System

Abstract

To provide a system according to an embodiment that implements nurture of a successor efficiently and in a long term.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a generation unit; and an education unit. The collection unit is configured to collect data on a successor. The analysis unit is configured to analyze the data collected by the collection unit, and prepare a plan for generating an optimal clone. The generation unit is configured to generate the clone on the basis of the plan prepared by the analysis unit. The education unit is configured to educate and train the clone generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not provide for long-term and efficient training of successors, and there is room for improvement.

[0005] The system according to the embodiment aims to train successors efficiently and over the long term. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an education unit. The collection unit collects data on successors. The analysis unit analyzes the data collected by the collection unit and creates a plan for generating an optimal clone. The generation unit generates a clone based on the plan created by the analysis unit. The education unit educates and trains the clone generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can train successors efficiently and over the long term. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] 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.

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

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

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

[0013] In the following embodiments, the coded 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), and magnetic tapes.

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The successor development system according to the embodiment of the present invention is a system that collects and analyzes data on successors, generates clones, and educates and trains them. This system collects data such as the genetic information, personality, and abilities of the person who will be the successor, and the generation AI analyzes these data to create a plan for generating the optimal clone. A clone is generated based on the generated plan, and the generated clone is further educated and trained by AI. For example, the genetic information of the person who will be the successor is obtained from a DNA sample, and the personality and abilities are collected from psychological tests and performance data. The generation AI analyzes the collected data and determines the characteristics of the optimal clone as a successor. For example, a plan is created to generate a clone with the leadership and management ability of the current CEO. Based on the generated plan, the clone's cells are cultured and grown based on the genetic information. For example, a clone as a successor is generated by culturing and growing the clone's cells based on the genetic information of the current CEO. The generated clone is educated and trained by AI, and is taught the management philosophy and business knowledge of the SoftBank Group and given practical training. For example, a management simulation is performed on the clone to cultivate the ability to make management decisions. This makes it possible for the SoftBank Group to continue forever. By constantly training clones as successors, continuity of management is ensured. For example, even if the current CEO retires, a clone can take over management as a successor. This allows the successor training system to collect and analyze data on successors, generate clones, and educate and train them.

[0029] The successor training system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an education unit. The collection unit collects data such as genetic information, personality, and ability of a person who will be a successor. For example, the collection unit can obtain genetic information using a DNA sample. The collection unit can also collect personality data by conducting a psychological test. Furthermore, the collection unit can analyze performance data to collect ability data. For example, the collection unit obtains a DNA sample of the current CEO, conducts a psychological test to collect personality data, and analyzes performance data to collect ability data. The analysis unit analyzes the data collected by the collection unit and creates a plan for generating an optimal clone. For example, the analysis unit analyzes the genetic information to evaluate genetic compatibility, analyzes the personality data to evaluate the degree of match of personality, and analyzes the ability data to evaluate the degree of match of ability. The analysis unit determines the characteristics of a clone that is optimal as a successor based on these evaluation results. For example, the analysis unit creates a plan for generating a clone that has the leadership and management ability of the current CEO. The generation unit generates a clone based on the plan created by the analysis unit. For example, the generation unit cultures and grows the clone's cells based on the genetic information. The generation unit sets conditions such as culture medium, temperature, and time for cultivating the clone's cells. For example, the generation unit generates a clone as a successor by cultivating and growing the clone's cells based on the genetic information of the current CEO. The education unit educates the generated clone on management philosophy and business knowledge and provides practical training. For example, the education unit conducts a management simulation on the clone to develop the ability to make management decisions. The education unit provides teaching materials and training programs for educating the clone on SoftBank Group's management philosophy and business knowledge. For example, the education unit conducts a management simulation on the clone to develop the ability to make management decisions. In this way, the successor development system according to the embodiment can collect and analyze data on successors, generate clones, and educate and train them.

[0030] The collection department collects data such as genetic information, personality, and abilities of the person who will be the successor. For example, the collection department can obtain genetic information using a DNA sample. Specifically, biological samples such as blood and saliva of the current CEO are collected and analyzed in a specialized laboratory to identify the gene sequence. The collection department can also conduct psychological tests to collect personality data. Psychological tests include personality diagnostic tests, stress tolerance tests, leadership evaluation tests, etc., and these results are stored as digital data. Furthermore, the collection department can analyze performance data to collect ability data. For example, the success rate of past projects, leadership evaluation, and business execution ability are evaluated, and these data are integrated to generate ability data. The collection department centrally manages these diverse data and uses them as basic data for successor training. The latest sensor technology and database management system are used to collect data, improving accuracy and efficiency. For example, the collection department obtains a DNA sample of the current CEO, conducts psychological tests to collect personality data, and analyzes performance data to collect ability data. This allows the collection department to provide a wealth of data for multifaceted evaluation of suitability as a successor.

[0031] The analysis unit analyzes the data collected by the collection unit and creates a plan for generating an optimal clone. For example, the analysis unit analyzes genetic information to evaluate genetic compatibility, analyzes personality data to evaluate personality match, and analyzes ability data to evaluate ability match. Specifically, for analyzing genetic information, next-generation sequencing technology is used to analyze gene sequences in detail to evaluate genetic compatibility. For analyzing personality data, a machine learning algorithm is used to analyze the collected psychological test results and evaluate the match of personality with the current CEO. For analyzing ability data, statistical analysis and data mining technology are used to analyze performance data in detail and evaluate the match of ability as a successor. Based on these evaluation results, the analysis unit determines the characteristics of a clone optimal as a successor. For example, the analysis unit creates a plan for generating a clone with the leadership and management ability of the current CEO. Furthermore, the analysis unit analyzes the interrelationships between genetic information, personality data, and ability data to clarify the conditions necessary for generating an optimal clone. This allows the analysis unit to provide a plan for generating a clone with suitability as a successor with high accuracy.

[0032] The generation unit generates clones based on the plan created by the analysis unit. For example, the generation unit cultures and grows the clone's cells based on the genetic information. Specifically, in order to culture cells based on the genetic information of the current CEO, appropriate conditions such as culture medium, temperature, and time are set. The generation unit sets conditions such as culture medium, temperature, and time for culturing the clone's cells. For example, the generation unit generates a clone as a successor by culturing and growing the clone's cells based on the genetic information of the current CEO. The generation unit monitors the health and growth rate of the cells during the cell culture process and adjusts the culture conditions as necessary. In addition, the generation unit detects genetic abnormalities and health problems that may occur during the growth process of the clone early and takes measures. For example, specific genetic abnormalities can be corrected using gene editing technology. This allows the generation unit to generate clones that are healthy and have excellent abilities. Furthermore, the generation unit appropriately supplies nutrients and hormones required during the growth process of the clone and provides an optimal growth environment. This allows the generation unit to efficiently generate clones as successors with high quality.

