system

Abstract

An object of a system according to an embodiment is to automatically calculate a proposed score based on a goal and an achievement of an MBO.SOLUTION: A system according to an embodiment includes a target result input unit, a constraint input unit, a proposed score generation unit, and a proposed score output unit. The target result input unit inputs a target and a result. The constraint input unit inputs a constraint. The proposed grade generation part generates a proposed grade on the basis of data input by the target result input part and the constraint input part. The proposed score output unit outputs the proposed scores generated by the proposed score 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 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] In conventional techniques, the calculation of proposed scores based on MBO goals and performance is done manually, which is inefficient and prone to errors.

[0005] The system according to the embodiment aims to automatically calculate a rating plan based on MBO targets and performance. [Means for solving the problem]

[0006] The system according to the embodiment includes a target performance input unit, a constraint condition input unit, a rating plan generation unit, and a rating plan output unit. The target performance input unit inputs targets and performance. The constraint condition input unit inputs constraint conditions. The rating plan generation unit generates a rating plan based on data input by the target performance input unit and the constraint condition input unit. The rating plan output unit outputs the rating plan generated by the rating plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically calculate a proposed score based on the MBO targets and performance. [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 MBO system according to the embodiment of the present invention is a system in which goals and results are input, and a generation AI generates and outputs a proposed score sheet taking into account constraints. This allows the MBO system to automatically calculate a proposed score sheet based on goals and results, and set appropriate rewards and incentives.

[0029] The MBO system according to the embodiment includes a target performance input unit, a constraint condition input unit, a rating proposal generation unit, and a rating proposal output unit. The target performance input unit inputs targets and performance results. For example, the sales department may set a target of "10 million yen in monthly sales" and input "9 million yen in monthly sales" as the performance result. The target performance input unit may also set a target of "5,000 units in monthly production" for the manufacturing department and input "4,800 units in monthly production" as the performance result. The target performance input unit may also set a target of "completion of new product development" for the research and development department and input "80% development progress" as the performance result. The constraint condition input unit inputs constraint conditions. For example, the constraint condition input unit inputs conditions such as a "5 million yen budget limit" and "personnel constraints." The constraint condition input unit may also input conditions such as raw material costs and equipment utilization rates. The constraint condition input unit may also input conditions such as budgets and development periods. The rating proposal generation unit generates a rating proposal based on the data input by the target performance input unit and the constraint condition input unit. For example, the generation AI may output a specific rating proposal such as "80 points" taking into account the goal achievement rate and constraints. The generation AI may also output a specific rating proposal such as "85 points" taking into account the goal achievement rate and constraints. The generation AI may also output a specific rating proposal such as "75 points" taking into account the goal achievement rate and constraints. The rating proposal output unit outputs the rating proposal generated by the rating proposal generation unit. For example, it may display the rating as "80 points for the sales department's monthly sales target." It may also display the rating as "85 points for the manufacturing department's monthly production volume target." It may also display the rating as "75 points for the research and development department's new product development target." As a result, the MBO system according to the embodiment can automatically generate and output rating proposals based on goals, actual results, and constraints. For example, it is possible to objectively evaluate the sales department's goal achievement and set appropriate compensation and incentives. Furthermore, by taking constraints into account, realistic and fair evaluations can be performed.

[0030] In the target and performance input unit, the generation AI automatically references past data when goals and performance data are input, enabling real-time evaluation of the validity of the input content. For example, when goals and performance data are input, the target and performance input unit references a past database and evaluates in real time whether the input goals and performance data match past performance. For example, the target and performance input unit determines whether the input sales target is realistic based on sales data from the past three years. The target and performance input unit can also determine whether the input production target is realistic based on past production volume data. Furthermore, the target and performance input unit can determine whether the input development target is realistic based on past development progress data. This allows real-time evaluation of the validity of the input content.

[0031] The target and performance input unit allows the generation AI to automatically collect relevant external data when goals and performance are input, and complement the input content. For example, when goals and performance are input, the target and performance input unit allows the generation AI to automatically collect market trend and competitive information and complement the input content. For example, the generation AI can evaluate whether the input goals are realistic based on the latest market reports and performance data of competitors. The target and performance input unit can also collect industry trend data and evaluate whether the input goals are in line with industry standards. Furthermore, the target and performance input unit can also collect economic indicator data and evaluate whether the input goals are in line with economic conditions. This allows the input content to be complemented with external data.

