System

Abstract

To provide a system according to an embodiment that provides an optimized advice for an individual, and satisfies an acknowledgement desire of a user.SOLUTION: A system according to an embodiment comprises: a reception unit; a generation unit; a providing unit; and a collection unit. The reception unit is configured to receive an input of a user. The generation unit is configured to generate a specific advice on the basis of information received by the reception unit. The providing unit is configured to provide the advice generated by the generation unit. The collection unit is configured to collect a reaction of the user with respect to the advice provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional techniques have had the problem that they do not adequately provide advice optimized for individuals and satisfy users' desire for recognition.

[0005] The system according to the embodiment aims to provide personalized advice and satisfy the user's need for recognition. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a collection unit. The reception unit receives a user's input. The generation unit generates a specific advice based on the information received by the reception unit. The provision unit provides the advice generated by the generation unit. The collection unit collects the user's reaction to the advice provided by the provision unit. Effect of the Invention

[0007] The system according to the embodiment can provide personalized advice to satisfy the user's need for recognition. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that satisfies the desire for recognition by providing advice from a great person that is optimized for an individual. In this system, when a user consults or shares his / her ideas or actions, the generation AI generates advice from a great person and responds with words of encouragement or praise. Next, the user sets a goal, and the generation AI provides words of support and recognition for the progress and results. Furthermore, when the user shares his / her feelings and experiences, the generation AI shows empathic reactions and understanding. Finally, the generation AI provides advice to help improve the user's skills, knowledge, and expertise. For example, a user consults or shares his / her ideas or actions. At this time, the user inputs his / her thoughts and actions. For example, the user inputs content such as "I want to start a new project." This information is input to the generation AI. Next, the generation AI analyzes the input information and generates advice from a great person. The generation AI responds to the user's thoughts and actions with words of encouragement and praise. For example, a response such as "Your idea is great. Please give it a try" is generated. Furthermore, the user sets a goal, and the generation AI provides words of support and recognition for the progress and results. For example, it generates a response such as "You're making good progress toward your goal. That's great." In addition, when a user shares their feelings or experiences, the generative AI responds with empathy and understanding. For example, if you input something like "I've been feeling stressed at work lately," the generative AI will generate a response such as "That's tough. I understand how you feel." Finally, the generative AI provides advice to help improve the user's skills, knowledge, and expertise. For example, if you input something like "I want to improve my programming skills," the generative AI will provide advice such as "Let's start by learning basic algorithms." This mechanism allows users to satisfy their desire for recognition through the advice of great people and promote their own self-growth. This allows the system to satisfy the user's desire for recognition and promote their own self-growth.

[0029] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a collection unit. The reception unit receives a user's input. The user's input includes, for example, ideas, actions, emotions, goals, and the like, but is not limited to such examples. The reception unit can receive input in, for example, a text format, an audio format, a video format, and the like. The generation unit generates advice of a great person based on the information received by the reception unit. The generation unit generates appropriate advice for the user's input, for example, using a generation AI. The generation AI generates advice based on the user's input, using a text generation AI (for example, LLM) or a multimodal generation AI. For example, the generation AI generates a response such as "Your idea is great. Please give it a try" in response to the user's input. The provision unit provides the advice generated by the generation unit. The provision unit can provide the advice in, for example, a text format, an audio format, a video format, and the like. For example, the provision unit transmits the generated advice to the user as a text message. The provision unit can also provide the advice as an audio message. The collection unit collects the user's feedback on the advice provided by the provision unit. The collection unit can collect the user's reaction in the form of, for example, a click, a comment, a rating, etc. For example, when the user rates the provided advice as "helpful," the collection unit collects the rating. In this way, the system according to the embodiment can satisfy the user's desire for recognition by accepting the user's input, generating and providing advice of a great person, and collecting feedback.