[0033] The Ministry of Education educates the clones on management principles and business knowledge, and provides practical training. For example, the Ministry of Education conducts management simulations for the clones to develop their ability to make management decisions. Specifically, the Ministry of Education uses management simulation software to provide the clones with virtual business management scenarios and practically trains them in management decisions and problem-solving skills. The Ministry of Education provides teaching materials and training programs for educating the clones on management principles and business knowledge. For example, the Ministry of Education conducts management simulations for the clones to develop their ability to make management decisions. In addition, the Ministry of Education provides leadership training and communication skill training for the clones to improve their overall management abilities. The Ministry of Education periodically evaluates and monitors the progress of the clones during their education process. For example, the Ministry of Education grasps the clones' growth level and challenges through regular tests and evaluation interviews, and adjusts the education program as necessary. The Ministry of Education also provides the clones with opportunities for internships and practical training to gain actual work experience. This allows the clones to acquire not only theory but also practical skills. Through the education and training of the clones, the Ministry of Education can improve their overall abilities as successors and support the sustainable growth of the company.

[0034] The collection unit can collect data on the genetic information, personality, and abilities of the person who will be the successor. The collection unit, for example, acquires genetic information using a DNA sample. For example, the collection unit acquires a DNA sample of the current CEO and collects genetic information. The collection unit can also collect personality data by conducting a psychological test. For example, the collection unit conducts a psychological test on the current CEO and collects personality data. Furthermore, the collection unit can also analyze performance data to collect ability data. For example, the collection unit analyzes performance data of the current CEO and collects ability data. This makes it possible to generate optimal clones by collecting detailed data on the person who will be the successor. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input a DNA sample into AI and have AI perform analysis of genetic information.

[0035] The analysis unit can analyze the collected data and create a plan for generating a clone. The analysis unit, for example, analyzes genetic information to evaluate genetic compatibility. For example, the analysis unit analyzes the genetic information of the current CEO to evaluate genetic compatibility. The analysis unit can also analyze personality data to evaluate the degree of personality match. For example, the analysis unit analyzes the personality data of the current CEO to evaluate the degree of personality match. Furthermore, the analysis unit can analyze ability data to evaluate the degree of ability match. For example, the analysis unit analyzes the ability data of the current CEO to evaluate the degree of ability match. Based on these evaluation results, the analysis unit determines the characteristics of a clone that is optimal as a successor. For example, the analysis unit creates a plan for generating a clone that has the leadership and management abilities of the current CEO. This makes it possible to generate clones with high accuracy by creating an optimal clone generation plan based on the collected data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the creation of a clone generation plan.

[0036] The generation unit can culture and grow the clone cells based on the genetic information. The generation unit, for example, cultures the clone cells based on the genetic information. For example, the generation unit cultures the clone cells based on the genetic information of the current CEO. The generation unit can also set conditions such as culture medium, temperature, and time for growing the clone cells. For example, the generation unit sets a culture medium for culturing the clone cells and cultures them at an appropriate temperature and time. In this way, the clone cells can be cultured and grown based on the genetic information to generate a clone as a successor. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the genetic information into AI and cause AI to execute the culture of the clone cells.

[0037] The education department can educate the generated clones on the management philosophy and business knowledge and provide practical training. The education department, for example, performs a management simulation on the clones to develop the ability to make management decisions. For example, the education department performs a management simulation on the clones to develop the ability to make management decisions. The education department can also provide teaching materials and training programs for educating the clones on the management philosophy and business knowledge of the SoftBank Group. For example, the education department performs a management simulation on the clones to develop the ability to make management decisions. This allows the generated clones to be educated on the management philosophy and business knowledge and to acquire the ability to be a successor by providing practical training. A part or all of the above-mentioned processing in the education department may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the education department can cause the generation AI to execute a management simulation on the clones.

[0038] The collection unit can analyze the past performance data of the successor candidate and select the optimal data collection method. The collection unit, for example, selects a data collection method based on the success rate of a specific project from the past performance data. For example, the collection unit selects a data collection method based on the success rate of a specific project from the past performance data. The collection unit can also analyze the performance data and customize the data collection method based on a specific skill set. For example, the collection unit analyzes the performance data and customizes the data collection method based on a specific skill set. Furthermore, the collection unit can identify the strengths and weaknesses of the successor candidate based on the performance data and select a data collection method accordingly. For example, the collection unit identifies the strengths and weaknesses of the successor candidate based on the performance data and selects a data collection method accordingly. In this way, the optimal data collection method can be selected by analyzing the past performance data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past performance data into AI and have the AI ​​select the data collection method.

[0039] The collection unit can perform filtering based on the current project or field of interest of the successor candidate when collecting data. For example, the collection unit preferentially collects data related to a project currently in progress. For example, the collection unit preferentially collects data related to a project currently in progress. The collection unit can also filter and collect related data based on the field of interest of the successor candidate. For example, the collection unit filters and collects related data based on the field of interest of the successor candidate. Furthermore, the collection unit can also collect data related to the task that the successor candidate is currently working on. For example, the collection unit collects data related to the task that the successor candidate is currently working on. As a result, highly relevant data can be collected by filtering the data based on the current project or field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the project data of the successor candidate to AI and have the AI ​​perform filtering.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the successor candidate when collecting data. The collection unit, for example, preferentially collects data related to the area where the successor candidate is currently located. For example, the collection unit preferentially collects data related to the area where the successor candidate is currently located. The collection unit can also collect data of areas where the successor candidate is likely to be involved in the future based on the geographical location information. For example, the collection unit collects data of areas where the successor candidate is likely to be involved in the future based on the geographical location information. Furthermore, the collection unit can also preferentially collect data of areas where the successor candidate frequently visits, taking into account the geographical location information. For example, the collection unit preferentially collects data of areas where the successor candidate frequently visits, taking into account the geographical location information. In this way, highly relevant data can be preferentially collected by taking into account the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to AI and cause AI to execute collection of highly relevant data.

[0041] The collection unit can analyze the social media activities of the successor candidates at the time of data collection and collect related data. The collection unit, for example, analyzes the activity content on social media and collects related data. For example, the collection unit analyzes the activity content on social media and collects related data. The collection unit can also collect related data based on followers and interests on social media. For example, the collection unit collects related data based on followers and interests on social media. Furthermore, the collection unit can analyze the content of posts on social media and collect data based on the interests and concerns of the successor candidates. For example, the collection unit analyzes the content of posts on social media and collects data based on the interests and concerns of the successor candidates. In this way, by analyzing social media activities, related data can be efficiently collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to AI and cause AI to execute collection of related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs a concise analysis on data with low importance. Furthermore, the analysis unit can adjust the depth and range of the analysis according to the importance of the data. For example, the analysis unit adjusts the depth and range of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and cause the AI ​​to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a gene analysis algorithm to genetic information. For example, the analysis unit applies a gene analysis algorithm to genetic information. The analysis unit can also apply a psychological analysis algorithm to personality data. For example, the analysis unit applies a psychological analysis algorithm to personality data. The analysis unit can also apply an economic analysis algorithm to performance data. For example, the analysis unit applies an economic analysis algorithm to performance data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause the AI ​​to apply the analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of old data. For example, the analysis unit postpones the analysis of old data. Furthermore, the analysis unit can adjust the order of analysis according to the collection time of the data. For example, the analysis unit adjusts the order of analysis according to the collection time of the data. In this way, the analysis can be prioritized by determining the priority of analysis based on the collection time of the data, so that the latest data can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the collection time of the data to the AI ​​and cause the AI ​​to execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of data with high relevance. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone data with low relevance. For example, the analysis unit postpones data with low relevance. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of data. For example, the analysis unit adjusts the order of analysis according to the relevance of data. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0046] The generation unit can adjust the accuracy of generation based on the level of detail of the genetic information when generating a clone. The generation unit generates a highly accurate clone based on, for example, detailed genetic information. For example, the generation unit generates a highly accurate clone based on detailed genetic information. The generation unit can also generate a standard clone based on simple genetic information. For example, the generation unit generates a standard clone based on simple genetic information. Furthermore, the generation unit can adjust the accuracy of generation according to the level of detail of the genetic information. For example, the generation unit adjusts the accuracy of generation according to the level of detail of the genetic information. As a result, the accuracy of generation can be adjusted according to the level of detail of the genetic information, thereby enabling optimal clone generation. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the level of detail of the genetic information to AI and cause AI to adjust the accuracy of generation.

