System

Abstract

An object of the system according to the embodiment is to propose a specific and measurable goal and to effectively perform goal setting and evaluation.SOLUTION: A system according to an embodiment includes an objective suggestion unit, an objective setting unit, and an objective retracement unit. The goal suggestion unit learns business data and personal data and suggests a specific and measurable goal. The goal setting unit sets an initial goal by the principal and the superior based on the goal proposed by the goal proposing unit. The goal retracement unit evaluates a degree of achievement of the goal set by the goal setting unit at the end of the term.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the goal management system did not function effectively, and goal setting and evaluation were unclear.

[0005] The system according to the embodiment aims to propose specific and measurable goals and to effectively set and evaluate goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal proposal unit, a goal setting unit, and a goal review unit. The goal proposal unit learns business data and personal data and proposes specific and measurable goals. The goal setting unit allows the individual and their supervisor to set goals at the beginning of a period based on the goals proposed by the goal proposal unit. The goal review unit evaluates the degree of achievement of the goals set by the goal setting unit at the end of the period. [Effects of the Invention]

[0007] The system according to the embodiment can propose specific and measurable goals and effectively set and evaluate goals. [Brief explanation of the drawings]

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

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The goal management assistant AI system according to an embodiment of the present invention learns business and personal data, and the generation AI proposes specific and measurable goals, supporting goal setting at the beginning of a period and goal review at the end of the period. As a result, the goal management assistant AI system supports corporate and business goal achievement and personal growth, improving business contribution and productivity, and achieving appropriate compensation.

[0029] A goal management assistant AI system according to an embodiment includes a goal proposal unit, a goal setting unit, and a goal review unit. The goal proposal unit learns business data and personal data and proposes specific, measurable goals. For example, the goal proposal unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to propose a specific goal, such as setting the number of monthly sales visits to 20 to increase sales by 10%. The generation AI analyzes data entered by a user, understands the content, and generates appropriate goals. The goal setting unit sets goals at the beginning of a fiscal year between the employee and their supervisor based on the goals proposed by the goal proposal unit. For example, the goal setting unit sets specific goals between the employee and their supervisor based on the goals proposed by the AI. The goal review unit evaluates the degree of achievement of the goals set by the goal setting unit at the end of the fiscal year. For example, the goal review unit compares the goals set by the AI ​​at the beginning of the fiscal year with actual results and evaluates the degree of achievement. As a result, the goal management assistant AI system according to an embodiment can improve the efficiency of the goal management process and improve business contribution and productivity.

[0030] The goal suggestion unit can evaluate the achievability of goals proposed by the generation AI in real time by referring to past success and failure cases. For example, the goal suggestion unit can automatically extract past success and failure cases from a database and evaluate the achievability of goals proposed by the generation AI. For example, it can refer to past cases in which similar goals were achieved and analyze the factors that contributed to their success. This allows for a real-time evaluation of the achievability of goals, making it possible to set realistic goals.

[0031] The goal suggestion unit can generate individually customized goals based on the user's past performance data for the goals proposed by the generation AI. For example, the generation AI analyzes the user's past performance data and generates individually customized goals based on that data. For example, the goal suggestion unit suggests appropriate goals taking into consideration past achievements and skill levels. This makes it possible to generate individually customized goals based on the user's past performance data.

[0032] The goal suggestion unit can refer to data from different industries and occupations for the goals proposed by the generation AI and generate crossover goals. For example, the goal suggestion unit allows the generation AI to collect data from different industries and occupations and generate crossover goals based on that data. For example, it can propose a goal that combines technical goals and marketing goals. This makes it possible to generate crossover goals by referencing data from different industries and occupations.

[0033] The goal suggestion unit can strengthen the cooperative system by linking the goals proposed by the generation AI with the goals of the entire team. For example, the goal suggestion unit can build a system that links the goals proposed by the generation AI with the goals of the entire team, thereby strengthening the cooperative system. For example, individual goals can be set to contribute to achieving the team's goals. This can strengthen the cooperative system by linking them with the goals of the entire team.

[0034] The goal setting unit can provide an interactive platform for the user and the supervisor to jointly fine-tune the goals proposed by the generation AI. The goal setting unit develops an interactive platform for the user and the supervisor to jointly fine-tune the goals proposed by the generation AI, for example. For example, it provides a function that allows goals to be edited in real time. This makes it possible to provide an interactive platform for the user and the supervisor to jointly fine-tune the goals.