[0030] The reception unit receives input from a user. The user's input includes, but is not limited to, for example, ideas, actions, emotions, goals, and the like. The reception unit can receive input in, for example, text format, voice format, video format, and the like. Specifically, the text format input receives a sentence input by the user using a keyboard or a message input by the user using a touch screen of a smartphone. The voice format input converts what the user speaks through a microphone into text using voice recognition technology and imports it into the system. The video format input analyzes the video recorded by the user through a camera and extracts necessary information from the voice and video. This allows the reception unit to flexibly receive input in various formats, thereby improving user convenience. Furthermore, the reception unit preprocesses the input content, removes noise, and standardizes the format, so that the generation unit can efficiently generate advice. For example, in the case of voice input, filtering is performed to remove background noise and improve the accuracy of voice recognition. In the case of video input, the resolution of the video is adjusted and only the necessary parts are extracted to improve the efficiency of processing. This allows the reception unit to process various user inputs with high precision and efficiency, thereby improving the performance of the entire system.

[0031] The generation unit generates advice of a great man based on the information received by the reception unit. The generation unit generates appropriate advice for the user's input, for example, using a generation AI. The generation AI generates advice based on the user's input, using a text generation AI (for example, LLM) or a multimodal generation AI. Specifically, the text generation AI has the ability to learn large amounts of text data and generate natural sentences for the user's input. For example, if a user inputs "I'm thinking of a new business idea," the generation AI generates advice such as "That idea is very interesting. It is important to conduct market research and clarify the target customers." The multimodal generation AI generates advice by integrating multiple data formats such as voice and images as well as text. For example, if a user inputs "I've been feeling stressed at work lately" in video format, the generation AI analyzes the user's facial expressions and tone of voice and generates advice such as "I recommend that you take a little time each day to take a deep breath to relax." This allows the generation unit to provide appropriate and personalized advice for the user's diverse input. In addition, the generation unit also has a function of evaluating the quality of the generated advice and making corrections and improvements as necessary. For example, if the generated advice does not meet the user's expectations, the AI ​​will continue to learn based on the feedback and reflect that in the next advice generation. This allows the generator to consistently provide high-quality advice and improve user satisfaction.

[0032] The providing unit provides the advice generated by the generating unit. The providing unit can provide the advice in a format such as text, audio, or video. Specifically, the advice in text format is sent to the user's device as a message, and the user can read it. The advice in audio format is provided as an audio message generated using speech synthesis technology, and the user can listen to it. The advice in video format is provided visually using animation or video, and the user can watch it. For example, the providing unit transmits the generated advice to the user as a text message. The providing unit can also provide the advice as an audio message. Furthermore, the providing unit can provide the advice in an optimal format according to the user's preferences and circumstances. For example, advice is provided in audio format to a user who is driving, and advice is provided in text format to a user who is doing desk work. This allows the providing unit to maximize the convenience of the user. Furthermore, the providing unit also has a function of managing the advice provision history and allowing the user to refer to advice received in the past at any time. This allows the user to look back on past advice and use it as a reference for self-improvement. The providing unit can also collect data regarding the provision of advice and use it as basic data for evaluating the performance of the entire system. This enables the providing unit to provide advice to the user effectively and efficiently, thereby improving the performance of the entire system.

[0033] The collection unit collects the user's feedback on the advice provided by the provision unit. The collection unit can collect the user's reaction in the form of, for example, clicks, comments, ratings, and the like. Specifically, if the user evaluates the provided advice as "useful," the collection unit collects the evaluation. In addition, if the user leaves a comment on the advice, the collection unit also collects the comment. Furthermore, the collection unit can analyze the user's behavioral data and evaluate the effectiveness of the advice. For example, if the user takes action in accordance with the advice, the collection unit collects the behavioral data and evaluates the effectiveness of the advice. This allows the collection unit to grasp in detail how the provided advice has affected the user. Furthermore, the collection unit provides data for improving the functions of the generation unit and the provision unit based on the collected feedback. For example, the collection unit analyzes the user's feedback, evaluates the quality of the generated advice, and updates the learning data of the generation AI as necessary. In addition, the collection unit reviews the method and timing of the provision unit and makes improvements to improve the convenience of the user. This allows the collection unit to continuously improve the performance of the entire system and increase the satisfaction of the user. Furthermore, the collection unit also has a mechanism for anonymizing the feedback data and utilizing the data while protecting privacy. This allows users to provide feedback with confidence and allows the system to continue to improve based on high-quality data.

