System

Abstract

An object of a system according to an embodiment is to make an efficient proposal based on a current situation or environment.SOLUTION: A system includes an input unit, an analysis unit, a proposal unit, and a quantification unit. The input unit inputs a current situation or environment. The analysis unit analyzes the information input by the input unit. The proposal unit proposes the efficiency based on the information analyzed by the analysis unit. The quantification unit answers the reason for the efficiency proposed by the proposal unit, the score, the operation until the efficiency, and the cost associated with the efficiency.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] Conventional techniques have had the problem that workers are bound by preconceived notions and find it difficult to make efficient suggestions.

[0005] The system according to the embodiment aims to make efficient suggestions based on the current situation and environment. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and a quantification unit. The input unit inputs the current situation or environment. The analysis unit analyzes the information input by the input unit. The proposal unit proposes efficiency improvements based on the information analyzed by the analysis unit. The quantification unit responds with the reasons for the efficiency improvements proposed by the proposal unit, the score, the operation required to achieve the efficiency improvements, and the costs associated with the efficiency improvements. [Effects of the Invention]

[0007] The system according to the embodiment can make efficient suggestions based on the current situation and environment. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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 efficiency suggestion system according to an embodiment of the present invention is a system in which AI proposes efficiency measures that are deemed appropriate based on a vast amount of information by inputting the current situation and environment. This allows workers to improve efficiency without being bound by preconceived ideas.

[0029] The efficiency proposal system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and a quantification unit. The input unit inputs the current situation and environment. For example, detailed information such as the status of a factory production line, an office workflow, and a farm work environment is input. The input unit can input data in a variety of formats, including text data, numerical data, and image data. For example, the status of a factory production line is input as text data. The office workflow is input as numerical data. The farm work environment is input as image data. The analysis unit analyzes the information input by the input unit. For example, the generation AI analyzes the input data using a text generation AI (e.g., LLM). The generation AI can also analyze the input data using a multimodal generation AI. The generation AI can also analyze the input data using a data analysis algorithm. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The data analysis algorithm analyzes patterns in the input data and extracts important information. The proposal unit makes efficiency suggestions based on the information analyzed by the analysis unit. For example, the proposal unit proposes optimizing machine placement and work procedures on a factory production line. The proposal unit also proposes optimizing task priorities and resource allocation in an office workflow. The proposal unit also proposes work timing and equipment selection in a farm work environment. For example, the proposal unit proposes improving production efficiency by changing the placement of machines on a factory production line. In an office workflow, the proposal unit proposes shortening work time by changing task priorities. In a farm work environment, the proposal unit proposes increasing harvest yields by changing the timing of work. The quantification unit responds with the reasons for the efficiency suggestions made by the proposal unit, their scores, the operation required to achieve the efficiency, and the costs associated with the efficiency. For example, in response to a proposal to optimize a factory production line, the quantification unit provides specific numerical values, such as, "This placement will improve production efficiency by 20% and reduce operating time by 10 hours."The quantification unit also makes suggestions for office workflow, such as "Changing the priority of this task will reduce overall work time by 15%." The quantification unit also suggests specific effects for the farm work environment, such as "Performing work at this timing will increase harvest yields by 10%." As a result, the efficiency recommendation system according to the embodiment can input the current situation and environment, allowing the AI ​​to use vast amounts of information to suggest efficiency measures that it deems appropriate. For example, efficiency improvements are expected in a variety of scenarios, such as optimizing factory production lines, improving office workflows, and streamlining the farm work environment.

[0030] The input unit can automatically match input data with similar past situations or environmental data and present reference cases. The input unit, for example, builds a system that analyzes input data and automatically searches for similar past situations and environmental data. For example, it presents the best case for the current situation based on past production line data. The input unit also references a past database and automatically matches the case that is closest to the input situation or environment. For example, it presents the best case for the current business based on past business flow data. The input unit also develops a system that compares input data with past data and automatically extracts cases with high similarity. For example, it presents the best case for the current work environment based on past farm work data. This makes it possible to make more appropriate suggestions by referring to similar past situations and environmental data.

[0031] The input unit can use sensor devices to collect environmental data in real time and feed it back to the AI ​​in order to improve the accuracy of the input data. The input unit, for example, uses sensor devices to build a system that collects environmental data in real time and feeds it back to the AI. For example, temperature sensors and humidity sensors are used to collect factory environmental data. The input unit also uses sensor devices to feed back the data collected in real time to the AI ​​to improve the accuracy of the input data. For example, light sensors and sound sensors are used to collect office environmental data. The input unit also develops a system that uses sensor devices to collect environmental data in real time and feeds that data back to the AI. For example, soil sensors and weather sensors are used to collect farm environmental data. In this way, the accuracy of the input data is improved by collecting environmental data in real time.

