligence to analyze human thought patterns. Specifically, it collects data on candidates' past actions, statements, and evaluations, and analyzes this data using a generative AI model. Based on the analysis results, it generates data to propose successor candidates.

[0794] The terminal acts as a visual display device, visually presenting analysis results transmitted from the server to the user. For example, it may use smart glasses or a tablet device to display information such as the candidate's strengths, weaknesses, and aptitudes. Furthermore, it may use an emotion engine to analyze the candidate's emotional state in real time and visually present the results.

[0795] The user selects successor candidates based on information presented through their device. The server then assists in obtaining approval for the selected candidates. Specifically, it provides information indicating the suitability and abilities of the selected candidates, facilitating a smooth approval process.

[0796] As a concrete example, when selecting a robot manager in a factory, the current manager, wearing smart glasses, reviews the candidates' past project decisions and evaluations, and then selects a candidate capable of making calm judgments based on the analysis results from the emotion engine. An example of a prompt to the generated AI model would be: "Analyze candidate A's past project decisions and evaluate their thought patterns. Also, based on the analysis results from the emotion engine, evaluate their ability to make decisions under pressure."

[0797] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0798] Step 1:

[0799] The server collects data on candidates' past behavior, statements, and evaluations. As input, it retrieves databases and historical information related to the candidates and prepares this as a dataset for analysis. As output, it generates a dataset formatted for analysis.

[0800] Step 2:

[0801] The server uses a generative AI model to analyze candidates' thought patterns from the collected dataset. The dataset formatted in Step 1 is used as input, and prompt sentences are fed into the AI ​​model. As data processing, the AI ​​model analyzes the data and extracts candidates' thought patterns. The output is the analysis results of the thought patterns.

[0802] Step 3:

[0803] The server proposes successor candidates based on the analysis results. It uses the analysis results of the thought patterns obtained in step 2 as input. As data calculation, it applies an algorithm to evaluate the analysis results and select the optimal successor candidate. As output, it generates a list of proposed successor candidates.

[0804] Step 4:

[0805] The terminal uses a visual display device to visually present information about proposed successor candidates to the user. It receives a list of successor candidates and their detailed information from a server as input. Specifically, it displays the candidates' strengths, weaknesses, and suitability on the display of smart glasses or a tablet device. As output, it provides information that the user can visually confirm.

[0806] Step 5:

[0807] The device uses an emotion engine to analyze the candidate's emotional state in real time and presents the results to the user. The input is real-time emotional data of the candidate. The data processing involves the emotion engine analyzing the data and evaluating the emotional state. The output is a visual display of the emotional state analysis results.

[0808] Step 6:

[0809] The user selects a successor candidate based on information presented through the terminal. The input consists of candidate information and an analysis of their emotional state, both provided by the terminal. The user makes a decision based on the presented information and selects the most suitable successor. The output is the transmission of the selected successor's information to the server.

[0810] Step 7:

[0811] The server assists in obtaining approval for the selected successor. It receives selection results submitted by users as input. As a data processing tool, it organizes information indicating the suitability and abilities of the selected successor and creates materials to support the approval process. As output, it provides the necessary information for approval to the relevant parties.

[0812] (Example 3)

[0813] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0814] Traditional successor selection processes have challenges in accurately evaluating candidates' thought patterns and aptitudes, and they lack an approval process that takes into account the emotional state of approvers, making it difficult to select a suitable successor.

[0815] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0816] In this invention, the server includes means for collecting and pre-processing information, means for analyzing thought patterns using artificial intelligence, and means for analyzing the emotional state of approvers using an emotion engine and obtaining approval. This makes it possible to accurately analyze the thought patterns of candidates, propose appropriate successors, and realize an approval process that takes into account the emotional state of approvers.

[0817] "Means of collecting and pre-processing information" refers to the process of collecting data about candidates and preparing it in a format suitable for analysis.

[0818] "Methods for analyzing thought patterns using artificial intelligence" refers to the process of using machine learning techniques to extract thinking tendencies and values ​​from a candidate's past actions and statements.

[0819] "Methods for proposing successor candidates" refers to the process of selecting and presenting appropriate successor candidates based on the analysis results.

[0820] "Means of supporting the selection of proposed successor candidates" refers to a process of evaluating the strengths, weaknesses, and aptitudes of candidates and providing information to select the most suitable successor.

[0821] "A means of obtaining approval by analyzing the emotional state of approvers using an emotion engine" refers to a process that analyzes the emotions of approvers, predicts the likelihood of approval based on the results, and supports the approval process.

[0822] A description of embodiments for carrying out this invention will be given.

[0823] The server first collects and preprocesses data about candidates. This data collection utilizes company databases and publicly available information sources. Preprocessing involves cleaning text data and normalizing numerical data to prepare the data for analysis. This process uses database management systems (DBMS) and data processing software.

[0824] Next, the server uses artificial intelligence to analyze thought patterns. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to extract thinking tendencies and values ​​from the candidate's past actions and statements. This makes it possible to clearly identify the candidate's characteristics.

[0825] The server then proposes successor candidates based on the analysis results. The artificial intelligence selects appropriate candidates by considering similarities to the current leader's thought patterns and new perspectives. The list of proposed candidates is sent to the terminal.

[0826] The device displays detailed information about the proposed successor candidates to the user. Based on the information provided through the device, the user selects the most suitable successor. To assist in the selection process, each candidate's strengths, weaknesses, and suitability are evaluated and displayed.

[0827] Finally, the server uses an emotion engine to analyze the approver's emotional state and obtain approval. Emotion recognition software is used to analyze the approver's emotions and predict the likelihood of approval based on the results. This ensures a smooth approval process.

[0828] As a concrete example, when a user inputs the prompt "Analyze candidate A's past leadership style and values, and evaluate their suitability as a successor by comparing them to the current leader" into the generating AI model, the server analyzes candidate A's data, evaluates their suitability, and displays the results on the terminal. The specific processing flow in Example 3 is explained using Figure 21.

[0829] Step 1:

[0830] The server collects data about candidates. It uses data from company databases and publicly available sources as input. Specifically, it accesses the database via an API to retrieve data on candidates' past behavior, statements, and evaluations. The output is the collected raw data.

[0831] Step 2:

[0832] The server preprocesses the collected data. The input is the raw data collected in step 1. Specifically, it cleans text data (removes unnecessary characters and noise) and normalizes numerical data. This prepares the data for analysis. The output is the preprocessed, clean data.

[0833] Step 3:

[0834] The server analyzes thought patterns using artificial intelligence. The input is the data preprocessed in step 2. Specifically, it trains machine learning models using TensorFlow or PyTorch to extract the thought patterns of the candidates. The output is the analysis results showing the thought patterns of each candidate.

[0835] Step 4:

[0836] The server proposes successor candidates based on the analysis results. The input is the analysis results obtained in step 3. Specifically, it runs an algorithm that selects appropriate candidates by considering similarities to the current leader's thinking patterns and new perspectives. The output is a list of proposed successor candidates.

[0837] Step 5:

[0838] The terminal displays detailed information about the proposed successor candidates to the user. The input is the candidate list generated in step 4. Specifically, it evaluates each candidate's strengths, weaknesses, and suitability, and displays the information in a user-friendly format. The output is the candidate information that the user can view.

[0839] Step 6:

[0840] The server uses an emotion engine to analyze the approver's emotional state and obtain approval. The input is the successor information selected by the user. Specifically, it uses emotion recognition software to analyze the approver's emotions and predict the likelihood of approval. The output is the predicted result regarding the likelihood of approval.

[0841] (Application Example 3)

[0842] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0843] In factory settings, selecting successors for machinery and equipment managers often relies on subjective judgment, making it difficult to select suitable personnel. Furthermore, failing to consider the emotional state of the approvers during the selection process can hinder its smooth execution.

[0844] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0845] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, and means for analyzing the emotional state of approvers and predicting the likelihood of approval. This enables objective and efficient selection of successors for managers of machinery and equipment in factories.

[0846] "Human thought patterns" refer to the unique tendencies and processes by which individual people act and make decisions based on their past experiences and values.

[0847] "Analysis results" refer to the data and information obtained after artificial intelligence analyzes human thought patterns.

[0848] A "successor candidate" refers to a person who has the potential to take over a specific role or position.

[0849] "Proposed successor candidates" refers to individuals selected by artificial intelligence and to be evaluated in the next stage.

[0850] An "approver" refers to a person or organization that has the authority to ultimately certify a successor candidate.

[0851] "Emotional state" refers to the psychological reactions and feelings that an approver exhibits in response to a particular situation or information.