[0035] The goal setting unit can automatically generate a specific action plan for achieving the goal proposed by the generation AI. The goal setting unit develops a system that automatically generates a specific action plan for achieving the goal proposed by the generation AI, for example. For example, it suggests the steps and resources necessary to achieve the goal. This makes it possible to automatically generate a specific action plan for achieving the goal.

[0036] The goal setting unit can set goals that promote collaboration between different departments and projects for the goals proposed by the generation AI. The goal setting unit develops a system that sets goals that promote collaboration between different departments and projects for the goals proposed by the generation AI, for example. For example, it proposes goals that require cooperation between departments. This makes it possible to set goals that promote collaboration between different departments and projects.

[0037] The goal setting unit can set composite goals that combine short-term and long-term goals for the goals proposed by the generation AI. The goal setting unit develops a system that sets composite goals that combine short-term and long-term goals for the goals proposed by the generation AI, for example. For example, monthly and yearly goals are set simultaneously. This makes it possible to set composite goals that combine short-term and long-term goals.

[0038] The goal review unit can integrate the user's self-assessment and the supervisor's assessment and provide comprehensive feedback when the generation AI conducts a goal review at the end of the term. For example, the goal review unit will develop a system in which the generation AI integrates the user's self-assessment and the supervisor's assessment and provides comprehensive feedback. For example, it will compare the assessments of both and analyze the similarities and differences. This will enable the user's self-assessment and the supervisor's assessment to be integrated and comprehensive feedback to be provided.

[0039] The goal review department can compare the results of the generation AI with those of other teams or departments and provide a benchmark when the generation AI conducts a goal review at the end of the term. The goal review department will develop a system that, for example, compares the results of the generation AI with those of other teams or departments and provides a benchmark when the generation AI conducts a goal review at the end of the term. For example, it will refer to the results of other teams that have the same goals. This will allow it to compare the results of the generation AI with those of other teams or departments and provide a benchmark.

[0040] The goal review unit can automatically suggest improvements for the next goal setting when the generation AI conducts a goal review at the end of the term. For example, the goal review unit will develop a system that automatically suggests improvements for the next goal setting when the generation AI conducts a goal review at the end of the term. For example, it will identify the cause of a goal that was not achieved and propose improvement measures. This will make it possible to automatically suggest improvements for the next goal setting.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The goal suggestion unit can also suggest goals aimed at maintaining or improving health based on the user's health data in addition to the goals proposed by the generation AI. For example, it can analyze the user's step count and sleep data to set daily exercise and sleep goals. It can also suggest nutritionally balanced meals based on food records. It can also monitor stress levels and recommend relaxation activities.

[0043] The goal suggestion unit can also suggest goals that take into account the user's hobbies and interests in addition to the goals proposed by the generation AI. For example, if a user is interested in music, the goal can be set as the amount of time they need to practice an instrument. Alternatively, a user who enjoys reading can set a goal of the number of books they want to read per month. Furthermore, a user who enjoys traveling can create a list of places they want to visit and set it as a goal to achieve.

[0044] The goal suggestion unit can also suggest goals to strengthen the user's social connections in addition to the goals suggested by the generation AI. For example, the goal could be to regularly meet with friends and family. It could also be to participate in community activities or volunteer activities. Furthermore, it could be possible to set a goal for the number of posts on a specific social networking site in order to increase online interactions.

[0045] The goal suggestion unit can also suggest goals to support the user's career advancement in addition to the goals suggested by the generation AI. For example, it can set a goal of study time to acquire a specific skill. It can also suggest a study plan for obtaining a qualification. Furthermore, it can be considered a goal to attend networking events or industry seminars.

[0046] The goal suggestion unit can also suggest sustainable living goals based on the user's lifestyle data in addition to the goals proposed by the generation AI. For example, it can set a goal to reduce energy consumption. It can also suggest goals to promote recycling and reuse. It can also set goals such as buying local produce or using public transportation.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The goal suggestion unit learns business and personal data and proposes specific, measurable goals. For example, the goal suggestion unit uses a generative AI (e.g., text generation AI or multimodal generation AI) to propose specific goals, such as setting the number of monthly sales visits to 20 in order to increase sales by 10%. The generative AI analyzes the data entered by the user, understands its contents, and generates appropriate goals. Step 2: The goal setting unit allows the employee and their superior to set goals at the beginning of the period based on the goals proposed by the goal suggestion unit. For example, the goal setting unit allows the employee and their superior to set specific goals based on the goals proposed by the AI. Step 3: The goal review section evaluates the degree of achievement of the goals set by the goal setting section at the end of the period. For example, the goal review section compares the goals set by the AI ​​at the beginning of the period with the actual results and evaluates the degree of achievement.

