system

Abstract

The system according to the embodiment aims to provide appropriate advice for the user's current situation and worries, and to simulate subsequent actions. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a simulation unit. The reception unit inputs the user's current situation or concerns. The analysis unit analyzes the information input by the reception unit. The provision unit provides appropriate advice to the user based on the information analyzed by the analysis unit. The simulation unit simulates actions the user should take based on the advice provided by the provision unit.

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 technologies have had the problem of not providing adequate advice to users regarding their current situation and concerns, and not simulating subsequent actions.

[0005] The system according to the embodiment aims to provide appropriate advice for the user's current situation and worries, and to simulate subsequent actions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a simulation unit. The reception unit inputs the user's current situation or concerns. The analysis unit analyzes the information input by the reception unit. The provision unit provides appropriate advice to the user based on the information analyzed by the analysis unit. The simulation unit simulates actions that the user should take based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate advice for the user's current situation or concerns, and can simulate subsequent actions. [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) A decision support system according to an embodiment of the present invention inputs a user's current situation and concerns, and a generation AI analyzes the input, provides optimal advice, and simulates actions. In this decision support system, the user inputs their current situation and concerns, and the generation AI analyzes the input and provides optimal advice to the user. This advice is customized based on the user's past experiences and current situation. Furthermore, the generation AI suggests specific actions the user should take and simulates the potential consequences of those actions. For example, a user inputs their current situation and concerns. The user can freely input their own feelings and thoughts. For example, they can input a specific concern such as, "I'm wondering whether I should quit my job." This information is then input into the generation AI. The generation AI then analyzes the input information. The generation AI considers the user's past experiences and current situation and provides optimal advice. For example, by referring to data from users who have had similar concerns in the past, the generation AI can provide specific advice such as, "Before looking for a new job, try discussing the problems with your current job with your boss." Furthermore, the generation AI suggests specific actions the user should take. For example, it suggests actions such as, "Providing specific steps to discuss with your boss" or "Providing resources to help you find a new job." In this case, the generative AI simulates the possible outcomes of the actions the user should take and presents the results to the user. For example, it may provide a simulation result such as, "As a result of discussing with your boss, there is a high possibility that your work environment will improve." This allows the user to more clearly understand their options and make the optimal decision. In this way, the decision support system allows the user to more clearly understand their options and make the optimal decision.

[0029] A decision support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a simulation unit. The reception unit inputs a user's current situation or concerns. The user's current situation or concerns include, but are not limited to, health status, work-related concerns, and lifestyle issues. The reception unit can accept, for example, text input, voice input, and multiple-choice input. The analysis unit analyzes the information input by the reception unit. The analysis can be performed using, for example, but not limited to, data mining, statistical analysis, or a machine learning algorithm. The analysis unit can also analyze information by referring to past user data or expert opinions. The provision unit provides appropriate advice to the user based on the information analyzed by the analysis unit. The advice can include, for example, health advice, career advice, and the like, but is not limited to these examples. The provision unit can also provide specific advice to the user. The simulation unit simulates actions the user should take based on the advice provided by the provision unit. The simulation can be performed using, for example, but not limited to, a Monte Carlo method or scenario analysis. The simulation unit can also simulate the results of actions the user should take. As a result, the decision support system according to the embodiment provides optimal advice for the user's current situation and worries, and simulates actions, allowing the user to make optimal decisions.

[0030] Furthermore, the decision support system includes a reception unit that receives text input, voice input, and multiple-choice input. The reception unit can receive, for example, text input, voice input, and multiple-choice input. For example, if the user selects text input, they can input their current situation and concerns using a keyboard. If the user selects voice input, they can input their current situation and concerns by voice using a microphone. If the user selects multiple-choice input, they can select the appropriate option from the options presented by the system. This allows the user to input information in a variety of ways, improving convenience.

[0031] The analysis unit can analyze information based on past user data or expert opinions. The analysis unit can analyze information based on, for example, past user data. Past user data includes, for example, past consultation content, behavioral history, etc., but is not limited to these examples. The analysis unit can also analyze information based on expert opinions. Expert opinions include, for example, expert interviews, literature research, etc., but are not limited to these examples. In this way, by utilizing past data and expert opinions, the accuracy of the analysis is improved.

[0032] The providing unit can provide specific advice to the user. For example, the providing unit can provide specific advice to the user. Specific advice includes, but is not limited to, an action plan, provision of resources, etc. For example, the providing unit can provide specific advice to the user such as "before looking for a new job, try discussing the problems with your current job with your boss." By providing specific advice, the user can more easily implement the advice.

[0033] The simulation unit can simulate the results of actions that the user should take. For example, the simulation unit can simulate the results of actions that the user should take. Examples of simulations include, but are not limited to, predictive models, scenario analyses, and the like. For example, the simulation unit can suggest actions that the user should take, such as "providing specific steps for discussing with your boss" or "providing resources for searching for a new job," and simulate the possible results of those actions. In this way, by simulating the results of the actions, the user can clearly understand their options.

[0034] The reception unit can analyze the user's past input history and suggest the optimal input format. The reception unit can, for example, analyze the user's past input history and suggest the optimal input format. The past input history includes, for example, past input content, input format history, etc., but is not limited to these examples. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. In addition, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the input format to be used in a specific time period based on the user's past input history. In this way, by utilizing the past input history, the optimal input format for the user can be provided.