[0032] The input unit can collect situational or environmental data from different industries or fields, enabling cross-domain efficiency proposals. The input unit, for example, collects situational or environmental data from different industries and builds a system that makes cross-domain efficiency proposals. For example, data from the manufacturing and service industries is integrated to make efficiency proposals. The input unit also collects environmental data from different fields and makes cross-domain efficiency proposals. For example, data from the medical and agricultural fields is integrated to make efficiency proposals. The input unit also collects data from different industries or fields, developing a system that makes cross-domain efficiency proposals. For example, data from the education and energy fields is integrated to make efficiency proposals. In this way, by collecting data from different industries or fields, it becomes possible to make cross-domain efficiency proposals.

[0033] The input unit allows a worker to input data using voice input or gesture input, thereby enabling more intuitive data input. The input unit, for example, builds a system in which a worker inputs situation and environmental data using voice input. For example, voice recognition technology is used to convert the worker's instructions into text data. The input unit also develops a system in which a worker inputs situation and environmental data using gesture input. For example, gesture recognition technology is used to collect the worker's movements as data. The input unit also builds a system in which a worker combines voice input and gesture input to achieve more intuitive data input. For example, voice and gestures are recognized simultaneously to input data. This allows more intuitive data input using voice input or gesture input.

[0034] The proposal unit can learn from past proposals and their results and build a feedback loop to improve the accuracy of proposals. The proposal unit, for example, develops a system that learns from past proposals and their results and builds a feedback loop to improve the accuracy of proposals. For example, it improves proposals based on past success stories and failure stories. The proposal unit also collects proposal results in real time and builds a feedback loop to improve the accuracy of proposals based on that data. For example, it measures the effectiveness of proposals and reflects this in the next proposal. The proposal unit also develops a system that analyzes past proposal data and builds a feedback loop to improve the accuracy of proposals. For example, it analyzes the factors that made a proposal successful and the factors that made it unsuccessful and reflects this in the proposal. In this way, the accuracy of proposals is improved by learning from past proposals and their results.

[0035] In order to ensure the diversity of proposals, the proposal unit can combine different algorithms to generate multiple proposals and select the optimal one. The proposal unit, for example, builds a system that generates multiple efficiency proposals by combining different algorithms and selects the optimal one from among them. For example, it combines a machine learning algorithm with a rule-based algorithm. In addition, in order to ensure the diversity of proposals, the proposal unit generates multiple proposals using different algorithms and selects the optimal one from among them. For example, it combines a genetic algorithm with a neural network. In addition, the proposal unit develops a system that generates multiple efficiency proposals by combining different algorithms and selects the optimal one. For example, it combines Bayesian estimation with a support vector machine. In this way, by combining different algorithms, the diversity of proposals is ensured and the optimal proposal can be selected.

[0036] The proposal unit can make efficiency proposals from a global perspective using data from different regions or cultural spheres. The proposal unit, for example, collects data from different regions and cultural spheres and builds a system that makes efficiency proposals from a global perspective. For example, it makes efficiency proposals based on production line data from each country. The proposal unit also analyzes data from different cultural spheres and makes efficiency proposals from a global perspective. For example, it makes efficiency proposals based on business flow data from different cultures. The proposal unit also develops a system that makes efficiency proposals from a global perspective using data from different regions. For example, it makes efficiency proposals based on agricultural data from each region. In this way, by using data from different regions and cultural spheres, it becomes possible to make efficiency proposals from a global perspective.

[0037] The proposal unit can verify the proposed efficiency improvement plan in advance in a simulation environment and confirm its effectiveness before implementation. The proposal unit, for example, builds a system that verifies the proposed efficiency improvement plan in advance in a simulation environment and confirms its effectiveness before implementation. For example, it simulates a factory production line. The proposal unit also uses the simulation environment to verify the effectiveness of the proposed efficiency improvement plan in advance. For example, it simulates an office work flow. The proposal unit also develops a system that verifies the proposed efficiency improvement plan in a simulation environment and confirms its effectiveness before implementation. For example, it simulates a farm work environment. In this way, by verifying in advance in a simulation environment, it is possible to confirm its effectiveness before implementation.

[0038] The quantification unit can generate infographics or animations to visually explain the reasons for the proposal. For example, the quantification unit builds a system that generates infographics to visually explain the reasons for the proposal. For example, the effects of the proposal are shown in graphs or charts. The quantification unit also develops a system that generates animations to visually explain the reasons for the proposal. For example, the proposal process is shown in animation. The quantification unit also builds a system that visually explains the reasons for the proposal using infographics or animations. For example, visually showing the effects of the proposal promotes understanding. This visual explanation promotes understanding of the proposal.