[0047] The generation unit can apply different generation algorithms according to the category of genetic information when generating a clone. For example, when the genetic information is related to health, the generation unit applies a health-related generation algorithm. For example, when the genetic information is related to health, the generation unit applies a health-related generation algorithm. In addition, when the genetic information is related to intelligence, the generation unit can also apply an intelligence-related generation algorithm. For example, when the genetic information is related to intelligence, the generation unit applies an intelligence-related generation algorithm. In addition, when the genetic information is related to personality, the generation unit can also apply a personality-related generation algorithm. For example, when the genetic information is related to personality, the generation unit applies a personality-related generation algorithm. This makes it possible to generate clones with high accuracy by applying an appropriate generation algorithm according to the category of genetic information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the category of genetic information to AI and cause AI to execute application of the generation algorithm.

[0048] The generating unit can determine the priority of generation based on the time of collection of genetic information when generating a clone. The generating unit, for example, prioritizes clone generation based on the latest genetic information. For example, the generating unit prioritizes clone generation based on the latest genetic information. The generating unit can also postpone old genetic information. For example, the generating unit postpones old genetic information. Furthermore, the generating unit can adjust the priority of generation according to the time of collection of genetic information. For example, the generating unit adjusts the priority of generation according to the time of collection of genetic information. As a result, by determining the priority of generation based on the time of collection of genetic information, it becomes possible to generate a clone based on the latest genetic information. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the time of collection of genetic information to AI and cause AI to execute the determination of the priority of generation.

[0049] The generation unit can adjust the order of generation based on the relevance of the genetic information when generating clones. The generation unit, for example, prioritizes clone generation based on genetic information with high relevance. For example, the generation unit prioritizes clone generation based on genetic information with high relevance. The generation unit can also postpone genetic information with low relevance. For example, the generation unit postpones genetic information with low relevance. Furthermore, the generation unit can also adjust the order of generation according to the relevance of the genetic information. For example, the generation unit adjusts the order of generation according to the relevance of the genetic information. As a result, efficient clone generation is possible by adjusting the order of generation based on the relevance of the genetic information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the genetic information to AI and cause AI to adjust the order of generation.

[0050] The education department can adjust the level of detail of the education based on the ability of the clone during education. For example, the education department provides detailed education to a clone with high ability. For example, the education department provides detailed education to a clone with high ability. In addition, the education department can also provide basic education to a clone with standard ability. For example, the education department provides basic education to a clone with standard ability. Furthermore, the education department can adjust the level of detail of the education according to the ability of the clone. For example, the education department adjusts the level of detail of the education according to the ability of the clone. As a result, efficient education is possible by adjusting the level of detail of the education according to the ability of the clone. Part or all of the above-mentioned processing in the education department may be performed, for example, using AI, or may be performed without using AI. For example, the education department can input the ability data of the clone into AI and cause AI to adjust the level of detail of the education.

[0051] The education department can apply different education algorithms depending on the category of the clone during education. For example, the education department applies a business education algorithm to a clone specialized in management. For example, the education department applies a business education algorithm to a clone specialized in management. The education department can also apply a technical education algorithm to a clone specialized in technology. For example, the education department applies a technical education algorithm to a clone specialized in technology. Furthermore, the education department can also select an appropriate education algorithm depending on the category of the clone. For example, the education department selects an appropriate education algorithm depending on the category of the clone. As a result, highly accurate education can be achieved by applying an appropriate education algorithm depending on the category of the clone. Part or all of the above-mentioned processing in the education department may be performed using, for example, AI, or may be performed without using AI. For example, the education department can input the category data of the clone into AI and cause the AI ​​to apply the education algorithm.

[0052] The education department can determine the priority of education based on the growth period of the clone during education. For example, when the clone is in its childhood, the education department prioritizes basic education. For example, when the clone is in its childhood, the education department prioritizes basic education. In addition, the education department can also prioritize specialized education when the clone is in its adolescence. For example, when the clone is in its adolescence, the education department prioritizes specialized education. Furthermore, the education department can adjust the priority of education according to the growth period of the clone. For example, the education department adjusts the priority of education according to the growth period of the clone. As a result, efficient education is possible by determining the priority of education based on the growth period of the clone. Part or all of the above-mentioned processing in the education department may be performed, for example, using AI, or may be performed without using AI. For example, the education department can input the growth period data of the clone into AI and have the AI ​​execute the determination of the priority of education.

[0053] The education department can adjust the order of education based on the relevance of the clones during education. For example, the education department provides educational content with high relevance with priority. For example, the education department provides educational content with high relevance with priority. The education department can also postpone educational content with low relevance. For example, the education department postpones educational content with low relevance. Furthermore, the education department can adjust the order of education according to the relevance of the educational content. For example, the education department adjusts the order of education according to the relevance of the educational content. As a result, efficient education is possible by adjusting the order of education based on the relevance of the clones. A part or all of the above-mentioned processing in the education department may be performed using, for example, AI, or may be performed without using AI. For example, the education department can input relevance data of the educational content to AI and cause AI to adjust the order of education.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The successor training system further includes a feedback unit. The feedback unit can evaluate the performance of the generated clones and provide feedback to the education department. For example, the feedback unit can evaluate the results of a management simulation performed by the clones and suggest improvements to the education department. The feedback unit can also monitor the work performance of the clones and notify the education department whether additional training is required. Furthermore, the feedback unit can track the growth process of the clones and suggest adjustments to the education program to the education department. This makes it possible to continuously evaluate the performance of the clones through the feedback unit and for the education department to provide appropriate education and training.

[0056] The collection unit can also collect health data of the successor candidates. For example, the collection unit can obtain the medical records of the successor candidates and evaluate their health conditions. The collection unit can also collect the results of regular health checkups and evaluate the health risks of the successor candidates. Furthermore, the collection unit can collect lifestyle data of the successor candidates and provide advice for maintaining their health. This makes it possible to grasp the health conditions of the successor candidates and create optimal clone generation and education plans.

[0057] The generation unit can adjust the conditions for generation in consideration of the environmental data when generating clones. For example, the generation unit monitors environmental data such as temperature and humidity during cell culture of the clones to maintain optimal conditions. The generation unit can also adjust the components of the culture medium based on the environmental data. Furthermore, the generation unit can adjust the growth rate of the clones based on the environmental data to provide an optimal growth environment. This makes it possible to improve the accuracy of clone generation by taking the environmental data into consideration.