[0035] The reception unit can filter the input content based on the user's current situation and areas of interest when receiving the input. For example, the reception unit can filter the input content based on the user's current situation and areas of interest when receiving the input. Examples of the current situation and areas of interest include, but are not limited to, survey results and behavioral history. For example, if the user inputs concerns about their current work, the reception unit can prioritize displaying related questions and options. Furthermore, if the user has a specific area of ​​interest (e.g., career, health, etc.), the reception unit can prioritize displaying input content related to that area. Furthermore, the reception unit can filter appropriate input content based on the user's current situation (e.g., considering changing jobs, having health problems, etc.). This improves input efficiency by providing input content that matches the user's situation and areas of interest.

[0036] The reception unit can select the optimal reception means depending on the user's input method when receiving input. For example, the reception unit can select the optimal reception means depending on the user's input method (voice, text, multiple-choice, etc.) when receiving input. Input methods include, but are not limited to, voice input, text input, multiple-choice format, etc. For example, if the user selects voice input, the reception unit can analyze the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the input content in real time and provide appropriate feedback. Furthermore, if the user selects multiple-choice format input, the reception unit can automatically generate and present related options. This improves input convenience by providing the optimal reception means depending on the user's input method.

[0037] The reception unit can prioritize accepting highly relevant inputs when receiving inputs, taking into consideration the user's geographical location information. For example, the reception unit can prioritize accepting highly relevant inputs when receiving inputs, taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, when the user is in a specific area, the reception unit can prioritize accepting information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize accepting information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize accepting information related to the user's home. This allows more relevant information to be collected by providing inputs based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and accept related input when receiving input. For example, the reception unit can analyze the user's social media activity and accept related input when receiving input. Social media activity includes, but is not limited to, posted content, likes, and the likes. For example, the reception unit can preferentially accept related input content based on information shared by the user on social media. Furthermore, the reception unit can analyze the user's social media activity and suggest related input content. Furthermore, the reception unit can accept related input content based on the activity of the user's friends on social media. In this way, by utilizing social media activity, input based on the user's interests can be provided.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can customize the reception method by reflecting the user's past feedback when receiving input. Past feedback includes, but is not limited to, user ratings, comments, etc. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. Furthermore, the reception unit can preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input reception method. In this way, the optimal reception method for the user can be provided by utilizing the past feedback.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The importance of the input information includes, but is not limited to, for example, urgency and impact. For example, the analysis unit can perform a detailed analysis of important input information. Furthermore, the analysis unit can perform a concise analysis of general input information. Furthermore, the analysis unit can perform a detailed analysis of information in which the user is particularly interested. This improves the efficiency of the analysis by providing an analysis according to the importance of the input information.

[0041] The analysis unit can apply different analysis algorithms depending on the category of input information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of input information during analysis. Categories of input information include, but are not limited to, health, work, and lifestyle. For example, the analysis unit can apply a career-related analysis algorithm to input information related to career. Furthermore, the analysis unit can apply a health-related analysis algorithm to input information related to health. Furthermore, the analysis unit can apply a relationship-related analysis algorithm to input information related to relationships. This improves the accuracy of the analysis by providing an analysis according to the category of input information.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past reports, analysis logs, etc. For example, the analysis unit can adjust the current analysis results based on the user's past analysis results. Furthermore, the analysis unit can extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. By utilizing the past analysis results, the accuracy of the current analysis is improved.

[0043] The analysis unit can determine the priority of analysis based on the submission time of the input information during analysis. For example, the analysis unit can determine the priority of analysis based on the submission time of the input information during analysis. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. For example, the analysis unit can prioritize analyzing the most recent input information. Furthermore, the analysis unit can prioritize analyzing input information submitted by the user during a specific time period. Furthermore, the analysis unit can prioritize analyzing input information designated by the user as urgent. This improves the efficiency of analysis by providing an analysis according to the submission time.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. The relevance of the input information includes, but is not limited to, for example, the degree of coincidence of topics and the frequency of related keywords. For example, the analysis unit can prioritize analyzing highly relevant input information. Furthermore, the analysis unit can prioritize analyzing highly relevant information specified by the user. Furthermore, the analysis unit can extract highly relevant information from the user's past input history and analyze it preferentially. This improves the efficiency of analysis by prioritizing analysis of highly relevant information.

[0045] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terms. Furthermore, the analysis unit can provide analysis results that use appropriate technical terms according to the user's level of expertise. This allows the user to understand the analysis results better by providing an analysis that is appropriate for the user's level of expertise.

[0046] The providing unit can adjust the level of detail of the advice by taking into account the user's past experience and current situation when providing advice. For example, the providing unit can adjust the level of detail of the advice by taking into account the user's past experience and current situation when providing advice. Past experience and current situation include, but are not limited to, past consultation content, current health status, etc. For example, the providing unit can provide detailed advice based on the user's past experience. Furthermore, the providing unit can provide appropriate advice by taking into account the user's current situation. Furthermore, the providing unit can provide optimal advice by comprehensively taking into account the user's past experience and current situation. In this way, more appropriate advice can be provided by taking into account the user's past experience and current situation.

[0047] The providing unit can apply different advice algorithms depending on the category of the user's concern when providing advice. For example, the providing unit can apply different advice algorithms depending on the category of the user's concern when providing advice. Concern categories include, but are not limited to, health, work, and life, for example. For example, the providing unit can apply a career-related advice algorithm to a career-related concern. Furthermore, the providing unit can apply a health-related advice algorithm to a health-related concern. Furthermore, the providing unit can apply a relationship-related advice algorithm to a relationship-related concern. This improves the accuracy of advice by providing advice according to the category of concern.