[0852] A "machinery manager" refers to the person responsible for the operation and maintenance of machinery and equipment within a factory.

[0853] A "successor selection support system" refers to a system that uses artificial intelligence to support the process from selecting a successor candidate to approving them.

[0854] A server plays a central role in implementing this invention. The server runs a program that uses artificial intelligence to analyze human thought patterns. Specifically, it uses Python and the machine learning library scikit-learn to analyze candidates' past behavior and evaluation data. This makes it possible to cluster candidates' thought patterns and propose successor candidates.

[0855] Furthermore, the server uses Hugging Face's Transformers to analyze the approver's emotional state. This allows it to predict the likelihood that the approver will approve the successor candidate. The results of the emotional analysis provide crucial information for facilitating a smooth approval process.

[0856] The device functions as a smartphone or tablet, receiving information provided by the server. Through the device, the user can review the proposed successor candidates and evaluate the likelihood of approval based on the approver's emotional state.

[0857] As a concrete example, based on candidate A's past work history and evaluation data, the AI ​​proposes candidate A as a successor candidate. Furthermore, an analysis of the approver's emotional state predicts that the approver has favorable feelings towards candidate A, thus indicating a high probability of approval.

[0858] An example of a prompt would be: "Based on Candidate A's past work history and evaluation data, analyze their suitability as a successor candidate. Also, analyze the approver's emotional state and predict the likelihood of approval."

[0859] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0860] Step 1:

[0861] The server collects data on candidates' past behavior and evaluations. The input consists of the candidate's work history and evaluation information. This data is preprocessed, and features are extracted. Specifically, this involves data cleaning and normalization.

[0862] Step 2:

[0863] The server uses scikit-learn to cluster the candidates' thinking patterns based on preprocessed data. The input is the features extracted in step 1. As a data operation, a clustering algorithm is applied to classify the candidates' thinking patterns. The output is the cluster information for each candidate.

[0864] Step 3:

[0865] The server proposes successor candidates based on the clustering results. The input is the cluster information from step 2. As a data processing step, the candidates within the cluster are evaluated, and the most suitable successor candidate is selected. The output is a list of proposed successor candidates.

[0866] Step 4:

[0867] The server uses Hugging Face's Transformers to analyze the approver's emotional state. The input consists of the approver's past statements and behavioral data. The data is then processed by applying an emotion analysis model to evaluate the approver's emotional state. The output is the evaluation result of the approver's emotional state.

[0868] Step 5:

[0869] The server predicts the likelihood that approvers will approve successor candidates based on the sentiment analysis results. The input is the result of the emotional state evaluation in step 4. As a data processing step, the relationship between emotional state and approval is evaluated, and the likelihood of approval is calculated. The output is the predicted likelihood of approval.

[0870] Step 6:

[0871] The terminal presents the user with a list of potential successors provided by the server and a prediction of the likelihood of approval. The input is the output from steps 3 and 5. Specifically, the information is displayed through the user interface for the user to review.

[0872] (Other examples)

[0873] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0874] 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.

[0875] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and 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.

[0876] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0877] 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.

[0878] [Third Embodiment]

[0879] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0880] 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.

[0881] 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).

[0882] 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.

[0883] 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.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0891] "Example of form 1"

[0892] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0893] "Example of form 2"

[0894] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0895] "Example of form 3"

[0896] The successor selection support system of the present invention includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, and means for obtaining approval for the selected successor. Artificial intelligence is used as the means for analyzing human thought patterns. The artificial intelligence learns and analyzes thought patterns from the candidate's past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold. Furthermore, as a means for proposing successor candidates based on the analysis results, the artificial intelligence proposes successor candidates by searching for individuals with similar thinking patterns or entirely new patterns based on the analysis results. Specifically, it proposes candidates considering how well their thought patterns match those of the current leader or whether they have the potential to bring a new perspective. As a means for supporting the selection of proposed successor candidates, information is provided to select the most suitable person from among the proposed candidates. Specifically, the strengths, weaknesses, and aptitudes of each candidate are evaluated, and the selection is supported based on these evaluations. Finally, as a means for obtaining approval for the selected successor, support is provided to ensure that the selected successor receives approval. Specifically, this involves providing information that demonstrates the suitability and abilities of the selected successor, and ensuring that the person receives approval.

[0897] The following describes the processing flow for each example of the form.

[0898] "Example of form 1"

[0899] Step 1: The artificial intelligence learns and analyzes the candidate's thought patterns from their past actions, statements, and evaluations. Specifically, it analyzes what decisions the candidate has made in the past and what values ​​they hold.

[0900] Step 2: Based on the analysis results, the artificial intelligence proposes potential successors by exploring individuals with similar thinking patterns or entirely new ones. Specifically, it considers factors such as how closely the candidate's thinking pattern matches that of the current leader, or whether they have the potential to bring a new perspective.

[0901] Step 3: Provide information to help select the most suitable person from the proposed candidates. Specifically, evaluate each candidate's strengths, weaknesses, and suitability, and use this information to support the selection process.

[0902] Step 4: Support the selected successor in gaining approval. Specifically, provide information demonstrating the suitability and abilities of the selected successor to help them gain approval.

[0903] (Example 1)

[0904] Next, we will describe Embodiment 1 of 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."

[0905] Traditional succession selection processes often relied on subjective judgments, making it difficult to accurately assess candidates' thought patterns and aptitudes. As a result, there was a risk of overlooking suitable candidates when selecting successors to lead the organization into the future.

[0906] 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.

[0907] In this invention, the server includes means for collecting data, means for analyzing thought patterns using the collected data, and means for proposing successor candidates based on the analysis results. This makes it possible to objectively and efficiently select successor candidates and find suitable personnel to lead the organization into the future.

[0908] "Means of collecting data" refers to devices or methods for obtaining information such as a candidate's past actions, statements, and evaluations.

[0909] "Means for analyzing thought patterns" refers to a device or method for evaluating a candidate's decision-making patterns and values ​​using collected data.

[0910] "Means of proposing successor candidates" refers to a device or method for selecting and presenting individuals who have the potential to lead the organization in the future, based on the results of an analysis.

[0911] A "machine learning framework" is a software library used to train models with data and perform pattern recognition and prediction.

[0912] "Calculating similarity" is the process of comparing the thinking patterns of current leaders and candidates and quantifying the degree of similarity.

[0913] "Evaluating the potential to bring a new perspective" is the process of determining whether a candidate has the ability to bring innovation and transformation to the organization.

[0914] The embodiment of this invention provides a specific method for constructing a successor selection support system. The server first collects data on candidates' past behavior, statements, and evaluations. This data is obtained via APIs from corporate databases and publicly available information sources. Next, the server uses the collected data to train an artificial intelligence (AI) model and analyze the candidates' thought patterns. Machine learning frameworks such as TensorFlow and PyTorch are used in this process. The AI ​​models the candidates' decision-making patterns and values, and evaluates their past project success rates and leadership style.

[0915] Subsequently, the server uses the analysis results to have AI suggest potential successors. The AI ​​compares the current leader's thought patterns with those of the candidates and calculates the degree of similarity. It also evaluates candidates who may bring a new perspective. This information is provided to the user through their device. The user can then select the most suitable successor based on the strengths, weaknesses, and suitability of each candidate displayed on their device.

[0916] As a concrete example, by inputting the following prompt into the generative AI model, you can receive suggestions for potential successors.

[0917] Prompt example:

[0918] "Based on the current leader's thought patterns, please propose a suitable candidate for the next leader. Consider the candidate's past project success rate and team evaluations."

[0919] By using this prompt, the AI ​​can suggest suitable successor candidates, and the user can make a selection based on that information.

[0920] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0921] Step 1:

[0922] The server collects data on candidates' past behavior, statements, and evaluations. It retrieves necessary information from company databases and publicly available sources via APIs as input. Specifically, the server sends API requests and stores the data received as responses in storage. The output is a detailed dataset about the candidates.

[0923] Step 2:

[0924] The server uses the collected data to train an artificial intelligence (AI) model and analyze the candidates' thought patterns. The dataset obtained in Step 1 is used as input. Specifically, the server preprocesses the data using machine learning frameworks such as TensorFlow or PyTorch and applies it to the model. The output is an analysis showing each candidate's thought pattern.

[0925] Step 3:

[0926] The server uses the analysis results to propose successor candidates using AI. The input is the analysis results of the thought patterns obtained in Step 2. Specifically, the server compares the current leader's thought patterns with those of the candidates and calculates the similarity. It also evaluates candidates who may bring new perspectives. The output is a list of proposed successor candidates.