[0039] The quantification unit can automatically collect objections or questions to the reasons for the proposal and generate answers thereto. The quantification unit, for example, builds a system that automatically collects objections or questions to the reasons for the proposal and generates answers thereto. For example, it analyzes objections to the proposal and generates appropriate answers. The quantification unit also collects objections or questions to the reasons for the proposal in real time and generates answers thereto. For example, it analyzes questions about the proposal and generates appropriate answers. The quantification unit also develops a system that automatically collects objections or questions to the reasons for the proposal and generates answers thereto. For example, it analyzes objections or questions to the proposal and generates appropriate answers. In this way, by automatically generating answers to objections or questions, acceptance of the proposal is promoted.

[0040] The Quantification Department translates the reasons for proposals into different languages ​​to obtain feedback from an international perspective. For example, the Quantification Department builds a system that translates the reasons for proposals into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The Quantification Department also develops a system that posts the translated reasons for proposals on a multilingual platform and obtains feedback from users around the world. The Quantification Department also collects advice and suggestions for improvement from an international perspective based on the reasons for proposals translated into different languages, thereby improving the quality of the proposals. For example, it reflects feedback that takes cultural and market differences into account. In this way, by translating into different languages, feedback from an international perspective can be obtained.

[0041] The quantification department can reevaluate the reasons for a proposal from the perspective of different fields of expertise and provide more multifaceted reasons. For example, the quantification department builds a system that reevaluates the reasons for a proposal from the perspective of different fields of expertise and provides more multifaceted reasons. For example, it combines technical and economic perspectives. The quantification department also collects feedback from experts in different fields of expertise and reevaluates the reasons for the proposal. For example, it reflects opinions from experts in the medical and engineering fields. The quantification department also develops a system that reevaluates the reasons for a proposal from the perspective of different fields of expertise and provides more multifaceted reasons. For example, it combines perspectives from environmental science and social science. This makes it possible to provide more multifaceted reasons by reevaluating from the perspective of different fields of expertise.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The efficiency suggestion system may further include a health management unit that monitors the user's health condition. The health management unit, for example, uses a wearable device to collect vital data such as the user's heart rate, blood pressure, and body temperature in real time. The health management unit also analyzes the collected vital data to evaluate the user's health condition. For example, a high heart rate may indicate increased stress, so the health management unit can make suggestions for relaxation. The health management unit can also make suggestions for improving work efficiency based on the user's health condition. For example, if the user's body temperature is high, the health management unit can suggest taking a break. This makes it possible to make efficiency suggestions that take the user's health condition into consideration.

[0044] The efficiency suggestion system may further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit may, for example, analyze the user's past work history and deliverables to evaluate the skill level. The skill evaluation unit may also suggest training programs for the user to acquire new skills. For example, if the user lacks skills in a specific task, the skill evaluation unit may suggest online courses or workshops to improve those skills. The skill evaluation unit may also suggest optimal work procedures and tools according to the user's skill level. This makes it possible to suggest efficiency improvements according to the user's skill level.

[0045] The efficiency suggestion system can further include a work style analysis unit that analyzes the user's work style. The work style analysis unit, for example, analyzes what time of day the user works most efficiently. The work style analysis unit can also evaluate what environment the user can best concentrate in. For example, noise-canceling headphones can be suggested to a user who works more efficiently in a quiet environment. The work style analysis unit can also suggest an optimal work schedule based on the user's work style. This makes it possible to suggest efficiency suggestions that suit the user's work style.

[0046] The efficiency suggestion system can further include a history analysis unit that analyzes the user's work history. The history analysis unit, for example, analyzes past work data and identifies which work procedures were most efficient. The history analysis unit can also identify bottlenecks in work based on past work data and propose improvement measures. For example, if a specific work task is frequently delayed, the history analysis unit can identify the cause and propose improvement measures. The history analysis unit can also evaluate work performance based on past work data and provide feedback. This makes it possible to propose efficiency measures based on past work history.