[0048] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. Past advice results include, for example, past advice content, advice effects, etc., but are not limited to these examples. For example, the providing unit can adjust current advice based on the user's past advice results. Furthermore, the providing unit can extract specific patterns from the user's past advice results and reflect them in current advice. Furthermore, the providing unit can analyze the user's past advice results and optimize the advice algorithm. In this way, the accuracy of current advice is improved by utilizing the past advice results.

[0049] The providing unit can determine the priority of advice when providing advice based on the time when the user submitted the concern. For example, the providing unit can determine the priority of advice when providing advice based on the time when the user submitted the concern. The submission time includes, for example, the submission date and time, the submission frequency, etc., but is not limited to these examples. For example, the providing unit can give priority to advice on the most recent concern. Furthermore, the providing unit can give priority to advice on concerns submitted by the user in a specific time period. Furthermore, the providing unit can give priority to advice on concerns designated by the user as urgent. This improves the efficiency of advice by providing advice according to the submission time.

[0050] The providing unit can adjust the order of advice when providing advice based on the relevance of the user's concerns. For example, the providing unit can adjust the order of advice when providing advice based on the relevance of the user's concerns. Relevance of concerns includes, but is not limited to, for example, the degree of coincidence of topics and the frequency of related keywords. For example, the providing unit can prioritize advice on highly relevant concerns. Furthermore, the providing unit can prioritize advice on highly relevant concerns specified by the user. Furthermore, the providing unit can extract highly relevant concerns from the user's past concern history and prioritize advice on them. This improves the efficiency of advice by prioritizing advice on highly relevant concerns.

[0051] The providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. For example, the providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the providing unit may provide advice that uses a lot of technical terms. Furthermore, if the user has general knowledge, the providing unit may provide advice that uses less technical terms. Furthermore, the providing unit may provide advice that uses appropriate technical terms according to the user's level of expertise. This allows the advice to be provided according to the user's level of expertise, thereby deepening the user's understanding of the advice.

[0052] The simulation unit can improve the accuracy of the simulation by referring to the user's past behavioral data during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to the user's past behavioral data during the simulation. Examples of past behavioral data include, but are not limited to, past behavioral history and behavioral patterns. For example, the simulation unit can adjust the current simulation results based on the user's past behavioral data. Furthermore, the simulation unit can extract specific patterns from the user's past behavioral data and reflect them in the current simulation. Furthermore, the simulation unit can analyze the user's past behavioral data and optimize the simulation algorithm. Thus, by utilizing the past behavioral data, the accuracy of the simulation is improved.

[0053] The simulation unit can customize the simulation method based on the user's current situation and concerns during the simulation. The simulation unit can customize the simulation method based on the user's current situation and concerns during the simulation. The current situation and concerns include, but are not limited to, health status, work-related concerns, and life problems. For example, the simulation unit can select an appropriate simulation method based on the user's current situation (e.g., considering changing jobs, having health problems, etc.). The simulation unit can also apply different simulation methods depending on the user's concern category (e.g., career, health, relationships, etc.). Furthermore, the simulation unit can customize an optimal simulation method by comprehensively considering the user's current situation and concerns. This improves the accuracy of the simulation by providing a simulation method that suits the user's current situation and concerns.

[0054] The simulation unit can improve the simulation method by reflecting user feedback during the simulation. The simulation unit can improve the simulation method by reflecting user feedback during the simulation. Feedback includes, but is not limited to, user ratings, comments, etc. For example, the simulation unit can adjust the simulation method based on the feedback provided by the user. The simulation unit can extract specific improvements from the user's feedback and reflect them in the simulation. Furthermore, the simulation unit can analyze the user's feedback and optimize the simulation algorithm. This allows the accuracy of the simulation to be improved by utilizing the feedback.

[0055] The simulation unit can select an optimal simulation method during a simulation by taking into account the user's geographical location information. For example, the simulation unit can select an optimal simulation method during a simulation by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and the like. For example, if the user is in a specific area, the simulation unit can select a simulation method related to that area. Furthermore, if the user is traveling, the simulation unit can select a simulation method related to the user's travel destination. Furthermore, if the user is at home, the simulation unit can select a simulation method related to the user's home. This improves the accuracy of the simulation by providing a simulation based on the geographical location information.

[0056] The simulation unit can analyze the user's social media activity during the simulation and suggest a simulation method. For example, the simulation unit can analyze the user's social media activity during the simulation and suggest a simulation method. Social media activity includes, for example, but is not limited to, posted content, like history, and the like. For example, the simulation unit can suggest a related simulation method based on information shared by the user on social media. The simulation unit can also analyze the user's social media activity and suggest a related simulation method. Furthermore, the simulation unit can suggest a related simulation method by referring to the activity of the user's friends on social media. In this way, by utilizing social media activity, it is possible to provide the user with an optimal simulation method.

[0057] The simulation unit can customize the simulation method by reflecting the user's past feedback during the simulation. For example, the simulation unit can customize the simulation method by reflecting the user's past feedback during the simulation. Past feedback includes, but is not limited to, user ratings, comments, etc. For example, the simulation unit can adjust the simulation method based on feedback provided by the user in the past. The simulation unit can also extract specific improvements from the user's past feedback and reflect them in the simulation. Furthermore, the simulation unit can analyze the user's past feedback and optimize the simulation algorithm. In this way, the accuracy of the simulation can be improved by utilizing the past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The decision support system can also obtain the user's geographic location information and provide location-specific advice based on that information. For example, if the user lives in a particular area, the system can provide advice based on the local labor market and health resources. If the user is traveling, the system can provide advice based on the culture and customs of the destination. Furthermore, if the user is considering moving, the system can provide information about the living environment and job opportunities in the new area. This allows the user to make decisions that take geographic factors into account.