[0032] The target and achievement input unit can improve user convenience by inputting targets and achievements using voice input or image recognition. The target and achievement input unit, for example, builds a system in which targets and achievements are input by voice input. For example, when a user inputs "monthly sales of 10 million yen" by voice, the system automatically converts it into text and stores it in a database. The target and achievement input unit can also convert handwritten targets and achievements into digital data using image recognition. For example, a piece of paper on which a user has handwritten "monthly production volume 5,000 units" can be scanned and converted into text data using image recognition technology. Furthermore, the target and achievement input unit can also take a photo of the target and achievements using a smartphone camera and convert the image data into text data using a dedicated app. This improves user convenience using voice input and image recognition.

[0033] The target and performance input unit can provide an interface to promote the sharing of goals and results between different departments and strengthen cooperation between them. The target and performance input unit, for example, develops an interface to promote the sharing of goals and results between different departments. For example, it allows the sales department and the manufacturing department to share goals and results on the same platform. The target and performance input unit can also provide an information sharing function to strengthen cooperation between departments. For example, it can share goal achievement status and progress between departments in real time to strengthen cooperation. Furthermore, the target and performance input unit can also provide a chat function and a notification function to facilitate communication between departments. This makes it possible to provide an interface to strengthen cooperation between departments.

[0034] When constraints are input, the generation AI automatically references past constraints and their results, enabling it to evaluate the validity of the input content in real time. For example, when constraints are input, the generation AI references a past database and evaluates in real time whether the input constraints match past performance. For example, the constraint input section determines whether the input constraints are realistic based on past budget limits and personnel constraints. The constraint input section can also determine whether the input constraints are realistic based on past raw material costs and facility utilization rates. Furthermore, the constraint input section can determine whether the input constraints are realistic based on past development periods and budgets. This allows the validity of the input constraints to be evaluated in real time.

[0035] The constraint condition input unit allows the generation AI to automatically collect external data related to the input of constraint conditions and complement the input content. For example, when inputting constraint conditions, the constraint condition input unit allows the generation AI to automatically collect external data such as economic indicators and legal regulations and complement the input content. For example, the generation AI evaluates whether the input constraint conditions are realistic based on the latest economic data and changes in legal regulations. The constraint condition input unit also allows the generation AI to collect industry trend data and evaluate whether the input constraint conditions conform to industry standards. Furthermore, the constraint condition input unit also allows the generation AI to collect market trend data and evaluate whether the input constraint conditions are in line with market conditions. This allows the input of constraint conditions to be complemented with external data.

[0036] The constraint condition input unit can improve user convenience by inputting constraint conditions using voice input or image recognition. The constraint condition input unit, for example, builds a system in which constraint conditions are input by voice input. For example, when a user inputs "budget limit 5 million yen" by voice, the system automatically converts it into text and stores it in a database. The constraint condition input unit can also convert handwritten constraint conditions into digital data using image recognition. For example, a piece of paper on which a user has handwritten "personnel constraints" can be scanned and converted into text data using image recognition technology. Furthermore, the constraint condition input unit can also photograph constraint conditions using a smartphone camera and convert the image data into text data using a dedicated app. This improves user convenience using voice input and image recognition.

[0037] The constraint input unit can provide an interface to promote the sharing of constraints between different departments and strengthen cooperation between them. The constraint input unit, for example, develops an interface to promote the sharing of constraints between different departments. For example, it allows the sales department and the manufacturing department to share constraints on the same platform. The constraint input unit can also provide an information sharing function to strengthen cooperation between departments. For example, it can share the achievement status and progress of constraints between departments in real time, strengthening the cooperative system. Furthermore, the constraint input unit can also provide a chat function and a notification function to facilitate communication between departments. This makes it possible to provide an interface to strengthen cooperation between departments.

[0038] When generating a rating proposal, the generation AI automatically refers to past rating proposals and their results, and can evaluate the validity of the generated content in real time. For example, when generating a rating proposal, the generation AI refers to a past database and evaluates in real time whether the generated rating proposal matches past performance. For example, the rating proposal generation unit determines whether the generated rating proposal is realistic based on rating data from the past three years. The rating proposal generation unit can also determine whether the generated rating proposal is appropriate based on past evaluation criteria. Furthermore, the rating proposal generation unit can also determine whether the generated rating proposal is reasonable based on past feedback data. This allows the validity of the generated content of the rating proposal to be evaluated in real time.