[0047] The efficiency suggestion system can further include an environment monitoring unit that monitors the user's work environment. The environment monitoring unit collects environmental data, such as temperature, humidity, illuminance, and noise level, in real time. The environment monitoring unit can also analyze the collected environmental data and make suggestions for improving the work environment. For example, if the illuminance is low, it can suggest adding more lighting. The environment monitoring unit can also suggest the optimal layout of the work space based on the user's work environment. This makes it possible to make efficiency suggestions that are tailored to the user's work environment.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The input section inputs the current situation and environment. For example, detailed information about the status of the factory production line, the office workflow, and the farm work environment is input. The input section can also input data in a variety of formats, including text data, numerical data, and image data. For example, the status of the factory production line is input as text data, the office workflow is input as numerical data, and the farm work environment is input as image data. Step 2: The analysis unit analyzes the information input by the input unit. For example, the generation AI analyzes the input data using a text generation AI (e.g., LLM). The generation AI can also analyze the input data using a multimodal generation AI. The generation AI can also analyze the input data using a data analysis algorithm. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The data analysis algorithm analyzes patterns in the input data and extracts important information. Step 3: The proposal unit makes efficiency suggestions based on the information analyzed by the analysis unit. For example, the proposal unit proposes optimizing machine placement and work procedures in a factory production line. The proposal unit also proposes optimizing task priorities and resource allocation in an office workflow. The proposal unit also proposes work timing and the selection of equipment to be used in a farm work environment. For example, the proposal unit proposes improving production efficiency by changing the placement of machines in a factory production line. In an office workflow, the proposal unit proposes shortening work time by changing task priorities. In a farm work environment, the proposal unit proposes increasing harvest yields by changing the timing of work. Step 4: The quantification department responds with the reasons and scores for the efficiencies proposed by the proposal department, the operation required to achieve the efficiency, and the costs associated with the efficiency. For example, in response to a proposal to optimize a factory production line, the quantification department provides specific figures such as, "This layout will improve production efficiency by 20% and reduce operating time by 10 hours." Similarly, the quantification department may provide suggestions for office workflows such as, "By changing the priority of this task, the overall work time will be reduced by 15%." Similarly, the quantification department may provide specific effects for a farm's work environment such as, "Performing work at this timing will increase harvest yields by 10%."

[0050] (Example 2) The efficiency suggestion system according to an embodiment of the present invention is a system in which AI proposes efficiency measures that are deemed appropriate based on a vast amount of information by inputting the current situation and environment. This allows workers to improve efficiency without being bound by preconceived ideas.

[0051] The efficiency proposal system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and a quantification unit. The input unit inputs the current situation and environment. For example, detailed information such as the status of a factory production line, an office workflow, and a farm work environment is input. The input unit can input data in a variety of formats, including text data, numerical data, and image data. For example, the status of a factory production line is input as text data. The office workflow is input as numerical data. The farm work environment is input as image data. The analysis unit analyzes the information input by the input unit. For example, the generation AI analyzes the input data using a text generation AI (e.g., LLM). The generation AI can also analyze the input data using a multimodal generation AI. The generation AI can also analyze the input data using a data analysis algorithm. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The data analysis algorithm analyzes patterns in the input data and extracts important information. The proposal unit makes efficiency suggestions based on the information analyzed by the analysis unit. For example, the proposal unit proposes optimizing machine placement and work procedures on a factory production line. The proposal unit also proposes optimizing task priorities and resource allocation in an office workflow. The proposal unit also proposes work timing and equipment selection in a farm work environment. For example, the proposal unit proposes improving production efficiency by changing the placement of machines on a factory production line. In an office workflow, the proposal unit proposes shortening work time by changing task priorities. In a farm work environment, the proposal unit proposes increasing harvest yields by changing the timing of work. The quantification unit responds with the reasons for the efficiency suggestions made by the proposal unit, their scores, the operation required to achieve the efficiency, and the costs associated with the efficiency. For example, in response to a proposal to optimize a factory production line, the quantification unit provides specific numerical values, such as, "This placement will improve production efficiency by 20% and reduce operating time by 10 hours."The quantification unit also makes suggestions for office workflow, such as "Changing the priority of this task will reduce overall work time by 15%." The quantification unit also suggests specific effects for the farm work environment, such as "Performing work at this timing will increase harvest yields by 10%." As a result, the efficiency recommendation system according to the embodiment can input the current situation and environment, allowing the AI ​​to use vast amounts of information to suggest efficiency measures that it deems appropriate. For example, efficiency improvements are expected in a variety of scenarios, such as optimizing factory production lines, improving office workflows, and streamlining the farm work environment.

[0052] The input unit uses a generative AI that estimates the worker's emotions to analyze the worker's emotional response to the input situation or environment, eliminating emotional bias. For example, the input unit uses generative AI to perform emotion analysis on the situation or environmental data input by the worker to identify emotional bias. For example, if the worker is feeling stressed, the input unit eliminates that emotion and processes the data as objective data. The input unit also uses an emotion estimation function to analyze the worker's emotional response in real time as they input data, eliminating emotional bias. For example, if the worker is feeling anxious, the input unit ignores that emotion and processes the data. The input unit also analyzes the worker's emotional state and builds a filtering system to eliminate emotional bias. For example, if the worker is feeling anger, the input unit eliminates that emotion and processes the data. This eliminates emotional bias, enabling more objective data processing.

[0053] The input unit can automatically match input data with similar past situations or environmental data and present reference cases. The input unit, for example, builds a system that analyzes input data and automatically searches for similar past situations and environmental data. For example, it presents the best case for the current situation based on past production line data. The input unit also references a past database and automatically matches the case that is closest to the input situation or environment. For example, it presents the best case for the current business based on past business flow data. The input unit also develops a system that compares input data with past data and automatically extracts cases with high similarity. For example, it presents the best case for the current work environment based on past farm work data. This makes it possible to make more appropriate suggestions by referring to similar past situations and environmental data.