[0060] The decision support system can further analyze the user's social media activity and provide advice based on that information. For example, relevant advice can be provided based on information shared by the user on social media. The system can also analyze the user's social media activity and provide advice based on topics in which the user is interested. Furthermore, the system can provide useful information to the user by taking into account the activity of the user's friends on social media. This allows the system to provide more personalized advice by utilizing the user's social media activity.

[0061] The decision support system can further customize the method of providing advice by reflecting the user's past feedback. For example, it can suggest the most appropriate advice method based on the user's past feedback. It can also preferentially suggest specific advice methods based on the user's past feedback. Furthermore, it can analyze the user's past feedback and optimize the advice algorithm. In this way, it can provide the most appropriate advice method for the user by utilizing the user's past feedback.

[0062] The decision support system can further adjust the use of technical terms in the advice depending on the user's level of expertise. For example, if the user has technical expertise, advice using a lot of technical terms can be provided. On the other hand, if the user has general knowledge, advice using less technical terms can be provided. Furthermore, advice using appropriate technical terms can be provided depending on the user's level of expertise. This allows the advice to be provided according to the user's level of expertise, thereby deepening the user's understanding of the advice.

[0063] The decision support system can further improve the accuracy of the simulation by referring to the user's past behavioral data. For example, the current simulation results can be adjusted based on the user's past behavioral data. Specific patterns can also be extracted from the user's past behavioral data and reflected in the current simulation. Furthermore, the user's past behavioral data can be analyzed to optimize the simulation algorithm. Thus, by utilizing past behavioral data, the accuracy of the simulation can be improved.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit inputs the user's current situation or concerns. The user's current situation or concerns may include, for example, health status, work-related concerns, and life problems. The reception unit can accept text input, voice input, and input in the form of multiple-choice options. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze the information by referring to past user data and expert opinions. Step 3: The providing unit provides appropriate advice to the user based on the information analyzed by the analyzing unit. The advice may include health advice, career advice, etc. The providing unit may also provide specific advice to the user. Step 4: The simulation unit simulates the actions that the user should take based on the advice provided by the provision unit. The simulation is performed using methods such as the Monte Carlo method and scenario analysis. The simulation unit can also simulate the results that would be brought about by the actions that the user should take.

[0066] (Example 2) A decision support system according to an embodiment of the present invention inputs a user's current situation and concerns, and a generation AI analyzes the input, provides optimal advice, and simulates actions. In this decision support system, the user inputs their current situation and concerns, and the generation AI analyzes the input and provides optimal advice to the user. This advice is customized based on the user's past experiences and current situation. Furthermore, the generation AI suggests specific actions the user should take and simulates the potential consequences of those actions. For example, a user inputs their current situation and concerns. The user can freely input their own feelings and thoughts. For example, they can input a specific concern such as, "I'm wondering whether I should quit my job." This information is then input into the generation AI. The generation AI then analyzes the input information. The generation AI considers the user's past experiences and current situation and provides optimal advice. For example, by referring to data from users who have had similar concerns in the past, the generation AI can provide specific advice such as, "Before looking for a new job, try discussing the problems with your current job with your boss." Furthermore, the generation AI suggests specific actions the user should take. For example, it suggests actions such as, "Providing specific steps to discuss with your boss" or "Providing resources to help you find a new job." In this case, the generative AI simulates the possible outcomes of the actions the user should take and presents the results to the user. For example, it may provide a simulation result such as, "As a result of discussing with your boss, there is a high possibility that your work environment will improve." This allows the user to more clearly understand their options and make the optimal decision. In this way, the decision support system allows the user to more clearly understand their options and make the optimal decision.

[0067] A decision support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a simulation unit. The reception unit inputs a user's current situation or concerns. The user's current situation or concerns include, but are not limited to, health status, work-related concerns, and lifestyle issues. The reception unit can accept, for example, text input, voice input, and multiple-choice input. The analysis unit analyzes the information input by the reception unit. The analysis can be performed using, for example, but not limited to, data mining, statistical analysis, or a machine learning algorithm. The analysis unit can also analyze information by referring to past user data or expert opinions. The provision unit provides appropriate advice to the user based on the information analyzed by the analysis unit. The advice can include, for example, health advice, career advice, and the like, but is not limited to these examples. The provision unit can also provide specific advice to the user. The simulation unit simulates actions the user should take based on the advice provided by the provision unit. The simulation can be performed using, for example, but not limited to, a Monte Carlo method or scenario analysis. The simulation unit can also simulate the results of actions the user should take. As a result, the decision support system according to the embodiment provides optimal advice for the user's current situation and worries, and simulates actions, allowing the user to make optimal decisions.

[0068] Furthermore, the decision support system includes a reception unit that receives text input, voice input, and multiple-choice input. The reception unit can receive, for example, text input, voice input, and multiple-choice input. For example, if the user selects text input, they can input their current situation and concerns using a keyboard. If the user selects voice input, they can input their current situation and concerns by voice using a microphone. If the user selects multiple-choice input, they can select the appropriate option from the options presented by the system. This allows the user to input information in a variety of ways, improving convenience.