[0039] The rating proposal generation unit allows the generation AI to automatically collect relevant external data when generating a rating proposal, and complement the generated content. For example, when generating a rating proposal, the generation AI automatically collects market trend and competitive information, and complements the generated content. For example, the rating proposal generation unit evaluates whether the generated rating proposal is realistic based on the latest market reports and performance data of competitors. The rating proposal generation unit can also have the generation AI collect industry trend data and evaluate whether the generated rating proposal conforms to industry standards. Furthermore, the rating proposal generation unit can have the generation AI collect economic indicator data and evaluate whether the generated rating proposal is in line with economic conditions. This allows the generated content of the rating proposal to be complemented with external data.

[0040] The rating proposal generation unit can generate rating proposals that correspond to different evaluation criteria, enabling multifaceted evaluation. The rating proposal generation unit, for example, builds a system that generates rating proposals that correspond to qualitative evaluation. For example, qualitative evaluation is performed based on user feedback and comments. The rating proposal generation unit can also build a system that supports 360-degree evaluation. For example, multifaceted evaluation is performed based on evaluations from colleagues, superiors, and subordinates. Furthermore, the rating proposal generation unit can also build a system that performs hybrid evaluation that combines quantitative evaluation and qualitative evaluation. This enables multifaceted evaluation.

[0041] The evaluation score proposal generation unit can provide an interface for promoting the sharing of evaluation scores between different departments and strengthening cooperation between them. The evaluation score proposal generation unit, for example, develops an interface for promoting the sharing of evaluation scores between different departments. For example, it enables the sales department and the manufacturing department to share evaluation scores on the same platform. The evaluation score proposal generation unit can also provide an information sharing function for strengthening cooperation between departments. For example, it can share the achievement status and progress of evaluation scores between departments in real time, strengthening cooperation. Furthermore, the evaluation score proposal generation unit can also provide a chat function and a notification function for facilitating communication between departments. This makes it possible to provide an interface for strengthening cooperation between departments.

[0042] When outputting a rating proposal, the generation AI automatically refers to past output results and feedback from them, enabling the output unit to evaluate the validity of the output content in real time. For example, when outputting a rating proposal, the generation AI refers to a past database and evaluates in real time whether the output rating proposal is consistent with past performance. For example, the rating proposal output unit determines whether the output rating proposal is realistic based on rating data from the past three years. The rating proposal output unit can also determine whether the output rating proposal is appropriate based on past evaluation criteria. Furthermore, the rating proposal output unit can determine whether the output rating proposal is reasonable based on past feedback data. This allows the validity of the output content of the rating proposal to be evaluated in real time.

[0043] The rating proposal output unit allows the generation AI to automatically collect external data related to the output of the rating proposal and complement the output content. For example, when outputting a rating proposal, the generation AI automatically collects market trend and competitive information to complement the output content. For example, the rating proposal output unit evaluates whether the output rating proposal is realistic based on the latest market reports and performance data of competitors. The rating proposal output unit can also allow the generation AI to collect industry trend data and evaluate whether the output rating proposal conforms to industry standards. Furthermore, the rating proposal output unit can also allow the generation AI to collect economic indicator data and evaluate whether the output rating proposal is in line with economic conditions. This allows the output content of the rating proposal to be complemented with external data.

[0044] The rating proposal output unit can output the rating proposal in different formats to make it easier for the user to intuitively understand. The rating proposal output unit, for example, outputs the rating proposal in graph format to make it easier for the user to intuitively understand. For example, a bar graph or a pie chart is used to visually display the distribution of ratings and the degree of achievement. The rating proposal output unit can also output the rating proposal in chart format. For example, a line graph or a radar chart is used to visually display the progress of ratings and the evaluation of each item. Furthermore, the rating proposal output unit can also output the rating proposal in table format. For example, the ratings for each item are displayed in a list in table format to make it easier for the user to intuitively understand. This makes it easier for the user to intuitively understand the rating proposal.