[0054] The input unit can use sensor devices to collect environmental data in real time and feed it back to the AI ​​in order to improve the accuracy of the input data. The input unit, for example, uses sensor devices to build a system that collects environmental data in real time and feeds it back to the AI. For example, temperature sensors and humidity sensors are used to collect factory environmental data. The input unit also uses sensor devices to feed back the data collected in real time to the AI ​​to improve the accuracy of the input data. For example, light sensors and sound sensors are used to collect office environmental data. The input unit also develops a system that uses sensor devices to collect environmental data in real time and feeds that data back to the AI. For example, soil sensors and weather sensors are used to collect farm environmental data. In this way, the accuracy of the input data is improved by collecting environmental data in real time.

[0055] The input unit can collect situational or environmental data from different industries or fields, enabling cross-domain efficiency proposals. The input unit, for example, collects situational or environmental data from different industries and builds a system that makes cross-domain efficiency proposals. For example, data from the manufacturing and service industries is integrated to make efficiency proposals. The input unit also collects environmental data from different fields and makes cross-domain efficiency proposals. For example, data from the medical and agricultural fields is integrated to make efficiency proposals. The input unit also collects data from different industries or fields, developing a system that makes cross-domain efficiency proposals. For example, data from the education and energy fields is integrated to make efficiency proposals. In this way, by collecting data from different industries or fields, it becomes possible to make cross-domain efficiency proposals.

[0056] The input unit allows a worker to input data using voice input or gesture input, thereby enabling more intuitive data input. The input unit, for example, builds a system in which a worker inputs situation and environmental data using voice input. For example, voice recognition technology is used to convert the worker's instructions into text data. The input unit also develops a system in which a worker inputs situation and environmental data using gesture input. For example, gesture recognition technology is used to collect the worker's movements as data. The input unit also builds a system in which a worker combines voice input and gesture input to achieve more intuitive data input. For example, voice and gestures are recognized simultaneously to input data. This allows more intuitive data input using voice input or gesture input.

[0057] The input unit uses the emotion estimation function to monitor the stress level of the worker when entering data, and can encourage input in a low-stress state. The input unit, for example, uses the emotion estimation function to build a system that monitors the stress level of the worker when entering data. For example, it analyzes the worker's facial expressions and voice to measure the stress level. Furthermore, if the stress level is high, the input unit makes suggestions to the worker to relax, and encourages input in a low-stress state. For example, it provides music or a message to help the worker relax. Furthermore, the input unit uses the emotion estimation function to develop a system that monitors the worker's stress level in real time, and encourages input in a low-stress state. For example, it encourages input during times when the stress level is low. In this way, by monitoring the stress level, it is possible to encourage input in a low-stress state.

[0058] The proposal unit can use the generation AI to analyze emotional responses to proposed efficiency improvement plans and prioritize proposals that are emotionally easy to accept. The proposal unit, for example, builds a system that analyzes workers' emotional responses to efficiency improvement plans proposed by the generation AI. For example, it prioritizes the adoption of plans that receive a large number of positive responses. The proposal unit also uses emotion analysis to evaluate the emotional acceptability of proposed efficiency improvement plans and prioritizes proposals that are emotionally easy to accept. For example, it evaluates based on the emotion score of the proposal. The proposal unit also develops a system that analyzes emotional responses to proposed efficiency improvement plans in real time and prioritizes proposals that are emotionally easy to accept. For example, it evaluates based on real-time emotional responses to the proposal. In this way, by prioritizing proposals that are emotionally easy to accept, the feasibility of the proposals is improved.

[0059] The proposal unit can learn from past proposals and their results and build a feedback loop to improve the accuracy of proposals. The proposal unit, for example, develops a system that learns from past proposals and their results and builds a feedback loop to improve the accuracy of proposals. For example, it improves proposals based on past success stories and failure stories. The proposal unit also collects proposal results in real time and builds a feedback loop to improve the accuracy of proposals based on that data. For example, it measures the effectiveness of proposals and reflects this in the next proposal. The proposal unit also develops a system that analyzes past proposal data and builds a feedback loop to improve the accuracy of proposals. For example, it analyzes the factors that made a proposal successful and the factors that made it unsuccessful and reflects this in the proposal. In this way, the accuracy of proposals is improved by learning from past proposals and their results.

[0060] In order to ensure the diversity of proposals, the proposal unit can combine different algorithms to generate multiple proposals and select the optimal one. The proposal unit, for example, builds a system that generates multiple efficiency proposals by combining different algorithms and selects the optimal one from among them. For example, it combines a machine learning algorithm with a rule-based algorithm. In addition, in order to ensure the diversity of proposals, the proposal unit generates multiple proposals using different algorithms and selects the optimal one from among them. For example, it combines a genetic algorithm with a neural network. In addition, the proposal unit develops a system that generates multiple efficiency proposals by combining different algorithms and selects the optimal one. For example, it combines Bayesian estimation with a support vector machine. In this way, by combining different algorithms, the diversity of proposals is ensured and the optimal proposal can be selected.