[0058] The collection unit can customize data collection based on the hobbies and interests of the successor candidates. For example, the collection unit preferentially collects data related to fields in which the successor candidates are interested. The collection unit can also collect data related to the hobbies of the successor candidates to create individual education plans. Furthermore, the collection unit can collect the latest information on related industries based on the interests of the successor candidates. This allows for more effective education and training by customizing data collection based on the hobbies and interests of the successor candidates.

[0059] The generation unit can adjust the generation method in consideration of the diversity of genetic information when generating clones. For example, the generation unit generates clones by combining different genetic information in order to ensure the diversity of genetic information. The generation unit can also set different culture conditions based on the diversity of genetic information. Furthermore, the generation unit can adjust the growth rate of the clones in consideration of the diversity of genetic information. This makes it possible to generate healthier and more diverse clones by considering the diversity of genetic information.

[0060] The collection unit can collect network data of the successor candidates and provide it to the analysis unit. For example, the collection unit collects business network and personal connection data of the successor candidates. The collection unit can also collect social network data of the successor candidates and provide it to the analysis unit. Furthermore, the collection unit can collect online activity data of the successor candidates and provide it to the analysis unit. In this way, by collecting network data of the successor candidates, more comprehensive data analysis is possible.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection department collects data on the potential successor, such as genetic information, personality, and abilities. For example, the collection department may use a DNA sample to obtain genetic information, conduct a psychological test to collect personality data, and analyze performance data to collect ability data. Step 2: The analysis unit analyzes the data collected by the collection unit and creates a plan for generating optimal clones. For example, the analysis unit analyzes genetic information to evaluate genetic compatibility, analyzes personality data to evaluate personality match, and analyzes ability data to evaluate ability match. Based on these evaluation results, the characteristics of the optimal clone as a successor are determined. Step 3: The generation unit generates clones based on the plan created by the analysis unit. For example, the generation unit cultures and grows the clone cells based on the genetic information. The generation unit sets the conditions for culturing the clone cells, such as the culture medium, temperature, and time. Step 4: The Ministry of Education educates the clones on business principles and business knowledge, and provides practical training. For example, the Ministry of Education may conduct business simulations on the clones to develop their business decision-making abilities. The Ministry of Education may provide the clones with teaching materials and training programs to teach them business principles and business knowledge.

[0063] (Example 2) The successor development system according to the embodiment of the present invention is a system that collects and analyzes data on successors, generates clones, and educates and trains them. This system collects data such as the genetic information, personality, and abilities of the person who will be the successor, and the generation AI analyzes these data to create a plan for generating the optimal clone. A clone is generated based on the generated plan, and the generated clone is further educated and trained by AI. For example, the genetic information of the person who will be the successor is obtained from a DNA sample, and the personality and abilities are collected from psychological tests and performance data. The generation AI analyzes the collected data and determines the characteristics of the optimal clone as a successor. For example, a plan is created to generate a clone with the leadership and management ability of the current CEO. Based on the generated plan, the clone's cells are cultured and grown based on the genetic information. For example, a clone as a successor is generated by culturing and growing the clone's cells based on the genetic information of the current CEO. The generated clone is educated and trained by AI, and is taught the management philosophy and business knowledge of the SoftBank Group and given practical training. For example, a management simulation is performed on the clone to cultivate the ability to make management decisions. This makes it possible for the SoftBank Group to continue forever. By constantly training clones as successors, continuity of management is ensured. For example, even if the current CEO retires, a clone can take over management as a successor. This allows the successor training system to collect and analyze data on successors, generate clones, and educate and train them.

[0064] The successor training system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an education unit. The collection unit collects data such as genetic information, personality, and ability of a person who will be a successor. For example, the collection unit can obtain genetic information using a DNA sample. The collection unit can also collect personality data by conducting a psychological test. Furthermore, the collection unit can analyze performance data to collect ability data. For example, the collection unit obtains a DNA sample of the current CEO, conducts a psychological test to collect personality data, and analyzes performance data to collect ability data. The analysis unit analyzes the data collected by the collection unit and creates a plan for generating an optimal clone. For example, the analysis unit analyzes the genetic information to evaluate genetic compatibility, analyzes the personality data to evaluate the degree of match of personality, and analyzes the ability data to evaluate the degree of match of ability. The analysis unit determines the characteristics of a clone that is optimal as a successor based on these evaluation results. For example, the analysis unit creates a plan for generating a clone that has the leadership and management ability of the current CEO. The generation unit generates a clone based on the plan created by the analysis unit. For example, the generation unit cultures and grows the clone's cells based on the genetic information. The generation unit sets conditions such as culture medium, temperature, and time for cultivating the clone's cells. For example, the generation unit generates a clone as a successor by cultivating and growing the clone's cells based on the genetic information of the current CEO. The education unit educates the generated clone on management philosophy and business knowledge and provides practical training. For example, the education unit conducts a management simulation on the clone to develop the ability to make management decisions. The education unit provides teaching materials and training programs for educating the clone on SoftBank Group's management philosophy and business knowledge. For example, the education unit conducts a management simulation on the clone to develop the ability to make management decisions. In this way, the successor development system according to the embodiment can collect and analyze data on successors, generate clones, and educate and train them.

[0065] The collection department collects data such as genetic information, personality, and abilities of the person who will be the successor. For example, the collection department can obtain genetic information using a DNA sample. Specifically, biological samples such as blood and saliva of the current CEO are collected and analyzed in a specialized laboratory to identify the gene sequence. The collection department can also conduct psychological tests to collect personality data. Psychological tests include personality diagnostic tests, stress tolerance tests, leadership evaluation tests, etc., and these results are stored as digital data. Furthermore, the collection department can analyze performance data to collect ability data. For example, the success rate of past projects, leadership evaluation, and business execution ability are evaluated, and these data are integrated to generate ability data. The collection department centrally manages these diverse data and uses them as basic data for successor training. The latest sensor technology and database management system are used to collect data, improving accuracy and efficiency. For example, the collection department obtains a DNA sample of the current CEO, conducts psychological tests to collect personality data, and analyzes performance data to collect ability data. This allows the collection department to provide a wealth of data for multifaceted evaluation of suitability as a successor.

[0066] The analysis unit analyzes the data collected by the collection unit and creates a plan for generating an optimal clone. For example, the analysis unit analyzes genetic information to evaluate genetic compatibility, analyzes personality data to evaluate personality match, and analyzes ability data to evaluate ability match. Specifically, for analyzing genetic information, next-generation sequencing technology is used to analyze gene sequences in detail to evaluate genetic compatibility. For analyzing personality data, a machine learning algorithm is used to analyze the collected psychological test results and evaluate the match of personality with the current CEO. For analyzing ability data, statistical analysis and data mining technology are used to analyze performance data in detail and evaluate the match of ability as a successor. Based on these evaluation results, the analysis unit determines the characteristics of a clone optimal as a successor. For example, the analysis unit creates a plan for generating a clone with the leadership and management ability of the current CEO. Furthermore, the analysis unit analyzes the interrelationships between genetic information, personality data, and ability data to clarify the conditions necessary for generating an optimal clone. This allows the analysis unit to provide a plan for generating a clone with suitability as a successor with high accuracy.