[0069] The analysis unit can analyze information based on past user data or expert opinions. The analysis unit can analyze information based on, for example, past user data. Past user data includes, for example, past consultation content, behavioral history, etc., but is not limited to these examples. The analysis unit can also analyze information based on expert opinions. Expert opinions include, for example, expert interviews, literature research, etc., but are not limited to these examples. In this way, by utilizing past data and expert opinions, the accuracy of the analysis is improved.

[0070] The providing unit can provide specific advice to the user. For example, the providing unit can provide specific advice to the user. Specific advice includes, but is not limited to, an action plan, provision of resources, etc. For example, the providing unit can provide specific advice to the user such as "before looking for a new job, try discussing the problems with your current job with your boss." By providing specific advice, the user can more easily implement the advice.

[0071] The simulation unit can simulate the results of actions that the user should take. For example, the simulation unit can simulate the results of actions that the user should take. Examples of simulations include, but are not limited to, predictive models, scenario analyses, and the like. For example, the simulation unit can suggest actions that the user should take, such as "providing specific steps for discussing with your boss" or "providing resources for searching for a new job," and simulate the possible results of those actions. In this way, by simulating the results of the actions, the user can clearly understand their options.

[0072] The decision support system further includes a reception unit that estimates a user's emotion and adjusts an input reception method based on the estimated user's emotion. The reception unit can, for example, estimate the user's emotion and adjust the input reception method based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Also, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input. This reduces the user's stress by providing an input method that corresponds to the user's emotion.

[0073] The reception unit can analyze the user's past input history and suggest the optimal input format. The reception unit can, for example, analyze the user's past input history and suggest the optimal input format. The past input history includes, for example, past input content, input format history, etc., but is not limited to these examples. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. In addition, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the input format to be used in a specific time period based on the user's past input history. In this way, by utilizing the past input history, the optimal input format for the user can be provided.

[0074] The reception unit can filter the input content based on the user's current situation and areas of interest when receiving the input. For example, the reception unit can filter the input content based on the user's current situation and areas of interest when receiving the input. Examples of the current situation and areas of interest include, but are not limited to, survey results and behavioral history. For example, if the user inputs concerns about their current work, the reception unit can prioritize displaying related questions and options. Furthermore, if the user has a specific area of ​​interest (e.g., career, health, etc.), the reception unit can prioritize displaying input content related to that area. Furthermore, the reception unit can filter appropriate input content based on the user's current situation (e.g., considering changing jobs, having health problems, etc.). This improves input efficiency by providing input content that matches the user's situation and areas of interest.

[0075] The reception unit can select the optimal reception means depending on the user's input method when receiving input. For example, the reception unit can select the optimal reception means depending on the user's input method (voice, text, multiple-choice, etc.) when receiving input. Input methods include, but are not limited to, voice input, text input, multiple-choice format, etc. For example, if the user selects voice input, the reception unit can analyze the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the input content in real time and provide appropriate feedback. Furthermore, if the user selects multiple-choice format input, the reception unit can automatically generate and present related options. This improves input convenience by providing the optimal reception means depending on the user's input method.

[0076] The decision support system further includes a reception unit that estimates a user's emotion and determines a priority of input contents based on the estimated user's emotion. The priority of input contents can be determined based on the user's emotions. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, and the like, but is not limited to these examples. For example, if the user is feeling anxious, the reception unit can prioritize displaying important input contents and respond quickly. Furthermore, if the user is relaxed, the reception unit can prioritize displaying detailed input contents and collect in-depth information. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying concise input contents and respond quickly. This allows the user's needs to be responded to quickly by providing a priority order of input contents according to the user's emotions.

[0077] The reception unit can prioritize accepting highly relevant inputs when receiving inputs, taking into consideration the user's geographical location information. For example, the reception unit can prioritize accepting highly relevant inputs when receiving inputs, taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, when the user is in a specific area, the reception unit can prioritize accepting information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize accepting information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize accepting information related to the user's home. This allows more relevant information to be collected by providing inputs based on the user's geographical location information.

[0078] The reception unit can analyze the user's social media activity and accept related input when receiving input. For example, the reception unit can analyze the user's social media activity and accept related input when receiving input. Social media activity includes, but is not limited to, posted content, likes, and the likes. For example, the reception unit can preferentially accept related input content based on information shared by the user on social media. Furthermore, the reception unit can analyze the user's social media activity and suggest related input content. Furthermore, the reception unit can accept related input content based on the activity of the user's friends on social media. In this way, by utilizing social media activity, input based on the user's interests can be provided.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can customize the reception method by reflecting the user's past feedback when receiving input. Past feedback includes, but is not limited to, user ratings, comments, etc. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. Furthermore, the reception unit can preferentially suggest a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input reception method. In this way, the optimal reception method for the user can be provided by utilizing the past feedback.

[0080] The decision support system further includes an analysis unit that estimates a user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. The analysis unit can, for example, estimate a user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is feeling anxious, the analysis unit can use a simple and easy-to-understand expression method. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This provides analysis expressions that correspond to the user's emotion, thereby deepening understanding of the analysis results.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information during analysis. The importance of the input information includes, but is not limited to, for example, urgency and impact. For example, the analysis unit can perform a detailed analysis of important input information. Furthermore, the analysis unit can perform a concise analysis of general input information. Furthermore, the analysis unit can perform a detailed analysis of information in which the user is particularly interested. This improves the efficiency of the analysis by providing an analysis according to the importance of the input information.