[0034] The generation unit may have a function for the user to select a specific great person. The generation unit may provide, for example, an interface for the user to select a specific great person. For example, the generation unit may display a list from which the user can select from historical figures and modern celebrities. By the user selecting a specific great person, the generation unit may generate advice based on the characteristics of the great person. For example, if the great person selected by the user is a scientist, the generation unit may generate advice including a scientific approach. Also, if the great person selected by the user is an artist, the generation unit may generate advice including a creative approach. This allows the user to provide more personalized advice by selecting a specific great person. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input the characteristics of the great person selected by the user to AI and generate advice based on the characteristics.

[0035] The generation unit can have a function of learning past input data. The generation unit has, for example, an algorithm for learning past input data. For example, the generation unit can collect input data of the user for the past year and improve the accuracy of advice based on the data. The generation unit can also learn data of a specific category. For example, the generation unit can learn data related to a project input by the user in the past and generate advice related to the project based on the data. As a result, the accuracy of advice is improved by learning the past input data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past input data into AI and generate advice based on the data.

[0036] The providing unit may have a function of providing a specific action plan. The providing unit may provide, for example, a specific action plan to a user. For example, the providing unit may provide a step-by-step guide or a checklist. When a user sets a goal, the providing unit may provide a specific action plan for achieving the goal. For example, the providing unit may present a specific step such as "Let's start by learning basic algorithms." This allows the user's behavior to be specifically supported by providing a specific action plan. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit may input a user's goal into AI and generate a specific action plan based on the goal.

[0037] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, includes an algorithm for analyzing the user's past input history. For example, the reception unit can collect the user's input history for the past year and select the optimal reception method based on the data. The reception unit can also analyze the input history of a specific category. For example, the reception unit can preferentially suggest an input method that the user has frequently used in the past. Furthermore, the reception unit can select the most efficient input method from the user's past input history. This allows the optimal reception method to be selected by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and select the optimal reception method based on the data.

[0038] The reception unit can filter the input contents based on the user's current situation or field of interest. The reception unit includes, for example, an algorithm for grasping the user's current situation or field of interest. For example, the reception unit can collect information such as the user's time zone, location, and activity content, and filter the input contents based on the information. If the user is at work, the reception unit can prioritize work-related input contents. Also, if the user is spending time on a hobby, the reception unit can prioritize input contents related to the hobby. Furthermore, if the user is on vacation, the reception unit can prioritize relaxing content. This makes it possible to accept more relevant inputs by filtering the input contents based on the user's current situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and field of interest into AI, and filter the input contents based on the data.

[0039] The reception unit can preferentially receive inputs with high relevance based on the user's location information. The reception unit includes, for example, a technology for acquiring the user's geographic location information. For example, the reception unit can acquire the user's location information using GPS data or an IP address. When the user is in a specific location, the reception unit can prioritize inputs related to the location. When the user is traveling, the reception unit can prioritize inputs related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputs related to the home. In this way, by taking into account the user's geographic location information, it is possible to preferentially accept inputs with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's location information to AI and select inputs with high relevance based on the information.

[0040] The reception unit can analyze the user's online activities and receive relevant inputs. The reception unit includes, for example, an algorithm for analyzing the user's social media activities and website browsing history. For example, the reception unit can suggest inputs based on the content the user has shared on social media and the topics the user follows. The reception unit can also prompt the user for input based on the user's social media activity time. In this way, the reception unit can accept relevant inputs by analyzing the user's social media activities. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and select relevant inputs based on the data.

[0041] The generation unit can adjust the level of detail of the advice based on the user's past actions and achievements when generating the advice. The generation unit includes, for example, an algorithm for analyzing the user's past actions and achievements. For example, the generation unit can provide specific advice based on projects in which the user has been successful in the past. In addition, the generation unit can provide advice including improvements in consideration of the user's past failures. Furthermore, the generation unit can suggest a next step based on the user's past achievements. As a result, more specific advice can be provided by adjusting the level of detail of the advice based on the user's past actions and achievements. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past action data into AI and adjust the level of detail of the advice based on the data.