[0927] Step 4:

[0928] The terminal presents the user with information on proposed successor candidates and assists in the selection process. The input is the list of successor candidates obtained in step 3. Specifically, the terminal visually displays each candidate's strengths, weaknesses, and suitability. Based on this information, the user can select the most suitable successor. The output is the successor selected by the user.

[0929] Step 5:

[0930] The server provides the necessary information to ensure the selected successor receives approval. The successor selected by the user in step 4 is used as input. Specifically, the server generates a detailed report detailing the selected successor's experience and skill set, and sends it to the relevant parties. The output is a detailed report to support the approval process.

[0931] (Application Example 1)

[0932] Next, we will describe Application Example 1 of Form 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."

[0933] Selecting successors for robot maintenance personnel in factories often relies on experience and intuition, making it difficult to choose the right person. Furthermore, the inability to effectively utilize on-site work history and evaluations reduces the efficiency of successor selection.

[0934] 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.

[0935] In this invention, the server includes means for analyzing human thought patterns, means for suggesting successor candidates based on the analysis results, and means for suggesting the most suitable successor based on work history and evaluation. This makes it possible to efficiently and appropriately select successors for robot maintenance personnel in factories.

[0936] "Methods for analyzing human thought patterns" refer to techniques for analyzing a person's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[0937] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting and presenting appropriate successor candidates by utilizing the results of an analysis of thought patterns.

[0938] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide the necessary information to choose the most suitable person from among the presented candidates and assist in the selection process.

[0939] "Means of obtaining approval for selected successors" refers to technologies that provide information demonstrating the suitability and abilities of selected successors in order to obtain approval.

[0940] "A method for proposing the most suitable successor based on work history and evaluations" refers to a technology that utilizes a candidate's past work history and evaluation data to select the most suitable successor.

[0941] "Methods for recording and analyzing on-site work" refers to techniques for recording activities at the work site and analyzing that data to identify areas for improvement and streamline operations.

[0942] The system for implementing this invention operates through the collaboration of a server, a terminal, and a user. The server runs a program that uses artificial intelligence to analyze human thought patterns. Specifically, it uses machine learning libraries such as TensorFlow to analyze the candidate's past behavior, statements, and evaluation data, and learns their thought patterns. Based on the analysis results, the server can then propose successor candidates.

[0943] The device functions as smart glasses or other wearable devices to record work performed on-site. It uses a camera and microphone to capture the work in real time and transmits the data to a server. The server analyzes this data, reviewing work history and performance evaluations, and suggests the most suitable successor.

[0944] The user receives information on proposed successor candidates through their device and makes a selection. Based on the information displayed on the device's screen, the user can evaluate each candidate's strengths and weaknesses and choose the most suitable person. To obtain approval for the selected successor, the server provides information indicating their suitability and abilities.

[0945] One concrete example is analyzing how maintenance personnel at a particular factory have solved problems in the past and learning their thought patterns. An example of a prompt for a generative AI model would be, "Based on past maintenance history and evaluation data, please suggest the most suitable candidate for the next maintenance personnel."

[0946] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0947] Step 1:

[0948] The terminal records on-site work using a camera and microphone. It acquires video and audio data of the work as input and transmits this data to the server in real time. The output is the work data transmitted to the server.

[0949] Step 2:

[0950] The server analyzes the received work data. It receives video and audio data transmitted from the terminal as input and analyzes the data using a machine learning model. Specifically, it uses TensorFlow to evaluate work efficiency and problem-solving methods. The output consists of work history and evaluation data as analysis results.

[0951] Step 3:

[0952] The server analyzes human thought patterns based on the analysis results. Using work history and evaluation data as input, the artificial intelligence learns the candidate's thought patterns. The output is the analysis result of the thought patterns.

[0953] Step 4:

[0954] The server proposes successor candidates based on the analysis results. It uses the analysis results of thought patterns as input and selects the optimal successor candidate using a generative AI model. The output is a list of proposed successor candidates.

[0955] Step 5:

[0956] The user receives information about proposed successor candidates through their device. The device receives a list of candidates sent from the server as input and displays it on the device's screen. The output is candidate information for the user to review.

[0957] Step 6:

[0958] The user selects the most suitable successor based on candidate information. The input involves evaluating and selecting candidate information displayed on the terminal. The output is information about the selected successor.

[0959] Step 7:

[0960] The server provides the information necessary to obtain approval for the selected successor. It receives information about the selected successor as input and generates data indicating their suitability and abilities. The output is the information required for approval.

[0961] (Example 2)

[0962] Next, we will describe Example 2 of the morphological example. 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."

[0963] Traditional successor selection processes have faced challenges in selecting a suitable successor because they fail to adequately analyze the thought patterns of candidates. Furthermore, a lack of information necessary to obtain approval for the selected successor hinders the smooth approval process.

[0964] 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.

[0965] In this invention, the server includes means for collecting and pre-processing information, means for analyzing thought patterns using the pre-processed information, and means for selecting and proposing candidates based on the analysis results. This makes it possible to analyze the thought patterns of candidates in detail and select an appropriate successor. Furthermore, by providing information for obtaining approval from the selected successor, the approval process can be facilitated.

[0966] "Means for collecting and pre-processing information" refers to technologies for collecting data about candidates and processing it to convert it into a format suitable for analysis.

[0967] "Methods for analyzing thought patterns" refer to techniques that analyze candidates' values ​​and decision-making tendencies based on collected data.

[0968] "Methods for selecting and proposing candidates" refers to the techniques for selecting and proposing appropriate successor candidates based on analysis results.

[0969] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and learn patterns.

[0970] "Similarity" is an indicator that shows how closely the thinking patterns of the current leader and the candidate match.

[0971] A "candidate who can offer a new perspective" is someone who possesses different values ​​and approaches from traditional leaders and has the potential to bring a new direction to the organization.

[0972] This invention takes the form of a system in which a server, terminal, and user cooperate to implement a successor selection support system.

[0973] The server first collects information about the candidates. Specifically, it retrieves data such as the candidates' past behavior, statements, and evaluations from company databases and publicly available information. The server preprocesses this data, cleaning text data and normalizing numerical data. This transforms the data into a format suitable for analysis.

[0974] Next, the server inputs the pre-processed data into a generative AI model to analyze the candidates' thought patterns. The generative AI model uses natural language processing techniques to extract values ​​and decision-making tendencies from the text data. The results of this analysis are quantified for each candidate's thought pattern and stored in a database.

[0975] The server selects successor candidates based on the analysis results. Specifically, it compares the current leader's thinking patterns with those of the candidates and lists candidates with high similarity or those who can offer new perspectives. This list of selected candidates is then sent to the terminal.

[0976] The terminal displays a list of candidates received from the server to the user. It provides a dashboard that visually shows each candidate's strengths, weaknesses, and suitability, helping the user select the most suitable successor.

[0977] The user selects the most suitable successor based on information provided through the device. To obtain approval for the selected successor, the user creates a presentation for the approval meeting. Using information on the candidate's achievements and abilities provided through the device, the user explains the suitability of the selected successor at the approval meeting and persuades the approval committee to approve it.

[0978] For example, if a candidate has a track record of successfully completing innovative projects in the past, the server will use that track record to suggest the candidate as a successor who could bring a fresh perspective. An example of a prompt to the generative AI model would be: "Based on the candidate's past behavioral data, analyze their leadership style and suggest successor candidates who are a good match for the current leader or who can offer a fresh perspective."

[0979] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0980] Step 1:

[0981] The server collects information about candidates. As input, it retrieves data such as candidates' past behavior, statements, and evaluations from company databases and publicly available information. Specifically, the server collects data from social media posting history and internal evaluation systems via APIs. The output is the collected raw data.

[0982] Step 2:

[0983] The server preprocesses the collected data. It uses the raw data obtained in step 1 as input. Specifically, the server cleans text data and normalizes numerical data, converting it into a format suitable for analysis. The output is the preprocessed data.

[0984] Step 3:

[0985] The server inputs pre-processed data into a generating AI model to analyze the candidates' thought patterns. The pre-processed data obtained in step 2 is used as input. Specifically, the server uses natural language processing techniques to extract values ​​and decision-making tendencies from the text data. The output is numerical data representing each candidate's thought pattern.

[0986] Step 4:

[0987] The server selects successor candidates based on the analysis results. The input is the quantified data of thought patterns obtained in step 3. Specifically, the server compares the current leader's thought patterns with those of the candidates, listing candidates with high similarity or those who can offer new perspectives. The output is a list of selected candidates.