[0049] (Example 2) The goal management assistant AI system according to an embodiment of the present invention learns business and personal data, and the generation AI proposes specific and measurable goals, supporting goal setting at the beginning of a period and goal review at the end of the period. As a result, the goal management assistant AI system supports corporate and business goal achievement and personal growth, improving business contribution and productivity, and achieving appropriate compensation.

[0050] A goal management assistant AI system according to an embodiment includes a goal proposal unit, a goal setting unit, and a goal review unit. The goal proposal unit learns business data and personal data and proposes specific, measurable goals. For example, the goal proposal unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to propose a specific goal, such as setting the number of monthly sales visits to 20 to increase sales by 10%. The generation AI analyzes data entered by a user, understands the content, and generates appropriate goals. The goal setting unit sets goals at the beginning of a fiscal year between the employee and their supervisor based on the goals proposed by the goal proposal unit. For example, the goal setting unit sets specific goals between the employee and their supervisor based on the goals proposed by the AI. The goal review unit evaluates the degree of achievement of the goals set by the goal setting unit at the end of the fiscal year. For example, the goal review unit compares the goals set by the AI ​​at the beginning of the fiscal year with actual results and evaluates the degree of achievement. As a result, the goal management assistant AI system according to an embodiment can improve the efficiency of the goal management process and improve business contribution and productivity.

[0051] The goal suggestion unit can evaluate the achievability of goals proposed by the generation AI in real time by referring to past success and failure cases. For example, the goal suggestion unit can automatically extract past success and failure cases from a database and evaluate the achievability of goals proposed by the generation AI. For example, it can refer to past cases in which similar goals were achieved and analyze the factors that contributed to their success. This allows for a real-time evaluation of the achievability of goals, making it possible to set realistic goals.

[0052] The goal suggestion unit can generate individually customized goals based on the user's past performance data for the goals proposed by the generation AI. For example, the generation AI analyzes the user's past performance data and generates individually customized goals based on that data. For example, the goal suggestion unit suggests appropriate goals taking into consideration past achievements and skill levels. This makes it possible to generate individually customized goals based on the user's past performance data.

[0053] The goal suggestion unit can use the emotion estimation function to analyze the user's current emotional state for the goals proposed by the generation AI and suggest goals to increase motivation. For example, the goal suggestion unit can use the emotion estimation function to analyze the user's current emotional state in real time and suggest goals to increase motivation based on the results. For example, if the user is feeling stressed, it can suggest goals that are easy to achieve. This makes it possible to analyze the user's emotional state and suggest goals to increase motivation.

[0054] The goal suggestion unit can refer to data from different industries and occupations for the goals proposed by the generation AI and generate crossover goals. For example, the goal suggestion unit allows the generation AI to collect data from different industries and occupations and generate crossover goals based on that data. For example, it can propose a goal that combines technical goals and marketing goals. This makes it possible to generate crossover goals by referencing data from different industries and occupations.

[0055] The goal suggestion unit can strengthen the cooperative system by linking the goals proposed by the generation AI with the goals of the entire team. For example, the goal suggestion unit can build a system that links the goals proposed by the generation AI with the goals of the entire team, thereby strengthening the cooperative system. For example, individual goals can be set to contribute to achieving the team's goals. This can strengthen the cooperative system by linking them with the goals of the entire team.

[0056] The goal setting unit can provide an interactive platform for the user and the supervisor to jointly fine-tune the goals proposed by the generation AI. The goal setting unit develops an interactive platform for the user and the supervisor to jointly fine-tune the goals proposed by the generation AI, for example. For example, it provides a function that allows goals to be edited in real time. This makes it possible to provide an interactive platform for the user and the supervisor to jointly fine-tune the goals.

[0057] The goal setting unit can automatically generate a specific action plan for achieving the goal proposed by the generation AI. The goal setting unit develops a system that automatically generates a specific action plan for achieving the goal proposed by the generation AI, for example. For example, it suggests the steps and resources necessary to achieve the goal. This makes it possible to automatically generate a specific action plan for achieving the goal.

[0058] The goal setting unit can use the emotion estimation function to analyze the emotional states of the user and superior for the goals proposed by the generation AI and support goal setting that is easy for both parties to agree on. For example, the goal setting unit can use the emotion estimation function to analyze the emotional states of the user and superior in real time and support goal setting that is easy for both parties to agree on based on the results. For example, goals with high emotion scores can be set preferentially. This allows the emotional states of the user and superior to be analyzed and goal setting that is easy for both parties to agree on to be supported.