[0067] The generation unit generates clones based on the plan created by the analysis unit. For example, the generation unit cultures and grows the clone's cells based on the genetic information. Specifically, in order to culture cells based on the genetic information of the current CEO, appropriate conditions such as culture medium, temperature, and time are set. The generation unit sets conditions such as culture medium, temperature, and time for culturing the clone's cells. For example, the generation unit generates a clone as a successor by culturing and growing the clone's cells based on the genetic information of the current CEO. The generation unit monitors the health and growth rate of the cells during the cell culture process and adjusts the culture conditions as necessary. In addition, the generation unit detects genetic abnormalities and health problems that may occur during the growth process of the clone early and takes measures. For example, specific genetic abnormalities can be corrected using gene editing technology. This allows the generation unit to generate clones that are healthy and have excellent abilities. Furthermore, the generation unit appropriately supplies nutrients and hormones required during the growth process of the clone and provides an optimal growth environment. This allows the generation unit to efficiently generate clones as successors with high quality.

[0068] The Ministry of Education educates the clones on management principles and business knowledge, and provides practical training. For example, the Ministry of Education conducts management simulations for the clones to develop their ability to make management decisions. Specifically, the Ministry of Education uses management simulation software to provide the clones with virtual business management scenarios and practically trains them in management decisions and problem-solving skills. The Ministry of Education provides teaching materials and training programs for educating the clones on management principles and business knowledge. For example, the Ministry of Education conducts management simulations for the clones to develop their ability to make management decisions. In addition, the Ministry of Education provides leadership training and communication skill training for the clones to improve their overall management abilities. The Ministry of Education periodically evaluates and monitors the progress of the clones during their education process. For example, the Ministry of Education grasps the clones' growth level and challenges through regular tests and evaluation interviews, and adjusts the education program as necessary. The Ministry of Education also provides the clones with opportunities for internships and practical training to gain actual work experience. This allows the clones to acquire not only theory but also practical skills. Through the education and training of the clones, the Ministry of Education can improve their overall abilities as successors and support the sustainable growth of the company.

[0069] The collection unit can collect data on the genetic information, personality, and abilities of the person who will be the successor. The collection unit, for example, acquires genetic information using a DNA sample. For example, the collection unit acquires a DNA sample of the current CEO and collects genetic information. The collection unit can also collect personality data by conducting a psychological test. For example, the collection unit conducts a psychological test on the current CEO and collects personality data. Furthermore, the collection unit can also analyze performance data to collect ability data. For example, the collection unit analyzes performance data of the current CEO and collects ability data. This makes it possible to generate optimal clones by collecting detailed data on the person who will be the successor. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input a DNA sample into AI and have AI perform analysis of genetic information.

[0070] The analysis unit can analyze the collected data and create a plan for generating a clone. The analysis unit, for example, analyzes genetic information to evaluate genetic compatibility. For example, the analysis unit analyzes the genetic information of the current CEO to evaluate genetic compatibility. The analysis unit can also analyze personality data to evaluate the degree of personality match. For example, the analysis unit analyzes the personality data of the current CEO to evaluate the degree of personality match. Furthermore, the analysis unit can analyze ability data to evaluate the degree of ability match. For example, the analysis unit analyzes the ability data of the current CEO to evaluate the degree of ability match. Based on these evaluation results, the analysis unit determines the characteristics of a clone that is optimal as a successor. For example, the analysis unit creates a plan for generating a clone that has the leadership and management abilities of the current CEO. This makes it possible to generate clones with high accuracy by creating an optimal clone generation plan based on the collected data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the creation of a clone generation plan.

[0071] The generation unit can culture and grow the clone cells based on the genetic information. The generation unit, for example, cultures the clone cells based on the genetic information. For example, the generation unit cultures the clone cells based on the genetic information of the current CEO. The generation unit can also set conditions such as culture medium, temperature, and time for growing the clone cells. For example, the generation unit sets a culture medium for culturing the clone cells and cultures them at an appropriate temperature and time. In this way, the clone cells can be cultured and grown based on the genetic information to generate a clone as a successor. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the genetic information into AI and cause AI to execute the culture of the clone cells.

[0072] The education department can educate the generated clones on the management philosophy and business knowledge and provide practical training. The education department, for example, performs a management simulation on the clones to develop the ability to make management decisions. For example, the education department performs a management simulation on the clones to develop the ability to make management decisions. The education department can also provide teaching materials and training programs for educating the clones on the management philosophy and business knowledge of the SoftBank Group. For example, the education department performs a management simulation on the clones to develop the ability to make management decisions. This allows the generated clones to be educated on the management philosophy and business knowledge and to acquire the ability to be a successor by providing practical training. A part or all of the above-mentioned processing in the education department may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the education department can cause the generation AI to execute a management simulation on the clones.

[0073] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the user's emotion. For example, when the user is relaxed, the collection unit immediately starts data collection. For example, when the user is relaxed, the collection unit immediately starts data collection. In addition, when the user is stressed, the collection unit can postpone data collection and wait until the user is relaxed. For example, when the user is stressed, the collection unit postpones data collection and waits until the user is relaxed. Furthermore, when the user is concentrating, the collection unit can collect data at that timing and efficiently acquire information. For example, when the user is concentrating, the collection unit collects data at that timing and efficiently acquire information. As a result, efficient data collection is possible by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0074] The collection unit can analyze the past performance data of the successor candidate and select the optimal data collection method. The collection unit, for example, selects a data collection method based on the success rate of a specific project from the past performance data. For example, the collection unit selects a data collection method based on the success rate of a specific project from the past performance data. The collection unit can also analyze the performance data and customize the data collection method based on a specific skill set. For example, the collection unit analyzes the performance data and customizes the data collection method based on a specific skill set. Furthermore, the collection unit can identify the strengths and weaknesses of the successor candidate based on the performance data and select a data collection method accordingly. For example, the collection unit identifies the strengths and weaknesses of the successor candidate based on the performance data and selects a data collection method accordingly. In this way, the optimal data collection method can be selected by analyzing the past performance data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past performance data into AI and have the AI ​​select the data collection method.

[0075] The collection unit can perform filtering based on the current project or field of interest of the successor candidate when collecting data. For example, the collection unit preferentially collects data related to a project currently in progress. For example, the collection unit preferentially collects data related to a project currently in progress. The collection unit can also filter and collect related data based on the field of interest of the successor candidate. For example, the collection unit filters and collects related data based on the field of interest of the successor candidate. Furthermore, the collection unit can also collect data related to the task that the successor candidate is currently working on. For example, the collection unit collects data related to the task that the successor candidate is currently working on. As a result, highly relevant data can be collected by filtering the data based on the current project or field of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the project data of the successor candidate to AI and have the AI ​​perform filtering.