[0988] Step 5:

[0989] The server sends the selected candidate list to the terminal. The candidate list obtained in step 4 is used as input. Specifically, the server uses a communication protocol to transfer the data to the terminal. The candidate list is displayed on the terminal as output.

[0990] Step 6:

[0991] The terminal displays the candidate list received from the server to the user. The candidate list submitted in step 5 is used as input. Specifically, the terminal provides a dashboard that visually displays each candidate's strengths, weaknesses, and suitability. The output is a screen that allows the user to visually review the information.

[0992] Step 7:

[0993] The user selects the most suitable successor based on the information provided through the device. The candidate information displayed in step 6 is used as input. Specifically, the user compares the candidates' evaluation points and makes a decision. The selected successor is obtained as output.

[0994] Step 8:

[0995] The user creates materials to obtain approval for the selected successor. The input uses the information of the successor selected in step 7. Specifically, the user uses information about the candidate's achievements and abilities provided by the terminal to create a presentation for the approval meeting. The output is the presentation materials to be used at the approval meeting.

[0996] (Application Example 2)

[0997] Next, we will describe application example 2 of form 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."

[0998] In selecting managers for machinery and equipment within a factory, there is a need to efficiently identify suitable candidates and ensure a smooth selection process. However, traditional methods make it difficult to adequately evaluate candidates' thought patterns and aptitudes, posing a challenge in selecting appropriate managers.

[0999] 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.

[1000] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for assisting in the selection of proposed successor candidates, means for obtaining approval from the selected successor, means for proposing candidates for managers of machinery and equipment within the factory, means for assisting in the selection of proposed manager candidates, and means for obtaining approval from the selected manager. This makes it possible to efficiently and accurately select managers for machinery and equipment within the factory.

[1001] "Human thought patterns" refer to the unique ways of thinking and decision-making tendencies that each individual possesses.

[1002] "Analysis results" refer to the information and data obtained after artificial intelligence analyzes human thought patterns.

[1003] A "successor candidate" refers to a person who has the potential to take over a specific role or position.

[1004] A "proposed successor candidate" refers to a person who has been selected based on the analysis results and is deemed suitable to be the successor.

[1005] The term "selected successor" refers to the person ultimately chosen from among the proposed candidates.

[1006] "Means of obtaining approval" refers to the methods and processes by which a selected successor obtains the necessary consent or permission to formally assume that role.

[1007] "Candidate for manager of machinery and equipment within the factory" refers to a person who may be responsible for managing the machinery and equipment used within the factory.

[1008] A "proposed manager candidate" is a person selected based on the analysis results as being suitable to manage the machinery and equipment.

[1009] The term "selected administrator" refers to the person ultimately chosen from among the proposed administrator candidates.

[1010] The system for implementing this invention is designed to efficiently select managers for machinery and equipment within a factory. The server uses artificial intelligence to analyze human thought patterns and proposes manager candidates based on past behavioral history and evaluation data. Specifically, it uses Python and leverages machine learning libraries such as TensorFlow and PyTorch to analyze candidate data.

[1011] Smart glasses are used as the terminal, displaying real-time information on suggested administrator candidates and support for selection. This allows users to visually confirm the strengths and weaknesses of candidates and make the best choice.

[1012] As a concrete example, maintenance history and troubleshooting records of robots within a factory are used as data. Based on this, the server inputs a prompt message into the AI ​​model saying, "Please suggest the most suitable robot administrator based on past maintenance history and troubleshooting records," and then suggests the most suitable administrator candidates.

[1013] This system is expected to enable efficient and accurate selection of managers within the factory, and to facilitate a smooth transition of leadership.

[1014] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1015] Step 1:

[1016] The server collects past behavioral history and evaluation data of management candidates within the factory. This data is retrieved from a database and used as input to analyze the candidates' thought patterns.

[1017] Step 2:

[1018] The server runs an artificial intelligence model using the collected data. Specifically, a machine learning model built using TensorFlow or PyTorch analyzes the candidates' thinking patterns. This analysis outputs the characteristics and tendencies of each candidate.

[1019] Step 3:

[1020] The server generates a list of administrator candidates based on the analysis results. Using the generation AI model, it receives the prompt message, "Please suggest the most suitable robot administrator based on past maintenance history and troubleshooting experience," and proposes the most suitable candidates. This list takes into account the candidates' aptitudes and strengths.

[1021] Step 4:

[1022] The smart glasses, acting as the terminal, receive a list of administrator candidates sent from the server and display it to the user. The user can view detailed information about the candidates and make a selection through the smart glasses.

[1023] Step 5:

[1024] The user selects the most suitable administrator candidate based on the information displayed through their smart glasses. The selected information is sent to the server, and the final approval process begins.

[1025] Step 6:

[1026] The server generates the necessary information to obtain approval for the selected administrator candidate and notifies the relevant parties. This allows the process for the selected administrator to officially assume their role to proceed.

[1027] (Example 3)

[1028] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1029] Traditional successor selection processes have made it difficult to accurately analyze candidates' thought patterns and propose suitable successors. Furthermore, the selection and approval processes for proposed candidates lacked support based on objective information, resulting in decreased efficiency and accuracy.

[1030] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1031] In this invention, the server includes means for collecting information, means for pre-processing the collected information, and means for analyzing thought patterns using the pre-processed information. This makes it possible to accurately analyze the thought patterns of candidates and propose appropriate successors. It also enables the efficient process of assisting in the selection of proposed candidates and obtaining approval from the selected candidates.

[1032] "Means of collecting information" refers to methods and devices for obtaining data on a candidate's past actions, statements, and evaluations.

[1033] "Means for preprocessing collected information" refers to methods or devices for converting acquired data into a format that is easily processed by AI models.

[1034] "Means for analyzing thought patterns" refers to methods or devices that use pre-processed data to analyze candidates' thought patterns using machine learning algorithms.

[1035] "Means of proposing candidates" refers to methods or devices for selecting and presenting the most suitable successor candidate based on the analysis results.

[1036] "Means of supporting selection" refer to methods or devices that provide information to help select the most suitable person from among the proposed candidates.

[1037] "Means of obtaining approval" refers to methods and devices that provide the necessary information to selected candidates to enable them to obtain approval and support the approval process.

[1038] To implement this invention, a server plays a central role. The server collects data about candidates, preprocesses it, and inputs it into the AI ​​model. Specifically, the server uses a database management system to obtain information such as candidates' past actions, statements, and evaluations. For data preprocessing, NLTK, a Python natural language processing library, is used to tokenize the text data and remove unnecessary words.

[1039] The server inputs pre-processed data into an AI model using machine learning frameworks such as TensorFlow and PyTorch to analyze the candidates' thought patterns. This analysis models the candidates' past behavior and values, and proposes the most suitable successor candidate.

[1040] The user receives candidate suggestions from the server and makes a selection. The server evaluates each candidate's strengths and weaknesses and provides the user with this information in HTML format. This allows the user to choose the best candidate based on objective information.

[1041] Furthermore, the server generates presentation materials to obtain approval from the selected candidates. Specifically, it creates slides in PowerPoint format that demonstrate the suitability of the selected candidates and provides the user with a download link.

[1042] As a concrete example, by inputting the following prompt into the generative AI model, you can obtain suggestions for successor candidates.

[1043] Prompt: "Please suggest a successor candidate whose thinking pattern most closely matches that of the current leader. Please consider the candidate's past project choices and values."

[1044] By using this prompt, the AI ​​can suggest a suitable successor candidate. The flow of the specific processing in Example 3 will be explained using Figure 15.

[1045] Step 1:

[1046] The server collects data about the candidates. As input, it retrieves information about the candidates' past actions, statements, and evaluations from a database. Specifically, the server executes SQL queries to extract the necessary data. The output is the collected raw data.

[1047] Step 2:

[1048] The server preprocesses the collected data. It uses the raw data obtained in step 1 as input. Specifically, the server tokenizes the text data using the Python NLTK library and removes stop words. The output is the preprocessed, clean data.

[1049] Step 3:

[1050] The server analyzes thought patterns using pre-processed data. The clean data obtained in step 2 is input to the AI ​​model. Specifically, the server uses TensorFlow to execute machine learning algorithms and extract the candidates' thought patterns. The output is the analyzed thought pattern data.

[1051] Step 4:

[1052] The server proposes successor candidates based on the analysis results. It uses the thought pattern data obtained in step 3 as input. Specifically, the server performs similarity calculations and selects the most suitable candidate. The output is a list of proposed successor candidates.

[1053] Step 5:

[1054] The user receives information to select the most suitable person from the proposed candidates. The candidate list obtained in step 4 is used as input. Specifically, the server evaluates the strengths and weaknesses of each candidate and provides this information to the user in HTML format. The output is detailed candidate information that the user can view.