[0061] The proposal unit can make efficiency proposals from a global perspective using data from different regions or cultural spheres. The proposal unit, for example, collects data from different regions and cultural spheres and builds a system that makes efficiency proposals from a global perspective. For example, it makes efficiency proposals based on production line data from each country. The proposal unit also analyzes data from different cultural spheres and makes efficiency proposals from a global perspective. For example, it makes efficiency proposals based on business flow data from different cultures. The proposal unit also develops a system that makes efficiency proposals from a global perspective using data from different regions. For example, it makes efficiency proposals based on agricultural data from each region. In this way, by using data from different regions and cultural spheres, it becomes possible to make efficiency proposals from a global perspective.

[0062] The proposal unit can verify the proposed efficiency improvement plan in advance in a simulation environment and confirm its effectiveness before implementation. The proposal unit, for example, builds a system that verifies the proposed efficiency improvement plan in advance in a simulation environment and confirms its effectiveness before implementation. For example, it simulates a factory production line. The proposal unit also uses the simulation environment to verify the effectiveness of the proposed efficiency improvement plan in advance. For example, it simulates an office work flow. The proposal unit also develops a system that verifies the proposed efficiency improvement plan in a simulation environment and confirms its effectiveness before implementation. For example, it simulates a farm work environment. In this way, by verifying in advance in a simulation environment, it is possible to confirm its effectiveness before implementation.

[0063] The suggestion unit uses the emotion estimation function to evaluate the worker's emotional acceptance of proposed efficiency improvement plans and can select the optimal proposal. The suggestion unit, for example, uses the emotion estimation function to build a system that evaluates the worker's emotional acceptance of proposed efficiency improvement plans. For example, the suggestion unit selects plans that receive a large number of positive emotional responses to the plans. The suggestion unit also analyzes the worker's emotional acceptance of proposed efficiency improvement plans in real time and selects the optimal proposal. For example, the suggestion unit evaluates the proposal based on an emotion score. The suggestion unit also uses the emotion estimation function to develop a system that evaluates the worker's emotional acceptance of proposed efficiency improvement plans and selects the optimal proposal. For example, the suggestion unit evaluates based on real-time emotional responses to the proposal. In this way, the optimal proposal can be selected by evaluating the emotional acceptance.

[0064] The quantification unit can use the generative AI to analyze emotional reactions to the reasons for the proposal and emphasize reasons that are emotionally persuasive. For example, the quantification unit constructs a system in which the generative AI analyzes emotional reactions to the reasons for the proposal and emphasizes reasons that are emotionally persuasive. For example, it emphasizes reasons that have a high number of positive emotional reactions. The quantification unit also uses emotion analysis to evaluate emotional reactions to the reasons for the proposal and emphasize reasons that are emotionally persuasive. For example, it evaluates reasons based on emotion scores. The quantification unit also analyzes emotional reactions to the reasons for the proposal in real time and develops a system that emphasizes reasons that are emotionally persuasive. For example, it evaluates based on real-time emotional reactions to the reasons for the proposal. In this way, emphasizing reasons that are emotionally persuasive promotes the acceptance of the proposal.

[0065] The quantification unit can generate infographics or animations to visually explain the reasons for the proposal. For example, the quantification unit builds a system that generates infographics to visually explain the reasons for the proposal. For example, the effects of the proposal are shown in graphs or charts. The quantification unit also develops a system that generates animations to visually explain the reasons for the proposal. For example, the proposal process is shown in animation. The quantification unit also builds a system that visually explains the reasons for the proposal using infographics or animations. For example, visually showing the effects of the proposal promotes understanding. This visual explanation promotes understanding of the proposal.

[0066] The quantification unit can automatically collect objections or questions to the reasons for the proposal and generate answers thereto. The quantification unit, for example, builds a system that automatically collects objections or questions to the reasons for the proposal and generates answers thereto. For example, it analyzes objections to the proposal and generates appropriate answers. The quantification unit also collects objections or questions to the reasons for the proposal in real time and generates answers thereto. For example, it analyzes questions about the proposal and generates appropriate answers. The quantification unit also develops a system that automatically collects objections or questions to the reasons for the proposal and generates answers thereto. For example, it analyzes objections or questions to the proposal and generates appropriate answers. In this way, by automatically generating answers to objections or questions, acceptance of the proposal is promoted.