[0045] The rating proposal output unit can provide an interface to promote the sharing of rating proposals between different departments and strengthen cooperation between them. The rating proposal output unit, for example, develops an interface to promote the sharing of rating proposals between different departments. For example, it enables the sales department and the manufacturing department to share rating proposals on the same platform. The rating proposal output unit can also provide an information sharing function to strengthen cooperation between departments. For example, it can share the achievement status and progress of rating proposals between departments in real time, strengthening cooperation. Furthermore, the rating proposal output unit can also provide a chat function and a notification function to facilitate communication between departments. This makes it possible to provide an interface to strengthen cooperation between departments.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0048] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0049] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0050] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0051] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0052] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0053] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0054] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0055] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0056] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: In the target and performance input section, targets and performance results are entered. For example, the sales department can set a target of "monthly sales of 10 million yen" and enter "monthly sales of 9 million yen" as the performance result. Similarly, the manufacturing department can set a target of "monthly production volume of 5,000 units" and enter "monthly production volume of 4,800 units" as the performance result. Furthermore, the research and development department can set a target of "completion of development of new product" and enter "development progress 80%" as the performance result. Step 2: The constraint condition input section inputs constraint conditions. For example, conditions such as a "budget limit of 5 million yen" or "personnel constraints" can be input. Conditions such as raw material costs and facility utilization rates can also be input. Conditions such as budget and development period can also be input. Step 3: The rating proposal generator generates a rating proposal based on the data input by the target achievement input unit and the constraint condition input unit. For example, the generation AI takes into account the target achievement rate and constraint conditions and outputs a specific rating proposal such as "rating of 80 points." It can also output specific rating proposals such as "rating of 85 points" or "rating of 75 points." Step 4: The rating proposal output unit outputs the rating proposal generated by the rating proposal generation unit. For example, it can be displayed in the form of "The rating for the monthly sales target of the sales department is 80 points." It can also be displayed in the form of "The rating for the monthly production volume target of the manufacturing department is 85 points" or "The rating for the new product development target of the research and development department is 75 points."

[0059] (Example 2) The MBO system according to the embodiment of the present invention is a system in which goals and results are input, and a generation AI generates and outputs a proposed score sheet taking into account constraints. This allows the MBO system to automatically calculate a proposed score sheet based on goals and results, and set appropriate rewards and incentives.

[0060] The MBO system according to the embodiment includes a target performance input unit, a constraint condition input unit, a rating proposal generation unit, and a rating proposal output unit. The target performance input unit inputs targets and performance results. For example, the sales department may set a target of "10 million yen in monthly sales" and input "9 million yen in monthly sales" as the performance result. The target performance input unit may also set a target of "5,000 units in monthly production" for the manufacturing department and input "4,800 units in monthly production" as the performance result. The target performance input unit may also set a target of "completion of new product development" for the research and development department and input "80% development progress" as the performance result. The constraint condition input unit inputs constraint conditions. For example, the constraint condition input unit inputs conditions such as a "5 million yen budget limit" and "personnel constraints." The constraint condition input unit may also input conditions such as raw material costs and equipment utilization rates. The constraint condition input unit may also input conditions such as budgets and development periods. The rating proposal generation unit generates a rating proposal based on the data input by the target performance input unit and the constraint condition input unit. For example, the generation AI may output a specific rating proposal such as "80 points" taking into account the goal achievement rate and constraints. The generation AI may also output a specific rating proposal such as "85 points" taking into account the goal achievement rate and constraints. The generation AI may also output a specific rating proposal such as "75 points" taking into account the goal achievement rate and constraints. The rating proposal output unit outputs the rating proposal generated by the rating proposal generation unit. For example, it may display the rating as "80 points for the sales department's monthly sales target." It may also display the rating as "85 points for the manufacturing department's monthly production volume target." It may also display the rating as "75 points for the research and development department's new product development target." As a result, the MBO system according to the embodiment can automatically generate and output rating proposals based on goals, actual results, and constraints. For example, it is possible to objectively evaluate the sales department's goal achievement and set appropriate compensation and incentives. Furthermore, by taking constraints into account, realistic and fair evaluations can be performed.

[0061] In the target and performance input unit, the generation AI automatically references past data when goals and performance data are input, enabling real-time evaluation of the validity of the input content. For example, when goals and performance data are input, the target and performance input unit references a past database and evaluates in real time whether the input goals and performance data match past performance. For example, the target and performance input unit determines whether the input sales target is realistic based on sales data from the past three years. The target and performance input unit can also determine whether the input production target is realistic based on past production volume data. Furthermore, the target and performance input unit can determine whether the input development target is realistic based on past development progress data. This allows real-time evaluation of the validity of the input content.