[0076] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the user's emotion. For example, when the user is relaxed, the collection unit preferentially collects data with high importance. For example, when the user is relaxed, the collection unit preferentially collects data with high importance. In addition, when the user is stressed, the collection unit can also postpone data with low importance. For example, when the user is stressed, the collection unit postpones data with low importance. In addition, when the user is concentrating, the collection unit can also preferentially collect the most important data. For example, when the user is concentrating, the collection unit preferentially collects the most important data. In this way, important data can be preferentially collected by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0077] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the successor candidate when collecting data. The collection unit, for example, preferentially collects data related to the area where the successor candidate is currently located. For example, the collection unit preferentially collects data related to the area where the successor candidate is currently located. The collection unit can also collect data of areas where the successor candidate is likely to be involved in the future based on the geographical location information. For example, the collection unit collects data of areas where the successor candidate is likely to be involved in the future based on the geographical location information. Furthermore, the collection unit can also preferentially collect data of areas where the successor candidate frequently visits, taking into account the geographical location information. For example, the collection unit preferentially collects data of areas where the successor candidate frequently visits, taking into account the geographical location information. In this way, highly relevant data can be preferentially collected by taking into account the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to AI and cause AI to execute collection of highly relevant data.

[0078] The collection unit can analyze the social media activities of the successor candidates at the time of data collection and collect related data. The collection unit, for example, analyzes the activity content on social media and collects related data. For example, the collection unit analyzes the activity content on social media and collects related data. The collection unit can also collect related data based on followers and interests on social media. For example, the collection unit collects related data based on followers and interests on social media. Furthermore, the collection unit can analyze the content of posts on social media and collect data based on the interests and concerns of the successor candidates. For example, the collection unit analyzes the content of posts on social media and collects data based on the interests and concerns of the successor candidates. In this way, by analyzing social media activities, related data can be efficiently collected. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to AI and cause AI to execute collection of related data.

[0079] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the user's emotion. The analysis unit provides a detailed analysis result when the user is relaxed, for example. For example, the analysis unit provides a detailed analysis result when the user is relaxed. In addition, the analysis unit can also provide a concise and to the point analysis result when the user is stressed. For example, the analysis unit provides a concise and to the point analysis result when the user is stressed. In addition, the analysis unit can also provide an analysis result including deep insight when the user is concentrated. For example, the analysis unit provides an analysis result including deep insight when the user is concentrated. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. For example, the analysis unit performs a concise analysis on data with low importance. Furthermore, the analysis unit can adjust the depth and range of the analysis according to the importance of the data. For example, the analysis unit adjusts the depth and range of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and cause the AI ​​to adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a gene analysis algorithm to genetic information. For example, the analysis unit applies a gene analysis algorithm to genetic information. The analysis unit can also apply a psychological analysis algorithm to personality data. For example, the analysis unit applies a psychological analysis algorithm to personality data. The analysis unit can also apply an economic analysis algorithm to performance data. For example, the analysis unit applies an economic analysis algorithm to performance data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause the AI ​​to apply the analysis algorithm.

[0082] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the user's emotion. The analysis unit, for example, performs a detailed analysis when the user is relaxed. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is stressed. For example, the analysis unit performs a concise analysis when the user is stressed. Furthermore, the analysis unit can also perform an appropriate length of analysis when the user is concentrating. For example, the analysis unit performs an appropriate length of analysis when the user is concentrating. In this way, by adjusting the length of the analysis according to the user's emotion, an analysis result of an appropriate length for the user can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0083] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit prioritizes the analysis of the latest data. The analysis unit can also postpone the analysis of old data. For example, the analysis unit postpones the analysis of old data. Furthermore, the analysis unit can adjust the order of analysis according to the collection time of the data. For example, the analysis unit adjusts the order of analysis according to the collection time of the data. In this way, the analysis can be prioritized by determining the priority of analysis based on the collection time of the data, so that the latest data can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the collection time of the data to the AI ​​and cause the AI ​​to execute the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of data with high relevance. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone data with low relevance. For example, the analysis unit postpones data with low relevance. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of data. For example, the analysis unit adjusts the order of analysis according to the relevance of data. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0085] The generation unit can estimate the user's emotion and adjust the clone generation method based on the user's emotion. The generation unit applies a normal clone generation method when the user is relaxed, for example. For example, the generation unit applies a normal clone generation method when the user is relaxed. The generation unit can also simplify the clone generation process when the user is stressed. For example, the generation unit simplifies the clone generation process when the user is stressed. Furthermore, the generation unit can also apply a detailed clone generation method when the user is concentrating. For example, the generation unit applies a detailed clone generation method when the user is concentrating. This enables efficient clone generation by adjusting the clone generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can adjust the accuracy of generation based on the level of detail of the genetic information when generating a clone. The generation unit generates a highly accurate clone based on, for example, detailed genetic information. For example, the generation unit generates a highly accurate clone based on detailed genetic information. The generation unit can also generate a standard clone based on simple genetic information. For example, the generation unit generates a standard clone based on simple genetic information. Furthermore, the generation unit can adjust the accuracy of generation according to the level of detail of the genetic information. For example, the generation unit adjusts the accuracy of generation according to the level of detail of the genetic information. As a result, the accuracy of generation can be adjusted according to the level of detail of the genetic information, thereby enabling optimal clone generation. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the level of detail of the genetic information to AI and cause AI to adjust the accuracy of generation.

[0087] The generation unit can apply different generation algorithms according to the category of genetic information when generating a clone. For example, when the genetic information is related to health, the generation unit applies a health-related generation algorithm. For example, when the genetic information is related to health, the generation unit applies a health-related generation algorithm. In addition, when the genetic information is related to intelligence, the generation unit can also apply an intelligence-related generation algorithm. For example, when the genetic information is related to intelligence, the generation unit applies an intelligence-related generation algorithm. In addition, when the genetic information is related to personality, the generation unit can also apply a personality-related generation algorithm. For example, when the genetic information is related to personality, the generation unit applies a personality-related generation algorithm. This makes it possible to generate clones with high accuracy by applying an appropriate generation algorithm according to the category of genetic information. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the category of genetic information to AI and cause AI to execute application of the generation algorithm.

[0088] The generation unit can estimate the user's emotion and determine the priority of clone generation based on the user's emotion. For example, when the user is relaxed, the generation unit prioritizes important clone generation. For example, when the user is relaxed, the generation unit prioritizes important clone generation. In addition, when the user is stressed, the generation unit can also postpone less important clone generation. For example, when the user is stressed, the generation unit postpones less important clone generation. In addition, when the user is concentrating, the generation unit can also prioritize the most important clone generation. For example, when the user is concentrating, the generation unit prioritizes the most important clone generation. This enables efficient clone generation by determining the priority of clone generation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0089] The generating unit can determine the priority of generation based on the time of collection of genetic information when generating a clone. The generating unit, for example, prioritizes clone generation based on the latest genetic information. For example, the generating unit prioritizes clone generation based on the latest genetic information. The generating unit can also postpone old genetic information. For example, the generating unit postpones old genetic information. Furthermore, the generating unit can adjust the priority of generation according to the time of collection of genetic information. For example, the generating unit adjusts the priority of generation according to the time of collection of genetic information. As a result, by determining the priority of generation based on the time of collection of genetic information, it becomes possible to generate a clone based on the latest genetic information. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the time of collection of genetic information to AI and cause AI to execute the determination of the priority of generation.