[0067] The Quantification Department translates the reasons for proposals into different languages ​​to obtain feedback from an international perspective. For example, the Quantification Department builds a system that translates the reasons for proposals into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. The Quantification Department also develops a system that posts the translated reasons for proposals on a multilingual platform and obtains feedback from users around the world. The Quantification Department also collects advice and suggestions for improvement from an international perspective based on the reasons for proposals translated into different languages, thereby improving the quality of the proposals. For example, it reflects feedback that takes cultural and market differences into account. In this way, by translating into different languages, feedback from an international perspective can be obtained.

[0068] The quantification department can reevaluate the reasons for a proposal from the perspective of different fields of expertise and provide more multifaceted reasons. For example, the quantification department builds a system that reevaluates the reasons for a proposal from the perspective of different fields of expertise and provides more multifaceted reasons. For example, it combines technical and economic perspectives. The quantification department also collects feedback from experts in different fields of expertise and reevaluates the reasons for the proposal. For example, it reflects opinions from experts in the medical and engineering fields. The quantification department also develops a system that reevaluates the reasons for a proposal from the perspective of different fields of expertise and provides more multifaceted reasons. For example, it combines perspectives from environmental science and social science. This makes it possible to provide more multifaceted reasons by reevaluating from the perspective of different fields of expertise.

[0069] The quantification unit can use the emotion estimation function to collect users' emotional reactions to the reason for the suggestion and identify reasons that are likely to be emotionally relatable. For example, the quantification unit uses the emotion estimation function to collect users' emotional reactions to the reason for the suggestion in real time and identify reasons that are likely to be emotionally relatable based on the data. For example, it emphasizes reasons that have a high number of positive emotional reactions. The quantification unit also analyzes users' emotional reactions to the reason for the suggestion and builds a system that identifies reasons that are likely to be emotionally relatable. For example, it evaluates the reasons based on an emotion score. The quantification unit also uses the emotion estimation function to collect users' emotional reactions to the reason for the suggestion and develops a system that identifies reasons that are likely to be emotionally relatable. For example, it evaluates based on real-time emotional reactions to the reason for the suggestion. In this way, by identifying reasons that are likely to be emotionally relatable, acceptance of the suggestion is promoted.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The efficiency suggestion system may further include a health management unit that monitors the user's health condition. The health management unit, for example, uses a wearable device to collect vital data such as the user's heart rate, blood pressure, and body temperature in real time. The health management unit also analyzes the collected vital data to evaluate the user's health condition. For example, a high heart rate may indicate increased stress, so the health management unit can make suggestions for relaxation. The health management unit can also make suggestions for improving work efficiency based on the user's health condition. For example, if the user's body temperature is high, the health management unit can suggest taking a break. This makes it possible to make efficiency suggestions that take the user's health condition into consideration.

[0072] The efficiency suggestion system can further include an environmental adjustment unit that estimates the user's emotions and adjusts the work environment based on the estimated user emotions. For example, if the user feels stressed, the environmental adjustment unit can adjust the brightness and color temperature of the lighting to enhance relaxation. The environmental adjustment unit can also provide music or white noise if the user wants to improve concentration. Furthermore, the environmental adjustment unit can adjust the temperature and humidity of the work space according to the user's emotional state. This makes it possible to provide an optimal work environment according to the user's emotional state.

[0073] The efficiency suggestion system may further include a skill evaluation unit that evaluates the user's skill level. The skill evaluation unit may, for example, analyze the user's past work history and deliverables to evaluate the skill level. The skill evaluation unit may also suggest training programs for the user to acquire new skills. For example, if the user lacks skills in a specific task, the skill evaluation unit may suggest online courses or workshops to improve those skills. The skill evaluation unit may also suggest optimal work procedures and tools according to the user's skill level. This makes it possible to suggest efficiency improvements according to the user's skill level.

[0074] The efficiency suggestion system may further include a priority adjustment unit that estimates the user's emotions and adjusts the priority of tasks based on the estimated user emotions. For example, if the user feels tired, the priority adjustment unit postpones less important tasks and prioritizes rest. Furthermore, if the user is highly motivated, the priority adjustment unit can prioritize tasks with high difficulty. Furthermore, the priority adjustment unit can dynamically change the order of tasks according to the user's emotional state. This enables optimal task management according to the user's emotional state.

[0075] The efficiency suggestion system can further include a work style analysis unit that analyzes the user's work style. The work style analysis unit, for example, analyzes what time of day the user works most efficiently. The work style analysis unit can also evaluate what environment the user can best concentrate in. For example, noise-canceling headphones can be suggested to a user who works more efficiently in a quiet environment. The work style analysis unit can also suggest an optimal work schedule based on the user's work style. This makes it possible to suggest efficiency suggestions that suit the user's work style.

[0076] The efficiency suggestion system may further include a communication adjustment unit that estimates the user's emotions and adjusts the communication method based on the estimated user emotions. For example, the communication adjustment unit may send a message in a soft tone if the user is feeling stressed. Also, the communication adjustment unit may send an encouraging message if the user is highly motivated. Furthermore, the communication adjustment unit may adjust the frequency and timing of communication according to the user's emotional state. This enables optimal communication according to the user's emotional state.