[1055] Step 6:

[1056] The server generates materials to obtain approval for the selected successor. It uses information about the candidate selected by the user as input. Specifically, the server creates a presentation in PowerPoint format and provides the user with a download link. The output is materials to support the approval process.

[1057] (Application Example 3)

[1058] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[1059] Traditional successor selection processes have made it difficult to accurately analyze candidates' thought patterns and select the appropriate successor. Furthermore, the lack of real-time information during the selection process has resulted in reduced efficiency and accuracy.

[1060] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1061] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, and means for presenting the analysis results using a visual display device. This enables accurate analysis of candidates' thought patterns and efficient and accurate successor selection through real-time information provision.

[1062] "Methods for analyzing human thought patterns" refer to technologies that use artificial intelligence to analyze a candidate's thinking tendencies based on data such as their past actions, statements, and evaluations.

[1063] "A method for proposing successor candidates based on analysis results" is a technique that, based on analyzed thought patterns, presents individuals who have a similar way of thinking to the current leader or who can offer new perspectives as potential successors.

[1064] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide information on each candidate's strengths, weaknesses, and suitability in order to select the most suitable person from among the presented candidates.

[1065] "Means of obtaining approval for selected successors" refers to technologies that provide information demonstrating the suitability and abilities of a selected successor, thereby supporting the approval process and enabling them to gain approval.

[1066] "Means of presenting analysis results using a visual display device" refers to a technology that uses visual devices such as smart glasses to display the analyzed information to the user in real time.

[1067] "Methods for collecting candidates' past behavioral data and analyzing it with artificial intelligence models" refers to technologies that collect candidates' past behavioral history and evaluation data, input this data into an artificial intelligence model, and analyze it.

[1068] "Means of displaying analysis results in real time" refers to technology that instantly displays the analyzed information on a visual display device, allowing users to check that information in real time.

[1069] The system for implementing this invention operates through the coordinated efforts of a server, a terminal, and a user. The server collects past behavioral data of candidates and analyzes this data using an artificial intelligence model. Specifically, it uses an AI model built with Python (e.g., TensorFlow) to analyze the candidates' thought patterns. The analyzed data is presented to the user in real time through a visual display device, such as smart glasses (e.g., Google Glass).

[1070] The terminal receives analysis results sent from the server and displays them on a visual display device. The user wears smart glasses and selects a successor based on the presented information. This allows the user to check the candidate's thought patterns in real time and select the appropriate successor.

[1071] As a concrete example, during leadership training within a factory, the server uses AI to analyze the candidate's past project management skills and teamwork evaluations, and displays a message on smart glasses via a terminal saying, "Candidate A has strong project management skills and excellent teamwork abilities."

[1072] An example of a prompt to input into the generating AI model is: "Based on candidate A's past project management data, analyze their thinking patterns and evaluate their leadership aptitude."

[1073] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1074] Step 1:

[1075] The server collects past behavioral data of candidates. It uses candidate behavioral history and evaluation data obtained from the factory's management system as input. This data is stored in a database in preparation for subsequent analysis.

[1076] Step 2:

[1077] The server inputs the collected data into an artificial intelligence model to analyze the candidates' thinking patterns. The behavioral data collected in Step 1 is used as input. Using an AI model (e.g., TensorFlow), the candidate's thinking tendencies and leadership aptitude are analyzed, and the analysis results are generated. The output is the analyzed thinking pattern data.

[1078] Step 3:

[1079] The server sends the analysis results to the terminal. The thought pattern data obtained in step 2 is used as input. The terminal prepares to display the received data on a visual display device. The output generates data in a displayable format.

[1080] Step 4:

[1081] The terminal presents the analysis results to the user through a visual display device. The displayable data prepared in step 3 is used as input. Information such as "Candidate A has strong project management skills and excellent teamwork abilities" is displayed in real time on the smart glasses. The output provides information that the user can visually confirm.

[1082] Step 5:

[1083] The user selects a successor based on the information presented. The input is the analysis results displayed on smart glasses. The user evaluates the suitability and abilities of the candidates and selects the most suitable successor. The output is information about the selected successor.

[1084] 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.

[1085] "Example of form 1"

[1086] One embodiment of the present invention provides a successor selection support system that incorporates an emotion engine. This system uses an emotion engine that recognizes the user's emotions as a means of analyzing human thought patterns. The emotion engine recognizes emotions from the user's facial expressions, tone of voice, and choice of words, and uses the results to analyze thought patterns. For example, if a user shows joy or excitement, it is determined that the user is likely to have a positive thought pattern. Conversely, if a user shows anger or dissatisfaction, it is determined that the user is likely to have a negative thought pattern.

[1087] "Example of form 2"

[1088] Furthermore, the results of the emotion recognition process by the emotion engine will also be taken into consideration when proposing successor candidates. Specifically, the emotional state of the candidates will be analyzed, and successor candidates will be proposed based on the results. For example, if a candidate can consistently maintain a calm and composed emotional state, it will be judged that the candidate is likely to be able to make calm judgments even under pressure, and will be proposed as a successor.

[1089] "Example of form 3"

[1090] Furthermore, as a means of obtaining approval from the selected successor, the system predicts the approver's reaction based on the emotion recognition results from the emotion engine. Specifically, it analyzes the approver's emotional state and predicts the likelihood that the approver will approve the successor based on the results. For example, if the approver shows joy or excitement, it is predicted that the approver is likely to approve the successor.

[1091] The following describes the processing flow for each example of the form.

[1092] "Example of form 1"

[1093] Step 1: Activate the emotion engine, which recognizes emotions from the user's facial expressions, tone of voice, and word choice.

[1094] Step 2: The emotion engine recognizes the user's emotions and uses the results to analyze their thought patterns.

[1095] Step 3: If a user shows joy or excitement, it is likely that they have a positive thought pattern.

[1096] Step 4: If a user expresses anger or dissatisfaction, it is likely that the user has a negative thought pattern.

[1097] "Example of form 2"

[1098] Step 1: Analyze the candidate's emotional state using the emotion engine.

[1099] Step 2: Propose successor candidates based on the analysis results.

[1100] Step 3: If a candidate can consistently maintain a calm and composed emotional state, they are likely to be able to make calm decisions even under pressure, and should be proposed as a successor.

[1101] "Example of form 3"

[1102] Step 1: Analyze the approver's emotional state using the emotion engine.

[1103] Step 2: Based on the analysis results, predict the likelihood that the approver will approve the successor.

[1104] Step 3: If the approver shows joy or excitement, predict that they are likely to approve the successor.

[1105] (Example 1)

[1106] Next, we will describe Embodiment 1 of 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."

[1107] Traditional successor selection processes often fail to adequately consider candidates' thought patterns and emotions, making it difficult to select a suitable successor. Furthermore, these processes often lack objective data-based evaluations and rely heavily on subjective judgments. Therefore, improving the accuracy of successor selection is crucial.

[1108] 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.

[1109] In this invention, the server includes means for collecting human behavioral data, means for analyzing thought patterns using the collected data, and means for analyzing the user's emotions using emotion recognition technology and reflecting them in the thought patterns. This makes it possible to comprehensively evaluate the thought patterns and emotions of candidates and objectively select an appropriate successor.

[1110] "Human behavioral data" refers to information including an individual's past actions, statements, and evaluations, and is used to analyze thought patterns.

[1111] "Thinking patterns" are characteristics that indicate an individual's values ​​and judgment criteria, and are analyzed from their past actions and statements.

[1112] "Emotion recognition technology" is a technology that analyzes emotions from a user's facial expressions, tone of voice, and word choice, and uses the results to analyze their thought patterns.

[1113] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate results that meet specific objectives.

[1114] A "potential successor" refers to a person who has the potential to take on the next leadership role within an organization or group.

[1115] "Aptitude" refers to characteristics that indicate an individual's ability or suitability for a particular role or job.

[1116] This invention provides a concrete method for realizing a successor selection support system. First, the server collects human behavior data. This data is obtained from corporate databases and publicly available information and extracted using SQL queries. Next, the server analyzes thought patterns using the collected data. For the analysis, machine learning frameworks such as TensorFlow and PyTorch are used to construct a neural network model. This allows the system to analyze the candidate's past behavior and values ​​and learn their thought patterns.

[1117] Furthermore, the server analyzes the user's emotions using emotion recognition technology. The emotion engine recognizes emotions from the user's facial expressions, tone of voice, and word choice, and uses the results to analyze thought patterns. For example, if a user shows joy or excitement, the system determines that the user is likely to have positive thought patterns.