[0042] The generation unit can apply different advice algorithms according to the characteristics of the great person selected by the user when generating advice. The generation unit has a function for applying different advice algorithms based on the characteristics of the great person selected by the user, for example. For example, if the great person selected by the user is a scientist, the generation unit can generate advice including a scientific approach. Also, if the great person selected by the user is an artist, advice including a creative approach can be generated. Furthermore, if the great person selected by the user is a leader, advice regarding leadership can be generated. This allows more personalized advice to be provided by applying different advice algorithms according to the characteristics of the great person selected by the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the characteristics of the great person selected by the user into AI and generate advice based on the characteristics.

[0043] The generating unit can determine the priority of advice based on the input time of the user when generating advice. The generating unit includes, for example, an algorithm for considering the input time of the user. For example, when the user inputs in the morning, the generating unit can prioritize advice to make a plan for the day. Also, when the user inputs in the evening, the generating unit can prioritize advice to review the day. Furthermore, when the user inputs on the weekend, the generating unit can prioritize advice to make a plan for the week. In this way, by determining the priority of advice based on the input time of the user, more appropriate advice can be provided. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the input time data of the user to AI and determine the priority of advice based on the data.

[0044] The generating unit can adjust the order of advice based on the relevance of the user when generating advice. The generating unit includes, for example, an algorithm for considering the relevance of the user. For example, the generating unit can prioritize advice related to a specific project by the user. Also, the generating unit can prioritize advice related to a specific skill by the user. Furthermore, the generating unit can prioritize advice related to a specific goal by the user. This allows more appropriate advice to be provided by adjusting the order of advice based on the relevance of the user. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can input the relevance data of the user into AI and adjust the order of advice based on the data.

[0045] When providing advice, the providing unit can select the optimal providing method by referring to the user's past feedback. The providing unit includes, for example, an algorithm for analyzing the user's past feedback. For example, the providing unit can prioritize the providing method that the user has preferred in the past. The providing unit can also select the most effective providing method from the user's past feedback. Furthermore, the providing unit can eliminate the providing method that the user has avoided in the past. This allows the optimal providing method to be selected by referring to the user's past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI and select the optimal providing method based on the data.

[0046] The providing unit can customize the means of providing advice based on the user's current situation when providing advice. The providing unit includes, for example, an algorithm for grasping the user's current situation. For example, the providing unit can collect information such as the user's time zone, location, and activity content, and customize the means of providing advice based on the information. When the user is at work, the providing unit can provide advice in the form of a short message. Also, when the user is on vacation, the providing unit can provide advice including detailed explanations. Furthermore, when the user is on the move, the providing unit can provide advice by voice. As a result, more appropriate advice can be provided by customizing the means of providing advice based on the user's current situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into AI and customize the means of providing advice based on the data.

[0047] The providing unit can select the optimal providing method by considering the geographical location information of the user when providing advice. The providing unit includes, for example, a technology for acquiring the geographical location information of the user. For example, the providing unit can acquire the user's location information using GPS data or an IP address. When the user is in a specific location, the providing unit can provide advice related to the location. When the user is traveling, the providing unit can provide advice related to the travel destination. Furthermore, when the user is at home, the providing unit can provide advice related to the home. This allows the optimal providing method to be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information into AI and select the optimal providing method based on the information.

[0048] The providing unit can analyze the user's social media activity and suggest a means of providing the advice when providing the advice. The providing unit includes, for example, an algorithm for analyzing the user's social media activity. For example, the providing unit can provide advice based on the content shared by the user on social media. In addition, the providing unit can provide advice related to topics the user follows on social media. Furthermore, the providing unit can provide advice based on the time the user is active on social media. In this way, the optimal means of providing the advice can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and suggest the optimal means of providing the advice based on the data.