[0082] The analysis unit can apply different analysis algorithms depending on the category of input information during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of input information during analysis. Categories of input information include, but are not limited to, health, work, and lifestyle. For example, the analysis unit can apply a career-related analysis algorithm to input information related to career. Furthermore, the analysis unit can apply a health-related analysis algorithm to input information related to health. Furthermore, the analysis unit can apply a relationship-related analysis algorithm to input information related to relationships. This improves the accuracy of the analysis by providing an analysis according to the category of input information.

[0083] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, but are not limited to, past reports, analysis logs, etc. For example, the analysis unit can adjust the current analysis results based on the user's past analysis results. Furthermore, the analysis unit can extract specific patterns from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. By utilizing the past analysis results, the accuracy of the current analysis is improved.

[0084] The decision support system further includes an analysis unit that estimates a user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The analysis unit can, for example, estimate a user's emotion and adjust the length of the analysis based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. On the other hand, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can provide a simple and easy-to-understand analysis result. This allows the user to understand the analysis result better by providing the length of the analysis according to the user's emotion.

[0085] The analysis unit can determine the priority of analysis based on the submission time of the input information during analysis. For example, the analysis unit can determine the priority of analysis based on the submission time of the input information during analysis. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. For example, the analysis unit can prioritize analyzing the most recent input information. Furthermore, the analysis unit can prioritize analyzing input information submitted by the user during a specific time period. Furthermore, the analysis unit can prioritize analyzing input information designated by the user as urgent. This improves the efficiency of analysis by providing an analysis according to the submission time.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. The relevance of the input information includes, but is not limited to, for example, the degree of coincidence of topics and the frequency of related keywords. For example, the analysis unit can prioritize analyzing highly relevant input information. Furthermore, the analysis unit can prioritize analyzing highly relevant information specified by the user. Furthermore, the analysis unit can extract highly relevant information from the user's past input history and analyze it preferentially. This improves the efficiency of analysis by prioritizing analysis of highly relevant information.

[0087] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during the analysis. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, if the user has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terms. Furthermore, the analysis unit can provide analysis results that use appropriate technical terms according to the user's level of expertise. This allows the user to understand the analysis results better by providing an analysis that is appropriate for the user's level of expertise.

[0088] Furthermore, the decision support system includes a providing unit that estimates the user's emotion and adjusts the way of expressing advice based on the estimated user's emotion. The providing unit can, for example, estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. The emotion estimation is performed by, for example, but not limited to, facial expression recognition, voice analysis, text analysis, etc. For example, if the user feels anxious, the providing unit can provide simple and easy-to-understand advice. Also, if the user feels relaxed, the providing unit can provide detailed advice. Furthermore, if the user is in a hurry, the advice providing unit can provide concise advice that focuses on the main points. This allows the user to understand the advice better by providing an expression of advice that matches the user's emotions.

[0089] The providing unit can adjust the level of detail of the advice by taking into account the user's past experience and current situation when providing advice. For example, the providing unit can adjust the level of detail of the advice by taking into account the user's past experience and current situation when providing advice. Past experience and current situation include, but are not limited to, past consultation content, current health status, etc. For example, the providing unit can provide detailed advice based on the user's past experience. Furthermore, the providing unit can provide appropriate advice by taking into account the user's current situation. Furthermore, the providing unit can provide optimal advice by comprehensively taking into account the user's past experience and current situation. In this way, more appropriate advice can be provided by taking into account the user's past experience and current situation.

[0090] The providing unit can apply different advice algorithms depending on the category of the user's concern when providing advice. For example, the providing unit can apply different advice algorithms depending on the category of the user's concern when providing advice. Concern categories include, but are not limited to, health, work, and life, for example. For example, the providing unit can apply a career-related advice algorithm to a career-related concern. Furthermore, the providing unit can apply a health-related advice algorithm to a health-related concern. Furthermore, the providing unit can apply a relationship-related advice algorithm to a relationship-related concern. This improves the accuracy of advice by providing advice according to the category of concern.

[0091] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. Past advice results include, for example, past advice content, advice effects, etc., but are not limited to these examples. For example, the providing unit can adjust current advice based on the user's past advice results. Furthermore, the providing unit can extract specific patterns from the user's past advice results and reflect them in current advice. Furthermore, the providing unit can analyze the user's past advice results and optimize the advice algorithm. In this way, the accuracy of current advice is improved by utilizing the past advice results.

[0092] The decision support system further includes a providing unit that estimates a user's emotion and adjusts the length of advice based on the estimated user's emotion. The providing unit can, for example, estimate a user's emotion and adjust the length of advice based on the estimated user's emotion. The emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is in a hurry, the providing unit can provide short, to-the-point advice. On the other hand, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is feeling anxious, the providing unit can provide simple, easy-to-understand advice. As a result, by providing the length of advice according to the user's emotion, the advice can be better understood.

[0093] The providing unit can determine the priority of advice when providing advice based on the time when the user submitted the concern. For example, the providing unit can determine the priority of advice when providing advice based on the time when the user submitted the concern. The submission time includes, for example, the submission date and time, the submission frequency, etc., but is not limited to these examples. For example, the providing unit can give priority to advice on the most recent concern. Furthermore, the providing unit can give priority to advice on concerns submitted by the user in a specific time period. Furthermore, the providing unit can give priority to advice on concerns designated by the user as urgent. This improves the efficiency of advice by providing advice according to the submission time.