[1118] The device provides users with information on proposed successor candidates. Specifically, it displays a dashboard that visualizes each candidate's strengths, weaknesses, and suitability, helping users select the most suitable successor.

[1119] Using a generative AI model, the server evaluates the suitability of candidates. An example of a prompt is: "Based on Candidate A's past behavioral data, analyze their thinking patterns suitable for leadership and evaluate their suitability as a successor candidate." In response to this prompt, the generative AI model analyzes Candidate A's data and evaluates their suitability as a successor.

[1120] In this way, servers, terminals, and users can cooperate to efficiently carry out the successor selection process.

[1121] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1122] Step 1:

[1123] The server collects candidate behavior data from the company's database. Inputs include candidate IDs and queries to the relevant database. The server executes SQL queries to extract data such as the candidate's past behavior, statements, and evaluations. The output is a dataset formatted for analysis. Specifically, a Python script is used to connect to the database and retrieve the necessary data.

[1124] Step 2:

[1125] The server analyzes thought patterns using the collected data. The input is the dataset obtained in Step 1. The server builds a neural network model using TensorFlow and analyzes the data. This allows it to learn the candidates' thought patterns and generate analysis results. The output is a feature vector representing each candidate's thought pattern. Specifically, the data is preprocessed, input into the model, and then analyzed.

[1126] Step 3:

[1127] The server analyzes the user's emotions using emotion recognition technology. The input consists of emotional data such as the user's facial expressions, tone of voice, and word choice. The server uses an emotion engine to analyze this data and determine the user's emotional state. The output is an indicator of the user's emotions. Specifically, it analyzes data acquired from cameras and microphones in real time.

[1128] Step 4:

[1129] The server proposes successor candidates based on the analysis results. The input is the thought patterns and emotional indicators obtained in steps 2 and 3. The server uses a generative AI model to evaluate the suitability of the candidates and lists the most suitable successor candidates. The output is a list of successor candidates. Specifically, the AI ​​model is run using the prompt message "Based on the candidates' thought patterns, please propose individuals suitable for leadership."

[1130] Step 5:

[1131] The terminal provides the user with information on the proposed successor candidates. The input is the list of successor candidates obtained in step 4. The terminal displays a dashboard that visualizes each candidate's strengths, weaknesses, and suitability. The output is an interface for candidate information that the user can view. Specifically, a web application is used to generate the dashboard and present it to the user.

[1132] Step 6:

[1133] The user selects the most suitable successor based on the information provided from the terminal. The input is the candidate information displayed in step 5. The user compares the information of each candidate and selects the most suitable person. The output is the ID of the selected successor. Specifically, the user selects a candidate on the interface and sends the selection result to the server.

[1134] Step 7:

[1135] The server assists in the process of obtaining approval for the selected successor. The input is the ID of the successor selected in step 6. The server generates a report showing the suitability and abilities of the selected successor and distributes it to the relevant parties. The output is a report to support the approval process. Specifically, the report is generated in PDF format and sent to the relevant parties via email.

[1136] (Application Example 1)

[1137] Next, we will describe Application Example 1 of Form 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."

[1138] Selecting robot managers within factories presents a challenge: traditional methods fail to adequately consider candidates' thought patterns and emotions, making it difficult to choose the most suitable manager. In particular, a lack of analysis based on candidates' emotions and past behavior makes effective leadership transfer difficult.

[1139] 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.

[1140] In this invention, the server includes means for analyzing human thought patterns, means for recognizing emotions, and means for utilizing the recognized emotions in the analysis of thought patterns. This makes it possible to propose candidates for managers within a factory and select the most suitable manager.

[1141] "Methods for analyzing human thought patterns" refer to techniques for analyzing a candidate's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[1142] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting the most suitable successor candidate by using the results of analyzing thought patterns.

[1143] "Means to support the selection of proposed successor candidates" refers to technologies that provide the information necessary to choose the most suitable person from among the proposed candidates.

[1144] "Means of obtaining approval for a selected successor" refers to technologies that provide information demonstrating the suitability and abilities of a selected successor in order to obtain approval.

[1145] "Means of recognizing emotions" refers to technologies that analyze emotions from a user's facial expressions, tone of voice, word choice, and other factors.

[1146] "Methods for utilizing recognized emotions in the analysis of thought patterns" refers to techniques that use the results of emotion analysis to perform a more precise analysis of thought patterns.

[1147] "A method for proposing candidates for factory managers" refers to a technology that uses the results of thought pattern and emotion analysis to select candidates for robot managers within a factory.

[1148] To implement this invention, a system is built in which a server plays a central role. The server runs a program that integrates an artificial intelligence model and an emotion engine. Specifically, it uses Python combined with TensorFlow and OpenCV to collect and analyze the candidate's past behavior logs, voice data, and facial expression data. This makes it possible to analyze the candidate's thought patterns and emotions in detail.

[1149] The server first retrieves the candidate's past actions and statements from a database and analyzes their thought patterns using an artificial intelligence model. Next, it uses an emotion engine to recognize emotions from voice and facial expression data and utilizes the results in the analysis of thought patterns. This provides the basic data needed to propose candidates for management positions within the factory.

[1150] As a concrete example, the server evaluates candidate A's suitability as the optimal robot administrator based on their past behavioral logs and emotional data. An example of a prompt to be input to the generating AI model in this case would be, "Please evaluate candidate A's suitability as the optimal robot administrator based on their past behavioral logs and emotional data."

[1151] This system allows users to obtain information to select the most suitable robot manager within the factory, enabling them to build a more efficient management system.

[1152] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1153] Step 1:

[1154] The server retrieves past behavior logs, voice data, and facial expression data of candidates from the database. The input is the candidate's ID, and the output is the corresponding dataset. This collects information about the candidate's past behavior and statements.

[1155] Step 2:

[1156] The server inputs the acquired behavioral logs into an artificial intelligence model and analyzes the thought patterns. The input is behavioral log data, and the output is the result of the thought pattern analysis. As part of the data processing, the behavioral logs are converted into features and then analyzed by the AI ​​model.

[1157] Step 3:

[1158] The server inputs voice data and facial expression data into the emotion engine to recognize emotions. The input is voice data and facial expression data, and the output is the result of emotion recognition. It analyzes changes in voice tone and facial expression to identify emotions.

[1159] Step 4:

[1160] The server integrates the results of thought pattern analysis and emotion recognition to evaluate the suitability of management candidates. The input is data on thought patterns and emotions, and the output is the result of the suitability evaluation. The data calculation involves comparing the two and quantifying the suitability.

[1161] Step 5:

[1162] The server uses a generative AI model to generate prompt messages and suggest the most suitable administrator candidates. The input is the result of the aptitude assessment, and the output is the prompt message and candidate suggestions. It performs the operation of generating the prompt message, "Please evaluate candidate A's suitability as the most suitable robot administrator based on their past behavior logs and sentiment data."

[1163] Step 6:

[1164] The user selects the most suitable administrator candidate based on information provided by the server. The input is the server's suggestions, and the output is the selected administrator candidate. The user reviews the provided data and determines the best candidate.

[1165] (Example 2)

[1166] Next, we will describe Example 2 of the morphological example. 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."

[1167] Traditional succession selection processes have faced challenges in selecting appropriate successors because they make it difficult to adequately consider an individual's decision-making patterns and emotional states. Furthermore, there was a problem in evaluating the degree of alignment with the current leader's thinking patterns and the potential to offer new perspectives when selecting successor candidates.

[1168] 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.

[1169] In this invention, the server includes means for analyzing an individual's decision-making patterns, means for presenting successor candidates based on the analysis results, and means for recognizing emotional states and presenting successor candidates based on those results. This makes it possible to select a successor that takes into account an individual's decision-making patterns and emotional states, and to efficiently select an appropriate successor.

[1170] An "information processing device" is a computer system used to collect, analyze, and process data.

[1171] An "individual decision-making pattern" refers to the tendencies of choices and judgments that a particular individual has made in the past.

[1172] "Analysis" is the process of examining data in detail and understanding its structure and meaning.

[1173] A "potential successor" is someone who has the potential to take over the role of the current leader.

[1174] "Presentation" refers to the act of showing specific information or options to others.

[1175] "Emotional state" refers to the emotional state an individual is experiencing at a particular moment.

[1176] "Machine learning technology" is a technique that allows computers to learn patterns from data and make predictions and decisions.

[1177] A "similar individual" is a person who shares common characteristics with other individuals based on specific criteria.

[1178] A "new perspective" refers to a new viewpoint or approach that differs from conventional ways of thinking or methods.