[0062] The target and performance input unit allows the generation AI to automatically collect relevant external data when goals and performance are input, and complement the input content. For example, when goals and performance are input, the target and performance input unit allows the generation AI to automatically collect market trend and competitive information and complement the input content. For example, the generation AI can evaluate whether the input goals are realistic based on the latest market reports and performance data of competitors. The target and performance input unit can also collect industry trend data and evaluate whether the input goals are in line with industry standards. Furthermore, the target and performance input unit can also collect economic indicator data and evaluate whether the input goals are in line with economic conditions. This allows the input content to be complemented with external data.

[0063] The goal and achievement input unit can use an emotion estimation function to analyze the emotions expressed when a user inputs goals and achievements, and provide feedback to elicit positive emotions. For example, when a goal and achievements are input, the goal and achievement input unit uses a generation AI to analyze the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the unit can provide advice on how to relax. Furthermore, if the user is feeling anxious about achieving their goal, the unit can also suggest adjusting the goal to a more realistic range. Furthermore, if the user is feeling a sense of accomplishment, the unit can provide positive feedback to reinforce that emotion. This makes it possible to provide feedback that elicits positive emotions from the user.

[0064] The target and achievement input unit can improve user convenience by inputting targets and achievements using voice input or image recognition. The target and achievement input unit, for example, builds a system in which targets and achievements are input by voice input. For example, when a user inputs "monthly sales of 10 million yen" by voice, the system automatically converts it into text and stores it in a database. The target and achievement input unit can also convert handwritten targets and achievements into digital data using image recognition. For example, a piece of paper on which a user has handwritten "monthly production volume 5,000 units" can be scanned and converted into text data using image recognition technology. Furthermore, the target and achievement input unit can also take a photo of the target and achievements using a smartphone camera and convert the image data into text data using a dedicated app. This improves user convenience using voice input and image recognition.

[0065] The target and performance input unit can provide an interface to promote the sharing of goals and results between different departments and strengthen cooperation between them. The target and performance input unit, for example, develops an interface to promote the sharing of goals and results between different departments. For example, it allows the sales department and the manufacturing department to share goals and results on the same platform. The target and performance input unit can also provide an information sharing function to strengthen cooperation between departments. For example, it can share goal achievement status and progress between departments in real time to strengthen cooperation. Furthermore, the target and performance input unit can also provide a chat function and a notification function to facilitate communication between departments. This makes it possible to provide an interface to strengthen cooperation between departments.

[0066] The goal and achievement input unit can use the emotion estimation function to analyze the emotions regarding the goals and achievements entered by the user and make suggestions for improving the input content. The goal and achievement input unit can, for example, use the emotion estimation function to analyze the emotions regarding the goals and achievements entered by the user in real time and make suggestions for improving the input content. For example, if the user is feeling anxious, it can make a suggestion to adjust the goal to a realistic range. Furthermore, if the user is feeling a sense of accomplishment, the goal and achievement input unit can also provide positive feedback to reinforce that feeling. Furthermore, if the user is feeling stressed, the goal and achievement input unit can also provide advice to relax. In this way, it is possible to make suggestions for improving the input content based on the user's emotions.

[0067] When constraints are input, the generation AI automatically references past constraints and their results, enabling it to evaluate the validity of the input content in real time. For example, when constraints are input, the generation AI references a past database and evaluates in real time whether the input constraints match past performance. For example, the constraint input section determines whether the input constraints are realistic based on past budget limits and personnel constraints. The constraint input section can also determine whether the input constraints are realistic based on past raw material costs and facility utilization rates. Furthermore, the constraint input section can determine whether the input constraints are realistic based on past development periods and budgets. This allows the validity of the input constraints to be evaluated in real time.

[0068] The constraint condition input unit allows the generation AI to automatically collect external data related to the input of constraint conditions and complement the input content. For example, when inputting constraint conditions, the constraint condition input unit allows the generation AI to automatically collect external data such as economic indicators and legal regulations and complement the input content. For example, the generation AI evaluates whether the input constraint conditions are realistic based on the latest economic data and changes in legal regulations. The constraint condition input unit also allows the generation AI to collect industry trend data and evaluate whether the input constraint conditions conform to industry standards. Furthermore, the constraint condition input unit also allows the generation AI to collect market trend data and evaluate whether the input constraint conditions are in line with market conditions. This allows the input of constraint conditions to be complemented with external data.