[0049] The collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. The collection unit includes, for example, an algorithm for analyzing the user's past feedback history. For example, the collection unit can prioritize collection methods that the user has preferred in the past. The collection unit can also select the most effective collection method from the user's past feedback history. Furthermore, the collection unit can eliminate collection methods that the user has avoided in the past. This allows the optimal collection method to be selected by referring to the user's past feedback history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and select the optimal collection method based on the data.

[0050] The collection unit can customize the collection means based on the user's current situation when collecting feedback. The collection unit includes, for example, an algorithm for grasping the user's current situation. For example, the collection unit can collect information such as the user's time zone, location, and activity content, and customize the collection means based on the information. If the user is at work, the collection unit can ask for feedback in the form of a short message. Also, if the user is on vacation, the collection unit can ask for detailed feedback. Furthermore, if the user is on the move, the collection unit can ask for feedback by voice. This allows more appropriate feedback to be collected by customizing the collection means based on the user's current situation. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's current situation data into AI and customize the collection means based on the data.

[0051] The collection unit can select the optimal collection method in consideration of the geographical location information of the user when collecting feedback. The collection unit includes, for example, a technology for acquiring the geographical location information of the user. For example, the collection unit can acquire the user's location information using GPS data or an IP address. When the user is in a specific location, the collection unit can collect feedback related to the location. When the user is traveling, the collection unit can collect feedback related to the travel destination. Furthermore, when the user is at home, the collection unit can collect feedback related to the home. This allows the optimal collection method to be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location information into AI and select the optimal collection method based on the information.

[0052] The collection unit can analyze the user's social media activity and suggest a collection means when collecting feedback. The collection unit includes, for example, an algorithm for analyzing the user's social media activity. For example, the collection unit can collect feedback based on the content shared by the user on social media. The collection unit can also collect feedback related to topics the user follows on social media. Furthermore, the collection unit can collect feedback based on the user's activity time on social media. This makes it possible to suggest an optimal collection means by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and suggest an optimal collection means based on the data.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can automatically search for related past advice based on the user's input content and present it to the user. For example, if the user inputs "I want to start a new project," the reception unit can search for advice generated at the time of a similar input in the past and present it to the user. This allows the user to refer to past advice and receive more effective advice. The reception unit can also save a history of advice the user has received in the past, and provide advice based on that history when the user inputs the same content again. Furthermore, the reception unit can preferentially present the most highly rated advice based on evaluations of advice the user has received in the past.

[0055] The generator can automatically search for related external resources based on the user's input and include them in the advice. For example, if the user inputs "I want to improve my programming skills," the generator can search for related online courses and tutorials and include links to them in the advice. This allows the user to improve their skills using specific resources. The generator can also search for books and interviews of famous people selected by the user and reflect the contents of those in the advice. Furthermore, the generator can search for the latest research papers and news articles related to the user's field of interest and include them in the advice.

[0056] The generator may suggest relevant communities and forums based on the user's input. For example, if the user inputs "I want to start a new project," the generator may search for relevant online communities and forums and include links to them in the recommendation. This allows the user to interact with and share information with people who share the same interests. The generator may also suggest communities and forums in which the great person selected by the user participated. Additionally, the generator may suggest events and workshops related to the user's field of interest.

[0057] The provider can suggest relevant video content based on the user's input. For example, if the user inputs "I want to improve my programming skills," the provider can search for relevant video tutorials and webinars and include links to them in the advice. This allows the user to learn visually. The provider can also suggest documentaries and interview videos related to the great person selected by the user. Furthermore, the provider can suggest YouTube® channels and video series related to the user's field of interest.