[1179] This invention provides a specific model for implementing a successor selection support system. The server collects data from a database, including candidates' past actions, statements, and evaluations, in order to analyze their individual decision-making patterns. This is achieved by using SQL queries to extract information from the database.

[1180] The server uses the collected data to build a machine learning model using the Python TensorFlow library, and learns and analyzes the candidates' decision-making patterns. This model uses a neural network to analyze the candidates' thinking patterns from their past actions and statements.

[1181] Furthermore, the server uses an emotion engine to recognize the emotional state of the candidates and presents potential successors based on the results. Emotion recognition uses algorithms to analyze emotional states.

[1182] As a concrete example, here is an example of a prompt sentence to be input into a generative AI model: "Based on Candidate A's past leadership evaluation data, analyze their thinking patterns and evaluate their suitability as a successor." By using this prompt sentence, the AI ​​model can analyze Candidate A's data and evaluate their suitability as a successor.

[1183] In this way, the server can select a successor by taking into account an individual's decision-making patterns and emotional state, enabling it to efficiently select an appropriate successor.

[1184] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1185] Step 1:

[1186] The server collects data from the database, including the candidate's past actions, statements, and evaluations. Input requires the candidate's ID and related queries. The server uses SQL queries to access the database and extract the necessary information. Output includes the candidate's past behavioral history and evaluation data.

[1187] Step 2:

[1188] The server uses the TensorFlow library in Python to build a machine learning model based on the collected data. The input requires candidate data obtained in Step 1. The server uses this data to train a neural network and learn the candidates' decision-making patterns. The output is a model representing the candidates' thinking patterns.

[1189] Step 3:

[1190] The server uses a machine learning model to analyze the candidates' thought patterns. The input requires the model built in Step 2 and the candidates' data. The server applies the model to analyze the candidates' decision-making tendencies and values. The output provides the analysis results, which are then used to suggest potential successors.

[1191] Step 4:

[1192] The server uses an emotion engine to recognize the candidate's emotional state. Input requires the candidate's voice data and facial expression data. The server applies an emotion recognition algorithm to analyze the candidate's emotional state. The output provides information about the candidate's emotional state.

[1193] Step 5:

[1194] The server presents potential successors based on the analysis results and emotional state. The input requires the data obtained in steps 3 and 4. The server integrates this information and presents individuals with similar thought patterns to the current leader, as well as individuals who may offer new perspectives. The output is a list of potential successors.

[1195] Step 6:

[1196] The server provides information to select the most suitable person from the presented successor candidates. The input is the candidate list obtained in step 5. The server evaluates each candidate's strengths, weaknesses, and suitability, and generates information to support the selection process. The output is selection support information.

[1197] Step 7:

[1198] The server assists in obtaining approval for the selected successor. The input requires information on the candidate selected in step 6. The server generates a report outlining the suitability and capabilities of the selected successor, supporting the approval process. The output is an approval support report.

[1199] (Application Example 2)

[1200] Next, we will describe application example 2 of form 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."

[1201] In selecting successors, there is a need for a more objective and efficient selection process by appropriately analyzing candidates' thought patterns and emotional states and presenting the information visually. However, traditional methods make it difficult to comprehensively consider these factors, and the selection process tends to be subjective.

[1202] 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.

[1203] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, means for supporting the selection of proposed successor candidates, means for visually presenting candidate information using a visual display device, and means for analyzing emotional states. This enables a more objective and efficient successor selection by comprehensively analyzing the candidates' thought patterns and emotional states and visually presenting the information.

[1204] "Methods for analyzing human thought patterns" refer to techniques that analyze a person's thinking tendencies and values ​​based on their past actions, statements, and evaluations.

[1205] "A method for proposing successor candidates based on analysis results" refers to a technique for selecting appropriate successor candidates using the results of analyzing thought patterns.

[1206] "Means of supporting the selection of proposed successor candidates" refers to technologies that provide the information necessary to select the most suitable person from among the presented candidates.

[1207] "Means of obtaining approval for selected successors" refers to technologies that support the information and procedures necessary for a selected successor to be formally approved.

[1208] "Means of visually presenting candidate information using a visual display device" refers to a technology that displays information about a candidate through a visual device such as a display, making it easy for users to understand.

[1209] "Methods for analyzing emotional states" refer to techniques that analyze the emotional state of candidates and utilize the results in selecting a successor.

[1210] The system for implementing this invention operates through the coordinated efforts of a server, a terminal, and a user. The server uses artificial intelligence to analyze human thought patterns. Specifically, it collects data on candidates' past actions, statements, and evaluations, and analyzes this data using a generative AI model. Based on the analysis results, it generates data to propose successor candidates.

[1211] The terminal acts as a visual display device, visually presenting analysis results transmitted from the server to the user. For example, it may use smart glasses or a tablet device to display information such as the candidate's strengths, weaknesses, and aptitudes. Furthermore, it may use an emotion engine to analyze the candidate's emotional state in real time and visually present the results.

[1212] The user selects successor candidates based on information presented through their device. The server then assists in obtaining approval for the selected candidates. Specifically, it provides information indicating the suitability and abilities of the selected candidates, facilitating a smooth approval process.

[1213] As a concrete example, when selecting a robot manager in a factory, the current manager, wearing smart glasses, reviews the candidates' past project decisions and evaluations, and then selects a candidate capable of making calm judgments based on the analysis results from the emotion engine. An example of a prompt to the generated AI model would be: "Analyze candidate A's past project decisions and evaluate their thought patterns. Also, based on the analysis results from the emotion engine, evaluate their ability to make decisions under pressure."

[1214] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1215] Step 1:

[1216] The server collects data on candidates' past behavior, statements, and evaluations. As input, it retrieves databases and historical information related to the candidates and prepares this as a dataset for analysis. As output, it generates a dataset formatted for analysis.

[1217] Step 2:

[1218] The server uses a generative AI model to analyze candidates' thought patterns from the collected dataset. The dataset formatted in Step 1 is used as input, and prompt sentences are fed into the AI ​​model. As data processing, the AI ​​model analyzes the data and extracts candidates' thought patterns. The output is the analysis results of the thought patterns.

[1219] Step 3:

[1220] The server proposes successor candidates based on the analysis results. It uses the analysis results of the thought patterns obtained in step 2 as input. As data calculation, it applies an algorithm to evaluate the analysis results and select the optimal successor candidate. As output, it generates a list of proposed successor candidates.

[1221] Step 4:

[1222] The terminal uses a visual display device to visually present information about proposed successor candidates to the user. It receives a list of successor candidates and their detailed information from a server as input. Specifically, it displays the candidates' strengths, weaknesses, and suitability on the display of smart glasses or a tablet device. As output, it provides information that the user can visually confirm.

[1223] Step 5:

[1224] The device uses an emotion engine to analyze the candidate's emotional state in real time and presents the results to the user. The input is real-time emotional data of the candidate. The data processing involves the emotion engine analyzing the data and evaluating the emotional state. The output is a visual display of the emotional state analysis results.

[1225] Step 6:

[1226] The user selects a successor candidate based on information presented through the terminal. The input consists of candidate information and an analysis of their emotional state, both provided by the terminal. The user makes a decision based on the presented information and selects the most suitable successor. The output is the transmission of the selected successor's information to the server.

[1227] Step 7:

[1228] The server assists in obtaining approval for the selected successor. It receives selection results submitted by users as input. As a data processing tool, it organizes information indicating the suitability and abilities of the selected successor and creates materials to support the approval process. As output, it provides the necessary information for approval to the relevant parties.

[1229] (Example 3)

[1230] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1231] Traditional successor selection processes have challenges in accurately evaluating candidates' thought patterns and aptitudes, and they lack an approval process that takes into account the emotional state of approvers, making it difficult to select a suitable successor.

[1232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1233] In this invention, the server includes means for collecting and pre-processing information, means for analyzing thought patterns using artificial intelligence, and means for analyzing the emotional state of approvers using an emotion engine and obtaining approval. This makes it possible to accurately analyze the thought patterns of candidates, propose appropriate successors, and realize an approval process that takes into account the emotional state of approvers.

[1234] "Means of collecting and pre-processing information" refers to the process of collecting data about candidates and preparing it in a format suitable for analysis.

[1235] "Methods for analyzing thought patterns using artificial intelligence" refers to the process of using machine learning techniques to extract thinking tendencies and values ​​from a candidate's past actions and statements.

[1236] "Methods for proposing successor candidates" refers to the process of selecting and presenting appropriate successor candidates based on the analysis results.