[0069] The constraint condition input unit can use the emotion estimation function to analyze the emotions of the user when inputting constraint conditions and provide feedback to elicit positive emotions. For example, when inputting constraint conditions, the constraint condition input unit uses a generation AI to analyze the user's facial expressions and voice and estimate emotions. For example, if the user is feeling stressed, the constraint condition input unit can provide advice on how to relax. Furthermore, if the user is feeling anxious about the constraint conditions, the constraint condition input unit can also suggest adjusting the constraint conditions to a realistic range. Furthermore, if the user is feeling a sense of accomplishment, the constraint condition input unit can provide positive feedback to reinforce that emotion. This makes it possible to provide feedback that elicits positive emotions from the user.

[0070] The constraint condition input unit can improve user convenience by inputting constraint conditions using voice input or image recognition. The constraint condition input unit, for example, builds a system in which constraint conditions are input by voice input. For example, when a user inputs "budget limit 5 million yen" by voice, the system automatically converts it into text and stores it in a database. The constraint condition input unit can also convert handwritten constraint conditions into digital data using image recognition. For example, a piece of paper on which a user has handwritten "personnel constraints" can be scanned and converted into text data using image recognition technology. Furthermore, the constraint condition input unit can also photograph constraint conditions using a smartphone camera and convert the image data into text data using a dedicated app. This improves user convenience using voice input and image recognition.

[0071] The constraint input unit can provide an interface to promote the sharing of constraints between different departments and strengthen cooperation between them. The constraint input unit, for example, develops an interface to promote the sharing of constraints between different departments. For example, it allows the sales department and the manufacturing department to share constraints on the same platform. The constraint input unit can also provide an information sharing function to strengthen cooperation between departments. For example, it can share the achievement status and progress of constraints between departments in real time, strengthening the cooperative system. Furthermore, the constraint input unit can also provide a chat function and a notification function to facilitate communication between departments. This makes it possible to provide an interface to strengthen cooperation between departments.

[0072] The constraint condition input unit can use the emotion estimation function to analyze the emotion of the constraint conditions input by the user and make suggestions for improving the input content. The constraint condition input unit, for example, uses the emotion estimation function to analyze the emotion of the constraint conditions input by the user in real time and make suggestions for improving the input content. For example, if the user is feeling anxious, the constraint condition input unit can make a suggestion to adjust the constraint conditions to a realistic range. Furthermore, if the user is feeling a sense of accomplishment, the constraint condition input unit can also provide positive feedback to reinforce that emotion. Furthermore, if the user is feeling stressed, the constraint condition input unit can also provide advice to relax. In this way, suggestions for improving the input content can be made based on the user's emotions.

[0073] When generating a rating proposal, the generation AI automatically refers to past rating proposals and their results, and can evaluate the validity of the generated content in real time. For example, when generating a rating proposal, the generation AI refers to a past database and evaluates in real time whether the generated rating proposal matches past performance. For example, the rating proposal generation unit determines whether the generated rating proposal is realistic based on rating data from the past three years. The rating proposal generation unit can also determine whether the generated rating proposal is appropriate based on past evaluation criteria. Furthermore, the rating proposal generation unit can also determine whether the generated rating proposal is reasonable based on past feedback data. This allows the validity of the generated content of the rating proposal to be evaluated in real time.

[0074] The rating proposal generation unit allows the generation AI to automatically collect relevant external data when generating a rating proposal, and complement the generated content. For example, when generating a rating proposal, the generation AI automatically collects market trend and competitive information, and complements the generated content. For example, the rating proposal generation unit evaluates whether the generated rating proposal is realistic based on the latest market reports and performance data of competitors. The rating proposal generation unit can also have the generation AI collect industry trend data and evaluate whether the generated rating proposal conforms to industry standards. Furthermore, the rating proposal generation unit can have the generation AI collect economic indicator data and evaluate whether the generated rating proposal is in line with economic conditions. This allows the generated content of the rating proposal to be complemented with external data.

[0075] The rating proposal generation unit can use the emotion estimation function to analyze the emotions of the user when checking the rating proposal and provide feedback to elicit positive emotions. For example, when generating a rating proposal, the rating proposal generation unit uses a generation AI to analyze the user's facial expressions and voice and estimate the emotion. For example, if the user is feeling stressed, the rating proposal generation unit can provide advice on how to relax. Furthermore, if the user is feeling anxious about the rating proposal, the rating proposal generation unit can also suggest adjusting the rating proposal to a more realistic range. Furthermore, if the user is feeling a sense of accomplishment, the rating proposal generation unit can provide positive feedback to reinforce that emotion. This makes it possible to provide feedback that elicits positive emotions from the user.