[0059] The goal setting unit can set goals that promote collaboration between different departments and projects for the goals proposed by the generation AI. The goal setting unit develops a system that sets goals that promote collaboration between different departments and projects for the goals proposed by the generation AI, for example. For example, it proposes goals that require cooperation between departments. This makes it possible to set goals that promote collaboration between different departments and projects.

[0060] The goal setting unit can set composite goals that combine short-term and long-term goals for the goals proposed by the generation AI. The goal setting unit develops a system that sets composite goals that combine short-term and long-term goals for the goals proposed by the generation AI, for example. For example, monthly and yearly goals are set simultaneously. This makes it possible to set composite goals that combine short-term and long-term goals.

[0061] The goal setting unit can use the emotion estimation function to suggest relaxation techniques for the goals proposed by the generation AI to reduce the stress the user feels when setting goals. For example, the goal setting unit can use the emotion estimation function to analyze in real time the stress the user feels when setting goals and suggest relaxation techniques based on the results. For example, it can suggest breathing techniques to help the user relax. This makes it possible to suggest relaxation techniques to reduce the stress the user feels when setting goals.

[0062] The goal review unit can integrate the user's self-assessment and the supervisor's assessment and provide comprehensive feedback when the generation AI conducts a goal review at the end of the term. For example, the goal review unit will develop a system in which the generation AI integrates the user's self-assessment and the supervisor's assessment and provides comprehensive feedback. For example, it will compare the assessments of both and analyze the similarities and differences. This will enable the user's self-assessment and the supervisor's assessment to be integrated and comprehensive feedback to be provided.

[0063] When the generation AI conducts a goal review at the end of the term, the goal review unit can use the emotion estimation function to analyze the user's emotional state and emphasize positive feedback. For example, the goal review unit can use the emotion estimation function to analyze the user's emotional state in real time and develop a system that emphasizes positive feedback based on the results. For example, it can provide feedback that makes the user feel a sense of accomplishment. This makes it possible to analyze the user's emotional state and emphasize positive feedback.

[0064] The goal review department can compare the results of the generation AI with those of other teams or departments and provide a benchmark when the generation AI conducts a goal review at the end of the term. The goal review department will develop a system that, for example, compares the results of the generation AI with those of other teams or departments and provides a benchmark when the generation AI conducts a goal review at the end of the term. For example, it will refer to the results of other teams that have the same goals. This will allow it to compare the results of the generation AI with those of other teams or departments and provide a benchmark.

[0065] The goal review unit can automatically suggest improvements for the next goal setting when the generation AI conducts a goal review at the end of the term. For example, the goal review unit will develop a system that automatically suggests improvements for the next goal setting when the generation AI conducts a goal review at the end of the term. For example, it will identify the cause of a goal that was not achieved and propose improvement measures. This will make it possible to automatically suggest improvements for the next goal setting.

[0066] When the generation AI conducts a goal review at the end of the term, the goal review unit can use the emotion estimation function to provide positive feedback to reinforce the sense of accomplishment the user feels when reviewing. The goal review unit, for example, uses the emotion estimation function to develop a system that provides positive feedback to reinforce the sense of accomplishment the user feels when reviewing. For example, it adds comments praising the user's efforts. This makes it possible to provide positive feedback to reinforce the sense of accomplishment the user feels when reviewing.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The goal suggestion unit can also suggest goals aimed at maintaining or improving health based on the user's health data in addition to the goals proposed by the generation AI. For example, it can analyze the user's step count and sleep data to set daily exercise and sleep goals. It can also suggest nutritionally balanced meals based on food records. It can also monitor stress levels and recommend relaxation activities.

[0069] The goal suggestion unit can also suggest goals that take into account the user's hobbies and interests in addition to the goals proposed by the generation AI. For example, if a user is interested in music, the goal can be set as the amount of time they need to practice an instrument. Alternatively, a user who enjoys reading can set a goal of the number of books they want to read per month. Furthermore, a user who enjoys traveling can create a list of places they want to visit and set it as a goal to achieve.

[0070] The goal suggestion unit can also suggest goals to strengthen the user's social connections in addition to the goals suggested by the generation AI. For example, the goal could be to regularly meet with friends and family. It could also be to participate in community activities or volunteer activities. Furthermore, it could be possible to set a goal for the number of posts on a specific social networking site in order to increase online interactions.

[0071] The goal suggestion unit can also suggest goals to support the user's career advancement in addition to the goals suggested by the generation AI. For example, it can set a goal of study time to acquire a specific skill. It can also suggest a study plan for obtaining a qualification. Furthermore, it can be considered a goal to attend networking events or industry seminars.