[0090] The generation unit can adjust the order of generation based on the relevance of the genetic information when generating clones. The generation unit, for example, prioritizes clone generation based on genetic information with high relevance. For example, the generation unit prioritizes clone generation based on genetic information with high relevance. The generation unit can also postpone genetic information with low relevance. For example, the generation unit postpones genetic information with low relevance. Furthermore, the generation unit can also adjust the order of generation according to the relevance of the genetic information. For example, the generation unit adjusts the order of generation according to the relevance of the genetic information. As a result, efficient clone generation is possible by adjusting the order of generation based on the relevance of the genetic information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the genetic information to AI and cause AI to adjust the order of generation.

[0091] The education unit can estimate the user's emotion and adjust the way of expressing education based on the user's emotion. The education unit, for example, provides detailed educational content when the user is relaxed. For example, the education unit provides detailed educational content when the user is relaxed. In addition, the education unit can also provide brief and to the point educational content when the user is stressed. For example, the education unit provides brief and to the point educational content when the user is stressed. In addition, the education unit can also provide educational content including deep insight when the user is focused. For example, the education unit provides educational content including deep insight when the user is focused. In this way, by adjusting the way of expressing education according to the user's emotion, it is possible to provide educational content that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the education unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the education department can input a user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The education department can adjust the level of detail of the education based on the ability of the clone during education. For example, the education department provides detailed education to a clone with high ability. For example, the education department provides detailed education to a clone with high ability. In addition, the education department can also provide basic education to a clone with standard ability. For example, the education department provides basic education to a clone with standard ability. Furthermore, the education department can adjust the level of detail of the education according to the ability of the clone. For example, the education department adjusts the level of detail of the education according to the ability of the clone. As a result, efficient education is possible by adjusting the level of detail of the education according to the ability of the clone. Part or all of the above-mentioned processing in the education department may be performed, for example, using AI, or may be performed without using AI. For example, the education department can input the ability data of the clone into AI and cause AI to adjust the level of detail of the education.

[0093] The education department can apply different education algorithms depending on the category of the clone during education. For example, the education department applies a business education algorithm to a clone specialized in management. For example, the education department applies a business education algorithm to a clone specialized in management. The education department can also apply a technical education algorithm to a clone specialized in technology. For example, the education department applies a technical education algorithm to a clone specialized in technology. Furthermore, the education department can also select an appropriate education algorithm depending on the category of the clone. For example, the education department selects an appropriate education algorithm depending on the category of the clone. As a result, highly accurate education can be achieved by applying an appropriate education algorithm depending on the category of the clone. Part or all of the above-mentioned processing in the education department may be performed using, for example, AI, or may be performed without using AI. For example, the education department can input the category data of the clone into AI and cause the AI ​​to apply the education algorithm.

[0094] The education unit can estimate the user's emotion and adjust the length of the education based on the user's emotion. The education unit, for example, provides detailed education when the user is relaxed. For example, the education unit provides detailed education when the user is relaxed. The education unit can also provide brief education when the user is stressed. For example, the education unit provides brief education when the user is stressed. Furthermore, the education unit can also provide education of a moderate length when the user is concentrated. For example, the education unit provides education of a moderate length when the user is concentrated. In this way, by adjusting the length of education according to the user's emotion, it is possible to provide educational content of an appropriate length for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the education unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the education unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] The education department can determine the priority of education based on the growth period of the clone during education. For example, when the clone is in its childhood, the education department prioritizes basic education. For example, when the clone is in its childhood, the education department prioritizes basic education. In addition, the education department can also prioritize specialized education when the clone is in its adolescence. For example, when the clone is in its adolescence, the education department prioritizes specialized education. Furthermore, the education department can adjust the priority of education according to the growth period of the clone. For example, the education department adjusts the priority of education according to the growth period of the clone. As a result, efficient education is possible by determining the priority of education based on the growth period of the clone. Part or all of the above-mentioned processing in the education department may be performed, for example, using AI, or may be performed without using AI. For example, the education department can input the growth period data of the clone into AI and have the AI ​​execute the determination of the priority of education.

[0096] The education department can adjust the order of education based on the relevance of the clones during education. For example, the education department provides educational content with high relevance with priority. For example, the education department provides educational content with high relevance with priority. The education department can also postpone educational content with low relevance. For example, the education department postpones educational content with low relevance. Furthermore, the education department can adjust the order of education according to the relevance of the educational content. For example, the education department adjusts the order of education according to the relevance of the educational content. As a result, efficient education is possible by adjusting the order of education based on the relevance of the clones. A part or all of the above-mentioned processing in the education department may be performed using, for example, AI, or may be performed without using AI. For example, the education department can input relevance data of the educational content to AI and cause AI to adjust the order of education.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The successor training system further includes a feedback unit. The feedback unit can evaluate the performance of the generated clones and provide feedback to the education department. For example, the feedback unit can evaluate the results of a management simulation performed by the clones and suggest improvements to the education department. The feedback unit can also monitor the work performance of the clones and notify the education department whether additional training is required. Furthermore, the feedback unit can track the growth process of the clones and suggest adjustments to the education program to the education department. This makes it possible to continuously evaluate the performance of the clones through the feedback unit and for the education department to provide appropriate education and training.

[0099] The collection unit can also collect health data of the successor candidates. For example, the collection unit can obtain the medical records of the successor candidates and evaluate their health conditions. The collection unit can also collect the results of regular health checkups and evaluate the health risks of the successor candidates. Furthermore, the collection unit can collect lifestyle data of the successor candidates and provide advice for maintaining their health. This makes it possible to grasp the health conditions of the successor candidates and create optimal clone generation and education plans.

[0100] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the user's emotions. For example, when the user is relaxed, the analysis unit prioritizes the analysis of important data. In addition, when the user is stressed, the analysis unit can postpone the analysis of less important data. Furthermore, when the user is concentrating, the analysis unit can prioritize the analysis of the most important data. This enables efficient data analysis by determining the priority of analysis according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0101] The generation unit can adjust the conditions for generation in consideration of the environmental data when generating clones. For example, the generation unit monitors environmental data such as temperature and humidity during cell culture of the clones to maintain optimal conditions. The generation unit can also adjust the components of the culture medium based on the environmental data. Furthermore, the generation unit can adjust the growth rate of the clones based on the environmental data to provide an optimal growth environment. This makes it possible to improve the accuracy of clone generation by taking the environmental data into consideration.

[0102] The education unit can estimate the user's emotions and adjust the timing of education based on the user's emotions. For example, if the user is relaxed, the education unit immediately starts education. Also, if the user is feeling stressed, the education unit can postpone education and wait until the user is relaxed. Furthermore, if the user is concentrating, the education unit can provide education at that timing and efficiently provide knowledge. This enables effective education by adjusting the timing of education according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the education unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the education unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The collection unit can customize data collection based on the hobbies and interests of the successor candidates. For example, the collection unit preferentially collects data related to fields in which the successor candidates are interested. The collection unit can also collect data related to the hobbies of the successor candidates to create individual education plans. Furthermore, the collection unit can collect the latest information on related industries based on the interests of the successor candidates. This allows for more effective education and training by customizing data collection based on the hobbies and interests of the successor candidates.