[0076] The rating proposal generation unit can generate rating proposals that correspond to different evaluation criteria, enabling multifaceted evaluation. The rating proposal generation unit, for example, builds a system that generates rating proposals that correspond to qualitative evaluation. For example, qualitative evaluation is performed based on user feedback and comments. The rating proposal generation unit can also build a system that supports 360-degree evaluation. For example, multifaceted evaluation is performed based on evaluations from colleagues, superiors, and subordinates. Furthermore, the rating proposal generation unit can also build a system that performs hybrid evaluation that combines quantitative evaluation and qualitative evaluation. This enables multifaceted evaluation.

[0077] The evaluation score proposal generation unit can provide an interface for promoting the sharing of evaluation scores between different departments and strengthening cooperation between them. The evaluation score proposal generation unit, for example, develops an interface for promoting the sharing of evaluation scores between different departments. For example, it enables the sales department and the manufacturing department to share evaluation scores on the same platform. The evaluation score proposal generation unit can also provide an information sharing function for strengthening cooperation between departments. For example, it can share the achievement status and progress of evaluation scores between departments in real time, strengthening cooperation. Furthermore, the evaluation score proposal generation unit can also provide a chat function and a notification function for facilitating communication between departments. This makes it possible to provide an interface for strengthening cooperation between departments.

[0078] The rating proposal generation unit can use the emotion estimation function to analyze the emotion of the user regarding the rating proposal confirmed by the user and make suggestions for improving the generated content. The rating proposal generation unit, for example, uses the emotion estimation function to analyze the emotion of the user regarding the rating proposal confirmed by the user in real time and make suggestions for improving the generated content. For example, if the user is feeling anxious, the rating proposal generation unit can make a suggestion to adjust the rating proposal to a realistic range. Furthermore, if the user is feeling a sense of accomplishment, the rating proposal generation unit can also provide positive feedback to reinforce that emotion. Furthermore, if the user is feeling stressed, the rating proposal generation unit can also provide advice to relax. In this way, suggestions for improving the generated content can be made based on the user's emotions.

[0079] When outputting a rating proposal, the generation AI automatically refers to past output results and feedback from them, enabling the output unit to evaluate the validity of the output content in real time. For example, when outputting a rating proposal, the generation AI refers to a past database and evaluates in real time whether the output rating proposal is consistent with past performance. For example, the rating proposal output unit determines whether the output rating proposal is realistic based on rating data from the past three years. The rating proposal output unit can also determine whether the output rating proposal is appropriate based on past evaluation criteria. Furthermore, the rating proposal output unit can determine whether the output rating proposal is reasonable based on past feedback data. This allows the validity of the output content of the rating proposal to be evaluated in real time.

[0080] The rating proposal output unit allows the generation AI to automatically collect external data related to the output of the rating proposal and complement the output content. For example, when outputting a rating proposal, the generation AI automatically collects market trend and competitive information to complement the output content. For example, the rating proposal output unit evaluates whether the output rating proposal is realistic based on the latest market reports and performance data of competitors. The rating proposal output unit can also allow the generation AI to collect industry trend data and evaluate whether the output rating proposal conforms to industry standards. Furthermore, the rating proposal output unit can also allow the generation AI to collect economic indicator data and evaluate whether the output rating proposal is in line with economic conditions. This allows the output content of the rating proposal to be complemented with external data.

[0081] The rating proposal output unit can use the emotion estimation function to analyze the emotions of the user when checking the rating proposal and provide feedback to elicit positive emotions. For example, when outputting a rating proposal, the rating proposal output unit uses the generation AI to analyze the user's facial expressions and voice and estimate the emotion. For example, if the user is feeling stressed, the rating proposal output unit can provide advice on how to relax. Furthermore, if the user is feeling anxious about the rating proposal, the rating proposal output unit can also suggest adjusting the rating proposal to a more realistic range. Furthermore, if the user is feeling a sense of accomplishment, the rating proposal output unit can provide positive feedback to reinforce that emotion. This makes it possible to provide feedback that elicits positive emotions from the user.

[0082] The rating proposal output unit can output the rating proposal in different formats to make it easier for the user to intuitively understand. The rating proposal output unit, for example, outputs the rating proposal in graph format to make it easier for the user to intuitively understand. For example, a bar graph or a pie chart is used to visually display the distribution of ratings and the degree of achievement. The rating proposal output unit can also output the rating proposal in chart format. For example, a line graph or a radar chart is used to visually display the progress of ratings and the evaluation of each item. Furthermore, the rating proposal output unit can also output the rating proposal in table format. For example, the ratings for each item are displayed in a list in table format to make it easier for the user to intuitively understand. This makes it easier for the user to intuitively understand the rating proposal.