[0072] The goal suggestion unit can also use the user's emotion estimation function to suggest refreshing goals based on the user's emotional state in response to the goals proposed by the generation AI. For example, if the user is tired, it can suggest a relaxing vacation. If the user is feeling stressed, it can also set a goal of a meditation or yoga session. Furthermore, if the user is feeling depressed, it can suggest a hobby activity to change their mood.

[0073] The goal suggestion unit can also provide feedback based on the user's emotional state for the goals proposed by the generation AI, using the user's emotion estimation function. For example, if the user feels a sense of accomplishment, it can provide feedback praising their efforts. If the user feels frustrated, it can also send an encouraging message. Furthermore, if the user feels anxious, it can provide advice to reassure them.

[0074] The goal suggestion unit can also use the user's emotion estimation function to suggest learning goals that correspond to the user's emotional state in relation to the goals proposed by the generation AI. For example, if the user is excited, it can suggest challenging learning goals. Also, if the user is tired, it can suggest content that can be learned while relaxing. Furthermore, if the user lacks concentration, it can be considered to set learning goals that can be achieved in a short period of time.

[0075] The goal suggestion unit can also use the user's emotion estimation function to suggest exercise goals based on the user's emotional state in response to the goals proposed by the generation AI. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching. If the user is energetic, it can also set an active exercise goal such as running or hiking. Furthermore, if the user is feeling down, it can suggest light exercise to refresh the user's mood.

[0076] The goal suggestion unit can also use the user's emotion estimation function to suggest creative goals based on the user's emotional state in addition to the goals proposed by the generation AI. For example, if the user is in a creative mood, it can suggest new projects or ideas. If the user is tired, it can set a goal of arts and crafts activities that can be enjoyed while relaxing. Furthermore, if the user is feeling stressed, it can suggest creative activities to relieve stress.

[0077] The goal suggestion unit can also suggest sustainable living goals based on the user's lifestyle data in addition to the goals proposed by the generation AI. For example, it can set a goal to reduce energy consumption. It can also suggest goals to promote recycling and reuse. It can also set goals such as buying local produce or using public transportation.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The goal suggestion unit learns business and personal data and proposes specific, measurable goals. For example, the goal suggestion unit uses a generative AI (e.g., text generation AI or multimodal generation AI) to propose specific goals, such as setting the number of monthly sales visits to 20 in order to increase sales by 10%. The generative AI analyzes the data entered by the user, understands its contents, and generates appropriate goals. Step 2: The goal setting unit allows the employee and their superior to set goals at the beginning of the period based on the goals proposed by the goal suggestion unit. For example, the goal setting unit allows the employee and their superior to set specific goals based on the goals proposed by the AI. Step 3: The goal review section evaluates the degree of achievement of the goals set by the goal setting section at the end of the period. For example, the goal review section compares the goals set by the AI ​​at the beginning of the period with the actual results and evaluates the degree of achievement.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] 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.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] 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.

[0092] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] 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.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] 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.

[0106] 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.

[0107] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] 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.

[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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] 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.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0139] 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.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 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 goal suggestion unit that learns business and personal data and suggests specific, measurable goals; a goal setting unit in which the employee and their superior set goals at the beginning of a period based on the goals proposed by the goal suggestion unit; and a goal review unit that evaluates the degree of achievement of the goals set by the goal setting unit at the end of the term. A system characterized by:

2. The goal suggestion unit The generative AI will refer to past successes and failures to evaluate the feasibility of achieving the goals proposed in real time.

2. The system of claim 1.

3. The goal suggestion unit The AI ​​generates crossover goals by referencing data from different industries and occupations.

2. The system of claim 1.

4. The goal setting unit The AI ​​uses an emotion estimation function to analyze the emotional states of the user and the superior in relation to the goals proposed by the AI, helping to set goals that are easy for both parties to agree on.

2. The system of claim 1.

5. A goal suggestion unit that learns business and personal data and suggests specific, measurable goals; a goal setting unit in which the employee and their superior set goals at the beginning of a period based on the goals proposed by the goal suggestion unit; and a goal review unit that evaluates the degree of achievement of the goals set by the goal setting unit at the end of the term. A system characterized by:

6. The goal suggestion unit The generative AI proposes goals, and then uses emotion estimation to analyze the user's current emotional state and propose goals to increase motivation.

2. The system of claim 1.

7. The goal suggestion unit Strengthen collaboration by linking the goals proposed by the generative AI with the goals of the entire team.

2. The system of claim 1.

8. The goal review unit When the generative AI reviews the end-of-term goals, it uses emotion estimation to analyze the user's emotional state and emphasize positive feedback.

2. The system of claim 1.

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