[0094] The providing unit can adjust the order of advice when providing advice based on the relevance of the user's concerns. For example, the providing unit can adjust the order of advice when providing advice based on the relevance of the user's concerns. Relevance of concerns includes, but is not limited to, for example, the degree of coincidence of topics and the frequency of related keywords. For example, the providing unit can prioritize advice on highly relevant concerns. Furthermore, the providing unit can prioritize advice on highly relevant concerns specified by the user. Furthermore, the providing unit can extract highly relevant concerns from the user's past concern history and prioritize advice on them. This improves the efficiency of advice by prioritizing advice on highly relevant concerns.

[0095] The providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. For example, the providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the providing unit may provide advice that uses a lot of technical terms. Furthermore, if the user has general knowledge, the providing unit may provide advice that uses less technical terms. Furthermore, the providing unit may provide advice that uses appropriate technical terms according to the user's level of expertise. This allows the advice to be provided according to the user's level of expertise, thereby deepening the user's understanding of the advice.

[0096] The decision support system further includes a simulation unit that estimates a user's emotion and adjusts the display method of the simulation based on the estimated user's emotion. The simulation unit can, for example, estimate the user's emotion and adjust the display method of the simulation based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is feeling anxious, the simulation unit can provide a simple and easy-to-understand display method. If the user is relaxed, the simulation unit can provide detailed simulation results. If the user is in a hurry, the simulation unit can provide concise simulation results that focus on the main points. This allows the user to better understand the simulation results by providing a simulation display that corresponds to the user's emotion.

[0097] The simulation unit can improve the accuracy of the simulation by referring to the user's past behavioral data during the simulation. For example, the simulation unit can improve the accuracy of the simulation by referring to the user's past behavioral data during the simulation. Examples of past behavioral data include, but are not limited to, past behavioral history and behavioral patterns. For example, the simulation unit can adjust the current simulation results based on the user's past behavioral data. Furthermore, the simulation unit can extract specific patterns from the user's past behavioral data and reflect them in the current simulation. Furthermore, the simulation unit can analyze the user's past behavioral data and optimize the simulation algorithm. Thus, by utilizing the past behavioral data, the accuracy of the simulation is improved.

[0098] The simulation unit can customize the simulation method based on the user's current situation and concerns during the simulation. The simulation unit can customize the simulation method based on the user's current situation and concerns during the simulation. The current situation and concerns include, but are not limited to, health status, work-related concerns, and life problems. For example, the simulation unit can select an appropriate simulation method based on the user's current situation (e.g., considering changing jobs, having health problems, etc.). The simulation unit can also apply different simulation methods depending on the user's concern category (e.g., career, health, relationships, etc.). Furthermore, the simulation unit can customize an optimal simulation method by comprehensively considering the user's current situation and concerns. This improves the accuracy of the simulation by providing a simulation method that suits the user's current situation and concerns.

[0099] The simulation unit can improve the simulation method by reflecting user feedback during the simulation. The simulation unit can improve the simulation method by reflecting user feedback during the simulation. Feedback includes, but is not limited to, user ratings, comments, etc. For example, the simulation unit can adjust the simulation method based on the feedback provided by the user. The simulation unit can extract specific improvements from the user's feedback and reflect them in the simulation. Furthermore, the simulation unit can analyze the user's feedback and optimize the simulation algorithm. This allows the accuracy of the simulation to be improved by utilizing the feedback.

[0100] The decision support system further includes a simulation unit that estimates a user's emotions and prioritizes simulations based on the estimated user emotions. The simulation unit can, for example, estimate a user's emotions and prioritize simulations based on the estimated user emotions. The emotion estimation can be performed using, for example, facial expression recognition, voice analysis, text analysis, or other methods, but is not limited to these examples. For example, if the user is feeling anxious, the simulation unit can prioritize displaying important simulation results. Furthermore, if the user is relaxed, the simulation unit can prioritize displaying detailed simulation results. Furthermore, if the user is in a hurry, the simulation unit can prioritize displaying concise simulation results. This improves simulation efficiency by providing simulation priorities according to the user's emotions.

[0101] The simulation unit can select an optimal simulation method during a simulation by taking into account the user's geographical location information. For example, the simulation unit can select an optimal simulation method during a simulation by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, and the like. For example, if the user is in a specific area, the simulation unit can select a simulation method related to that area. Furthermore, if the user is traveling, the simulation unit can select a simulation method related to the user's travel destination. Furthermore, if the user is at home, the simulation unit can select a simulation method related to the user's home. This improves the accuracy of the simulation by providing a simulation based on the geographical location information.

[0102] The simulation unit can analyze the user's social media activity during the simulation and suggest a simulation method. For example, the simulation unit can analyze the user's social media activity during the simulation and suggest a simulation method. Social media activity includes, for example, but is not limited to, posted content, like history, and the like. For example, the simulation unit can suggest a related simulation method based on information shared by the user on social media. The simulation unit can also analyze the user's social media activity and suggest a related simulation method. Furthermore, the simulation unit can suggest a related simulation method by referring to the activity of the user's friends on social media. In this way, by utilizing social media activity, it is possible to provide the user with an optimal simulation method.