[0083] The rating proposal output unit can provide an interface to promote the sharing of rating proposals between different departments and strengthen cooperation between them. The rating proposal output unit, for example, develops an interface to promote the sharing of rating proposals between different departments. For example, it enables the sales department and the manufacturing department to share rating proposals on the same platform. The rating proposal output unit can also provide an information sharing function to strengthen cooperation between departments. For example, it can share the achievement status and progress of rating proposals between departments in real time, strengthening cooperation. Furthermore, the rating proposal output unit can also provide a chat function and a notification function to facilitate communication between departments. This makes it possible to provide an interface to strengthen cooperation between departments.

[0084] The proposed rating output unit can use the emotion estimation function to analyze the emotion of the user regarding the proposed rating confirmed by the user and make suggestions for improving the output content. The proposed rating output unit can, for example, use the emotion estimation function to analyze the emotion of the user regarding the proposed rating confirmed by the user in real time and make suggestions for improving the output content. For example, if the user is feeling anxious, the proposed rating output unit can make a suggestion to adjust the proposed rating to a realistic range. Furthermore, if the user is feeling a sense of accomplishment, the proposed rating output unit can provide positive feedback to reinforce that emotion. Furthermore, if the user is feeling stressed, the proposed rating output unit can provide advice to relax. In this way, suggestions for improving the output content can be made based on the user's emotions.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0087] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0088] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0089] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0090] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0091] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0092] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0093] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0094] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0095] The target and performance input unit may have a function to check the consistency of the input content when the user inputs the target and performance. For example, if the target and performance input by the user are inconsistent, a warning message is displayed. The target and performance input unit may also display a warning if the input content is significantly different from past data. Furthermore, the target and performance input unit may also display a warning if the input content is significantly different from industry standards. This makes it easier for the user to check the consistency of the input content.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: In the target and performance input section, targets and performance results are entered. For example, the sales department can set a target of "monthly sales of 10 million yen" and enter "monthly sales of 9 million yen" as the performance result. Similarly, the manufacturing department can set a target of "monthly production volume of 5,000 units" and enter "monthly production volume of 4,800 units" as the performance result. Furthermore, the research and development department can set a target of "completion of development of new product" and enter "development progress 80%" as the performance result. Step 2: The constraint condition input section inputs constraint conditions. For example, conditions such as a "budget limit of 5 million yen" or "personnel constraints" can be input. Conditions such as raw material costs and facility utilization rates can also be input. Conditions such as budget and development period can also be input. Step 3: The rating proposal generator generates a rating proposal based on the data input by the target achievement input unit and the constraint condition input unit. For example, the generation AI takes into account the target achievement rate and constraint conditions and outputs a specific rating proposal such as "rating of 80 points." It can also output specific rating proposals such as "rating of 85 points" or "rating of 75 points." Step 4: The rating proposal output unit outputs the rating proposal generated by the rating proposal generation unit. For example, it can be displayed in the form of "The rating for the monthly sales target of the sales department is 80 points." It can also be displayed in the form of "The rating for the monthly production volume target of the manufacturing department is 85 points" or "The rating for the new product development target of the research and development department is 75 points."

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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, in order to avoid confusion and to 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.

[0164] 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]

[0165] 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 target and achievement input section for inputting the target and achievement; a constraint condition input unit for inputting constraint conditions; a rating plan generation unit that generates a rating plan based on the data input by the target performance input unit and the constraint condition input unit; a rating plan output unit that outputs the rating plan generated by the rating plan generation unit. A system characterized by:

2. The target performance input unit When the goals and results are entered, the generation AI automatically collects relevant external data to complement the input.

2. The system of claim 1.

3. The constraint condition input unit When the constraints are entered, the generation AI automatically references past constraints and their results to evaluate the validity of the input in real time.

2. The system of claim 1.

4. The rating plan generation unit When generating the rating proposal, the AI ​​automatically collects relevant external data to complement the generated content.

2. The system of claim 1.

5. The rating proposal output unit The proposed evaluations are output in different formats to make them easier for users to understand intuitively.

2. The system of claim 1.

6. The target performance input unit Analyzing the emotions of users when they input their goals and achievements, and providing feedback to elicit positive emotions.

2. The system of claim 1.

Images