[0077] The efficiency suggestion system can further include a history analysis unit that analyzes the user's work history. The history analysis unit, for example, analyzes past work data and identifies which work procedures were most efficient. The history analysis unit can also identify bottlenecks in work based on past work data and propose improvement measures. For example, if a specific work task is frequently delayed, the history analysis unit can identify the cause and propose improvement measures. The history analysis unit can also evaluate work performance based on past work data and provide feedback. This makes it possible to propose efficiency measures based on past work history.

[0078] The efficiency suggestion system may further include a feedback adjustment unit that estimates the user's emotions and adjusts the content of the feedback based on the estimated user emotions. For example, if the user has positive emotions, the feedback adjustment unit may provide feedback including specific improvements. Alternatively, if the user has negative emotions, the feedback adjustment unit may provide feedback including an encouraging message. Furthermore, the feedback adjustment unit may adjust the timing and frequency of the feedback depending on the user's emotional state. This enables optimal feedback according to the user's emotional state.

[0079] The efficiency suggestion system can further include an environment monitoring unit that monitors the user's work environment. The environment monitoring unit collects environmental data, such as temperature, humidity, illuminance, and noise level, in real time. The environment monitoring unit can also analyze the collected environmental data and make suggestions for improving the work environment. For example, if the illuminance is low, it can suggest adding more lighting. The environment monitoring unit can also suggest the optimal layout of the work space based on the user's work environment. This makes it possible to make efficiency suggestions that are tailored to the user's work environment.

[0080] The efficiency suggestion system may further include a progress management unit that estimates the user's emotions and manages the progress of work based on the estimated user emotions. For example, if the user feels tired, the progress management unit may suggest slowing down the progress of work. Also, if the user is highly motivated, the progress management unit may suggest accelerating the progress of work. Furthermore, the progress management unit may dynamically adjust the progress of work according to the user's emotional state. This enables optimal work progress management according to the user's emotional state.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The input section inputs the current situation and environment. For example, detailed information about the status of the factory production line, the office workflow, and the farm work environment is input. The input section can also input data in a variety of formats, including text data, numerical data, and image data. For example, the status of the factory production line is input as text data, the office workflow is input as numerical data, and the farm work environment is input as image data. Step 2: The analysis unit analyzes the information input by the input unit. For example, the generation AI analyzes the input data using a text generation AI (e.g., LLM). The generation AI can also analyze the input data using a multimodal generation AI. The generation AI can also analyze the input data using a data analysis algorithm. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The data analysis algorithm analyzes patterns in the input data and extracts important information. Step 3: The proposal unit makes efficiency suggestions based on the information analyzed by the analysis unit. For example, the proposal unit proposes optimizing machine placement and work procedures in a factory production line. The proposal unit also proposes optimizing task priorities and resource allocation in an office workflow. The proposal unit also proposes work timing and the selection of equipment to be used in a farm work environment. For example, the proposal unit proposes improving production efficiency by changing the placement of machines in a factory production line. In an office workflow, the proposal unit proposes shortening work time by changing task priorities. In a farm work environment, the proposal unit proposes increasing harvest yields by changing the timing of work. Step 4: The quantification department responds with the reasons and scores for the efficiencies proposed by the proposal department, the operation required to achieve the efficiency, and the costs associated with the efficiency. For example, in response to a proposal to optimize a factory production line, the quantification department provides specific figures such as, "This layout will improve production efficiency by 20% and reduce operating time by 10 hours." Similarly, the quantification department may provide suggestions for office workflows such as, "By changing the priority of this task, the overall work time will be reduced by 15%." Similarly, the quantification department may provide specific effects for a farm's work environment such as, "Performing work at this timing will increase harvest yields by 10%."

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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 AI 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] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 AI 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 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. an input section for inputting the current situation or environment; an analysis unit that analyzes the information input by the input unit; a proposal unit that proposes efficiency based on the information analyzed by the analysis unit; a quantification unit that answers the reason for the efficiency proposed by the proposal unit, the score, the operation up to the efficiency, and the cost associated with the efficiency. A system characterized by:

2. The input unit includes: Using generative AI to estimate worker emotions, analyze emotional responses to input situations or environments, eliminating emotional bias.

2. The system of claim 1.

3. The input unit includes: Automatically matches input data with similar past situations or environmental data, and provides reference cases.

2. The system of claim 1.

4. The input unit includes: To improve the accuracy of input data, sensor devices are used to collect environmental data in real time and provide feedback to AI.

2. The system of claim 1.

5. The input unit includes: Collect situational or environmental data from different industries or fields to enable cross-domain efficiency proposals 2. The system of claim 1.

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