[1237] "Means of supporting the selection of proposed successor candidates" refers to a process of evaluating the strengths, weaknesses, and aptitudes of candidates and providing information to select the most suitable successor.

[1238] "A means of obtaining approval by analyzing the emotional state of approvers using an emotion engine" refers to a process that analyzes the emotions of approvers, predicts the likelihood of approval based on the results, and supports the approval process.

[1239] A description of embodiments for carrying out this invention will be given.

[1240] The server first collects and preprocesses data about candidates. This data collection utilizes company databases and publicly available information sources. Preprocessing involves cleaning text data and normalizing numerical data to prepare the data for analysis. This process uses database management systems (DBMS) and data processing software.

[1241] Next, the server uses artificial intelligence to analyze thought patterns. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to extract thinking tendencies and values ​​from the candidate's past actions and statements. This makes it possible to clearly identify the candidate's characteristics.

[1242] The server then proposes successor candidates based on the analysis results. The artificial intelligence selects appropriate candidates by considering similarities to the current leader's thought patterns and new perspectives. The list of proposed candidates is sent to the terminal.

[1243] The device displays detailed information about the proposed successor candidates to the user. Based on the information provided through the device, the user selects the most suitable successor. To assist in the selection process, each candidate's strengths, weaknesses, and suitability are evaluated and displayed.

[1244] Finally, the server uses an emotion engine to analyze the approver's emotional state and obtain approval. Emotion recognition software is used to analyze the approver's emotions and predict the likelihood of approval based on the results. This ensures a smooth approval process.

[1245] As a concrete example, when a user inputs the prompt "Analyze candidate A's past leadership style and values, and evaluate their suitability as a successor by comparing them to the current leader" into the generating AI model, the server analyzes candidate A's data, evaluates their suitability, and displays the results on the terminal. The specific processing flow in Example 3 is explained using Figure 21.

[1246] Step 1:

[1247] The server collects data about candidates. It uses data from company databases and publicly available sources as input. Specifically, it accesses the database via an API to retrieve data on candidates' past behavior, statements, and evaluations. The output is the collected raw data.

[1248] Step 2:

[1249] The server preprocesses the collected data. The input is the raw data collected in step 1. Specifically, it cleans text data (removes unnecessary characters and noise) and normalizes numerical data. This prepares the data for analysis. The output is the preprocessed, clean data.

[1250] Step 3:

[1251] The server analyzes thought patterns using artificial intelligence. The input is the data preprocessed in step 2. Specifically, it trains machine learning models using TensorFlow or PyTorch to extract the thought patterns of the candidates. The output is the analysis results showing the thought patterns of each candidate.

[1252] Step 4:

[1253] The server proposes successor candidates based on the analysis results. The input is the analysis results obtained in step 3. Specifically, it runs an algorithm that selects appropriate candidates by considering similarities to the current leader's thinking patterns and new perspectives. The output is a list of proposed successor candidates.

[1254] Step 5:

[1255] The terminal displays detailed information about the proposed successor candidates to the user. The input is the candidate list generated in step 4. Specifically, it evaluates each candidate's strengths, weaknesses, and suitability, and displays the information in a user-friendly format. The output is the candidate information that the user can view.

[1256] Step 6:

[1257] The server uses an emotion engine to analyze the approver's emotional state and obtain approval. The input is the successor information selected by the user. Specifically, it uses emotion recognition software to analyze the approver's emotions and predict the likelihood of approval. The output is the predicted result regarding the likelihood of approval.

[1258] (Application Example 3)

[1259] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[1260] In factory settings, selecting successors for machinery and equipment managers often relies on subjective judgment, making it difficult to select suitable personnel. Furthermore, failing to consider the emotional state of the approvers during the selection process can hinder its smooth execution.

[1261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1262] In this invention, the server includes means for analyzing human thought patterns, means for proposing successor candidates based on the analysis results, and means for analyzing the emotional state of approvers and predicting the likelihood of approval. This enables objective and efficient selection of successors for managers of machinery and equipment in factories.

[1263] "Human thought patterns" refer to the unique tendencies and processes by which individual people act and make decisions based on their past experiences and values.

[1264] "Analysis results" refer to the data and information obtained after artificial intelligence analyzes human thought patterns.

[1265] A "successor candidate" refers to a person who has the potential to take over a specific role or position.

[1266] "Proposed successor candidates" refers to individuals selected by artificial intelligence and to be evaluated in the next stage.

[1267] An "approver" refers to a person or organization that has the authority to ultimately certify a successor candidate.

[1268] "Emotional state" refers to the psychological reactions and feelings that an approver exhibits in response to a particular situation or information.

[1269] A "machinery manager" refers to the person responsible for the operation and maintenance of machinery and equipment within a factory.

[1270] A "successor selection support system" refers to a system that uses artificial intelligence to support the process from selecting a successor candidate to approving them.

[1271] A server plays a central role in implementing this invention. The server runs a program that uses artificial intelligence to analyze human thought patterns. Specifically, it uses Python and the machine learning library scikit-learn to analyze candidates' past behavior and evaluation data. This makes it possible to cluster candidates' thought patterns and propose successor candidates.

[1272] Furthermore, the server uses Hugging Face's Transformers to analyze the approver's emotional state. This allows it to predict the likelihood that the approver will approve the successor candidate. The results of the emotional analysis provide crucial information for facilitating a smooth approval process.

[1273] The device functions as a smartphone or tablet, receiving information provided by the server. Through the device, the user can review the proposed successor candidates and evaluate the likelihood of approval based on the approver's emotional state.

[1274] As a concrete example, based on candidate A's past work history and evaluation data, the AI ​​proposes candidate A as a successor candidate. Furthermore, an analysis of the approver's emotional state predicts that the approver has favorable feelings towards candidate A, thus indicating a high probability of approval.

[1275] An example of a prompt would be: "Based on Candidate A's past work history and evaluation data, analyze their suitability as a successor candidate. Also, analyze the approver's emotional state and predict the likelihood of approval."

[1276] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1277] Step 1:

[1278] The server collects data on candidates' past behavior and evaluations. The input consists of the candidate's work history and evaluation information. This data is preprocessed, and features are extracted. Specifically, this involves data cleaning and normalization.

[1279] Step 2:

[1280] The server uses scikit-learn to cluster the candidates' thinking patterns based on preprocessed data. The input is the features extracted in step 1. As a data operation, a clustering algorithm is applied to classify the candidates' thinking patterns. The output is the cluster information for each candidate.

[1281] Step 3:

[1282] The server proposes successor candidates based on the clustering results. The input is the cluster information from step 2. As a data processing step, the candidates within the cluster are evaluated, and the most suitable successor candidate is selected. The output is a list of proposed successor candidates.

[1283] Step 4:

[1284] The server uses Hugging Face's Transformers to analyze the approver's emotional state. The input consists of the approver's past statements and behavioral data. The data is then processed by applying an emotion analysis model to evaluate the approver's emotional state. The output is the evaluation result of the approver's emotional state.

[1285] Step 5:

[1286] The server predicts the likelihood that approvers will approve successor candidates based on the sentiment analysis results. The input is the result of the emotional state evaluation in step 4. As a data processing step, the relationship between emotional state and approval is evaluated, and the likelihood of approval is calculated. The output is the predicted likelihood of approval.

[1287] Step 6:

[1288] The terminal presents the user with a list of potential successors provided by the server and a prediction of the likelihood of approval. The input is the output from steps 3 and 5. Specifically, the information is displayed through the user interface for the user to review.

[1289] (Other examples)

[1290] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1291] 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.

[1292] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and 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.

[1293] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1294] 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.

[1295] [Fourth Embodiment]

[1296] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1297] 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.

[1298] 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).

[1299] 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.

[1300] 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.

[1301] 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).

[1302] 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...

Claims

1. Equipped with a processor, The aforementioned processor, The system receives conditions from the user regarding the selection of a successor, and based on these conditions, generates prompt statements using a generative AI model. The aforementioned prompt sentence is input into the generative AI model to analyze the thinking patterns, which are the unique ways of thinking or decision-making tendencies of each candidate. Analyzing the results of the analysis of the aforementioned thought patterns, By comparing the thinking patterns of the current leader with those of the aforementioned candidates and listing the candidates with the highest degree of similarity as the candidates who best fit the aforementioned conditions, we propose successor candidates. Using an emotion engine, we analyze the emotional state of the approver. Based on the emotional state of the approver, the relationship between the emotional state and approval is evaluated to predict the likelihood that the approver will approve the successor candidate. system.

2. The aforementioned processor, Using a generative AI model trained on a dataset of candidates, insights into the successor candidate are provided based on the prompt sentence. The system according to claim 1.

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