[0104] The analysis unit can estimate the user's emotions and adjust the way the analysis is expressed based on the user's emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. In addition, the analysis unit can provide concise and to the point analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results including deep insights when the user is focused. This allows the analysis results to be provided that are easy for the user to understand by adjusting the way the analysis is expressed according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0105] The generation unit can adjust the generation method in consideration of the diversity of genetic information when generating clones. For example, the generation unit generates clones by combining different genetic information in order to ensure the diversity of genetic information. The generation unit can also set different culture conditions based on the diversity of genetic information. Furthermore, the generation unit can adjust the growth rate of the clones in consideration of the diversity of genetic information. This makes it possible to generate healthier and more diverse clones by considering the diversity of genetic information.

[0106] The education unit can estimate the user's emotions and adjust the educational content based on the user's emotions. For example, the education unit provides content with a high level of difficulty when the user is relaxed. In addition, the education unit can also provide basic content when the user is stressed. Furthermore, the education unit can also provide applied content when the user is concentrating. This allows effective learning by adjusting the educational content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the education unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the education unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0107] The collection unit can collect network data of the successor candidates and provide it to the analysis unit. For example, the collection unit collects business network and personal connection data of the successor candidates. The collection unit can also collect social network data of the successor candidates and provide it to the analysis unit. Furthermore, the collection unit can collect online activity data of the successor candidates and provide it to the analysis unit. In this way, by collecting network data of the successor candidates, more comprehensive data analysis is possible.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection department collects data on the potential successor, such as genetic information, personality, and abilities. For example, the collection department may use a DNA sample to obtain genetic information, conduct a psychological test to collect personality data, and analyze performance data to collect ability data. Step 2: The analysis unit analyzes the data collected by the collection unit and creates a plan for generating optimal clones. For example, the analysis unit analyzes genetic information to evaluate genetic compatibility, analyzes personality data to evaluate personality match, and analyzes ability data to evaluate ability match. Based on these evaluation results, the characteristics of the optimal clone as a successor are determined. Step 3: The generation unit generates clones based on the plan created by the analysis unit. For example, the generation unit cultures and grows the clone cells based on the genetic information. The generation unit sets the conditions for culturing the clone cells, such as the culture medium, temperature, and time. Step 4: The Ministry of Education educates the clones on business principles and business knowledge, and provides practical training. For example, the Ministry of Education may conduct business simulations on the clones to develop their business decision-making abilities. The Ministry of Education may provide the clones with teaching materials and training programs to teach them business principles and business knowledge.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the collection unit, analysis unit, generation unit, and education unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects genetic information and personality data using the camera 42 and microphone 38B of the smart device 14, and collects performance data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a plan for generating an optimal clone by analyzing the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and cultures and grows the clone's cells based on the genetic information. The education unit is realized, for example, by the control unit 46A of the smart device 14, and educates the generated clone about management principles and business knowledge, and provides practical training. The correspondence between each unit and the device and control unit is not limited to the above example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and education unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects genetic information and personality data using the camera 42 and microphone 238 of the smart glasses 214, and collects performance data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to create a plan for generating an optimal clone. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and cultures and grows the cells of the clone based on the genetic information. The education unit is realized, for example, by the control unit 46A of the smart glasses 214, and educates the generated clone on management principles and business knowledge, and provides practical training. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and education unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects genetic information and personality data using the camera 42 and microphone 238 of the headset type terminal 314, and collects performance data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a plan for generating an optimal clone by analyzing the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and cultures and grows the cells of the clone based on the genetic information. The education unit is realized, for example, by the control unit 46A of the headset type terminal 314, and educates the generated clone on management principles and business knowledge, and provides practical training. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and education unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects genetic information and personality data using the camera 42 and microphone 238 of the robot 414, and collects performance data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a plan for generating an optimal clone by analyzing the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and cultures and grows the cells of the clone based on the genetic information. The education unit is realized, for example, by the control unit 46A of the robot 414, and educates the generated clone on management principles and business knowledge, and provides practical training. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A collection department that collects data on successors; An analysis unit that analyzes the data collected by the collection unit and creates a plan for generating an optimal clone; a generation unit that generates a clone based on the plan created by the analysis unit; An education unit that educates and trains the clones generated by the generation unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on the genetics, personality, and abilities of potential successors 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data and create a plan for generating clones 2. The system of claim 1, (Appendix 4) The generation unit is Cultivate and grow cloned cells based on genetic information 2. The system of claim 1, (Appendix 5) The Ministry of Education, The clones are then taught management principles and business knowledge, and given practical training. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Infer user emotions and adjust the timing of data collection based on the user emotions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze the past performance data of successor candidates and select the most appropriate data collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: As you collect data, filter it based on the potential successor’s current projects and areas of interest. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user sentiment and prioritize data collection based on user sentiment 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, prioritize collecting the most relevant data by considering the geographic location of the potential successor. 2. The system of claim 1, (Appendix 11) The collecting unit includes: During data collection, analyze the social media activity of potential successors and collect relevant data. 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user sentiment and adjust analysis presentation based on user sentiment 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and adjust parsing length based on user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 18) The generation unit is Estimate the user's emotions and adjust the cloning method based on the user's emotions 2. The system of claim 1, (Appendix 19) The generation unit is When generating clones, the accuracy of generation is adjusted based on the level of detail of the genetic information. 2. The system of claim 1, (Appendix 20) The generation unit is When generating clones, different generation algorithms are applied depending on the category of genetic information. 2. The system of claim 1, (Appendix 21) The generation unit is Estimate user emotions and prioritize cloning based on user emotions 2. The system of claim 1, (Appendix 22) The generation unit is When generating clones, generation priorities are determined based on when genetic information was collected. 2. The system of claim 1, (Appendix 23) The generation unit is When generating clones, the order of generation is adjusted based on the relevance of genetic information 2. The system of claim 1, (Appendix 24) The Ministry of Education, Estimate user emotions and adjust the way educational content is presented based on the user's emotions 2. The system of claim 1, (Appendix 25) The Ministry of Education, When teaching, adjust the level of detail of teaching based on the clone's abilities 2. The system of claim 1, (Appendix 26) The Ministry of Education, During training, different training algorithms are applied depending on the category of clones. 2. The system of claim 1, (Appendix 27) The Ministry of Education, Estimate user's emotions and adjust the length of education based on the user's emotions 2. The system of claim 1, (Appendix 28) The Ministry of Education, During training, prioritize training based on clone growth time 2. The system of claim 1, (Appendix 29) The Ministry of Education, During training, adjust the order of training based on clone relatedness 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects data on successors; an analysis unit that analyzes the data collected by the collection unit and creates a plan for generating an optimal clone; a generation unit that generates a clone based on the plan created by the analysis unit; An education unit that educates and trains the clones generated by the generation unit. A system characterized by:

2. The collecting unit includes: Collect data on the genetics, personality, and abilities of potential successors 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data and create a plan for generating clones 2. The system of claim 1 .

4. The generation unit is Cultivate and grow cloned cells based on genetic information 2. The system of claim 1 .

5. The Ministry of Education, The clones are then taught management principles and business knowledge, and given practical training.

2. The system of claim 1 .

6. The collecting unit includes: Infer user emotions and adjust the timing of data collection based on the user emotions 2. The system of claim 1 .

7. The collecting unit includes: Analyze the past performance data of successor candidates and select the most appropriate data collection method 2. The system of claim 1 .

8. The collecting unit includes: As you collect data, filter it based on the potential successor’s current projects and areas of interest.

2. The system of claim 1 .

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