[0103] The simulation unit can customize the simulation method by reflecting the user's past feedback during the simulation. For example, the simulation unit can customize the simulation method by reflecting the user's past feedback during the simulation. Past feedback includes, but is not limited to, user ratings, comments, etc. For example, the simulation unit can adjust the simulation method based on feedback provided by the user in the past. The simulation unit can also extract specific improvements from the user's past feedback and reflect them in the simulation. Furthermore, the simulation unit can analyze the user's past feedback and optimize the simulation algorithm. In this way, the accuracy of the simulation can be improved by utilizing the past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and simulation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and can input the user's current situation and concerns. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the analysis results. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates actions that the user should take. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and simulation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and can input the user's current situation and concerns. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the analysis results. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates actions that the user should take. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and simulation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and can input the user's current situation and concerns. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the analysis results. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates actions that the user should take. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and simulation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and can input the user's current situation and concerns. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice to the user based on the analysis results. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and simulates actions that the user should take.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The decision support system can also obtain the user's geographic location information and provide location-specific advice based on that information. For example, if the user lives in a particular area, the system can provide advice based on the local labor market and health resources. If the user is traveling, the system can provide advice based on the culture and customs of the destination. Furthermore, if the user is considering moving, the system can provide information about the living environment and job opportunities in the new area. This allows the user to make decisions that take geographic factors into account.

[0106] The decision support system can further analyze the user's social media activity and provide advice based on that information. For example, relevant advice can be provided based on information shared by the user on social media. The system can also analyze the user's social media activity and provide advice based on topics in which the user is interested. Furthermore, the system can provide useful information to the user by taking into account the activity of the user's friends on social media. This allows the system to provide more personalized advice by utilizing the user's social media activity.

[0107] The decision support system can further customize the method of providing advice by reflecting the user's past feedback. For example, it can suggest the most appropriate advice method based on the user's past feedback. It can also preferentially suggest specific advice methods based on the user's past feedback. Furthermore, it can analyze the user's past feedback and optimize the advice algorithm. In this way, it can provide the most appropriate advice method for the user by utilizing the user's past feedback.

[0108] The decision support system can further adjust the use of technical terms in the advice depending on the user's level of expertise. For example, if the user has technical expertise, advice using a lot of technical terms can be provided. On the other hand, if the user has general knowledge, advice using less technical terms can be provided. Furthermore, advice using appropriate technical terms can be provided depending on the user's level of expertise. This allows the advice to be provided according to the user's level of expertise, thereby deepening the user's understanding of the advice.

[0109] The decision support system can further improve the accuracy of the simulation by referring to the user's past behavioral data. For example, the current simulation results can be adjusted based on the user's past behavioral data. Specific patterns can also be extracted from the user's past behavioral data and reflected in the current simulation. Furthermore, the user's past behavioral data can be analyzed to optimize the simulation algorithm. Thus, by utilizing past behavioral data, the accuracy of the simulation can be improved.

[0110] The decision support system can further estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. For example, if the user is feeling anxious, simple and easy-to-understand advice can be provided. If the user is relaxed, detailed advice can be provided. If the user is in a hurry, concise advice that focuses on the main points can be provided. This allows the advice to be presented in a way that suits the user's emotions, thereby deepening understanding of the advice.

[0111] The decision support system can further estimate the user's emotions and adjust the display method of the simulation based on the estimated user emotions. For example, if the user is feeling anxious, a simple and easy-to-understand display method can be provided. If the user is relaxed, detailed simulation results can be provided. If the user is in a hurry, concise simulation results that focus on the main points can be provided. In this way, providing a simulation display that corresponds to the user's emotions deepens the user's understanding of the simulation results.

[0112] The decision support system can further estimate the user's emotions and determine the priority of simulations based on the estimated user's emotions. For example, if the user feels anxious, important simulation results can be displayed with priority. If the user feels relaxed, detailed simulation results can be displayed with priority. If the user is in a hurry, brief simulation results can be displayed with priority. This improves the efficiency of simulations by providing simulation priorities according to the user's emotions.

[0113] The decision support system can further estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling anxious, a simple and easy-to-understand presentation can be used. If the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. This allows the analysis results to be presented in accordance with the user's emotions, thereby deepening understanding of the analysis results.

[0114] The decision support system can further estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, it can provide a short and concise analysis result. If the user is relaxed, it can provide a detailed analysis result. If the user is anxious, it can provide a simple and easy-to-understand analysis result. By providing the length of the analysis according to the user's emotions, the user can better understand the analysis results.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit inputs the user's current situation or concerns. The user's current situation or concerns may include, for example, health status, work-related concerns, and life problems. The reception unit can accept text input, voice input, and input in the form of multiple-choice options. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze the information by referring to past user data and expert opinions. Step 3: The providing unit provides appropriate advice to the user based on the information analyzed by the analyzing unit. The advice may include health advice, career advice, etc. The providing unit may also provide specific advice to the user. Step 4: The simulation unit simulates the actions that the user should take based on the advice provided by the provision unit. The simulation is performed using methods such as the Monte Carlo method and scenario analysis. The simulation unit can also simulate the results that would be brought about by the actions that the user should take.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting the user's current situation or worries; an analysis unit that analyzes the information input by the reception unit; a providing unit that provides appropriate advice to a user based on the information analyzed by the analyzing unit; a simulation unit that simulates an action that a user should take based on the advice provided by the providing unit; Equipped with A system characterized by:

2. The reception unit Accepts text input, voice input, and multiple choice input 2. The system of claim 1.

3. The analysis unit Analyze information based on past user data or expert opinion 2. The system of claim 1.

4. The providing unit Providing specific advice to users 2. The system of claim 1.

5. The simulation unit Simulate the consequences of user actions 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the input reception method based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's past input history and suggests the optimal input format 2. The system of claim 1.

8. The reception unit As input is accepted, filter it based on the user's current context and interests.

2. The system of claim 1.

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