[0058] The reception unit can suggest related books and articles based on the user's input. For example, if the user inputs "I want to start a new project," the reception unit can search for related books and articles and include links to them in the advice. This allows the user to learn more deeply. The reception unit can also suggest works and biographies of great people selected by the user. Furthermore, the reception unit can suggest specialized books and academic papers related to the user's field of interest.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The reception unit receives a user input. The user input includes, but is not limited to, ideas, actions, emotions, goals, etc. The reception unit can receive input in, for example, text format, audio format, video format, etc. Step 2: The generation unit generates advice from a great person based on the information received by the reception unit. The generation unit generates appropriate advice for the user's input, for example, using a generation AI. The generation AI generates advice based on the user's input, using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates a response to the user's input, such as, "Your idea is great. Please give it a try." Step 3: The providing unit provides the advice generated by the generating unit. The providing unit can provide the advice in a format such as a text format, an audio format, or a video format. For example, the providing unit transmits the generated advice to the user as a text message. The providing unit can also provide the advice as an audio message. Step 4: The collecting unit collects user feedback on the advice provided by the providing unit. The collecting unit can collect user responses in the form of, for example, clicks, comments, ratings, etc. For example, if a user rates the provided advice as "helpful," the collecting unit collects the rating.

[0061] (Example 2) The system according to the embodiment of the present invention is a system that satisfies the desire for recognition by providing advice from a great person that is optimized for an individual. In this system, when a user consults or shares his / her ideas or actions, the generation AI generates advice from a great person and responds with words of encouragement or praise. Next, the user sets a goal, and the generation AI provides words of support and recognition for the progress and results. Furthermore, when the user shares his / her feelings and experiences, the generation AI shows empathic reactions and understanding. Finally, the generation AI provides advice to help improve the user's skills, knowledge, and expertise. For example, a user consults or shares his / her ideas or actions. At this time, the user inputs his / her thoughts and actions. For example, the user inputs content such as "I want to start a new project." This information is input to the generation AI. Next, the generation AI analyzes the input information and generates advice from a great person. The generation AI responds to the user's thoughts and actions with words of encouragement and praise. For example, a response such as "Your idea is great. Please give it a try" is generated. Furthermore, the user sets a goal, and the generation AI provides words of support and recognition for the progress and results. For example, it generates a response such as "You're making good progress toward your goal. That's great." In addition, when a user shares their feelings or experiences, the generative AI responds with empathy and understanding. For example, if you input something like "I've been feeling stressed at work lately," the generative AI will generate a response such as "That's tough. I understand how you feel." Finally, the generative AI provides advice to help improve the user's skills, knowledge, and expertise. For example, if you input something like "I want to improve my programming skills," the generative AI will provide advice such as "Let's start by learning basic algorithms." This mechanism allows users to satisfy their desire for recognition through the advice of great people and promote their own self-growth. This allows the system to satisfy the user's desire for recognition and promote their own self-growth.

[0062] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a collection unit. The reception unit receives a user's input. The user's input includes, for example, ideas, actions, emotions, goals, and the like, but is not limited to such examples. The reception unit can receive input in, for example, a text format, an audio format, a video format, and the like. The generation unit generates advice of a great person based on the information received by the reception unit. The generation unit generates appropriate advice for the user's input, for example, using a generation AI. The generation AI generates advice based on the user's input, using a text generation AI (for example, LLM) or a multimodal generation AI. For example, the generation AI generates a response such as "Your idea is great. Please give it a try" in response to the user's input. The provision unit provides the advice generated by the generation unit. The provision unit can provide the advice in, for example, a text format, an audio format, a video format, and the like. For example, the provision unit transmits the generated advice to the user as a text message. The provision unit can also provide the advice as an audio message. The collection unit collects the user's feedback on the advice provided by the provision unit. The collection unit can collect the user's reaction in the form of, for example, a click, a comment, a rating, etc. For example, when the user rates the provided advice as "helpful," the collection unit collects the rating. In this way, the system according to the embodiment can satisfy the user's desire for recognition by accepting the user's input, generating and providing advice of a great person, and collecting feedback.

[0063] The reception unit receives input from a user. The user's input includes, but is not limited to, for example, ideas, actions, emotions, goals, and the like. The reception unit can receive input in, for example, text format, voice format, video format, and the like. Specifically, the text format input receives a sentence input by the user using a keyboard or a message input by the user using a touch screen of a smartphone. The voice format input converts what the user speaks through a microphone into text using voice recognition technology and imports it into the system. The video format input analyzes the video recorded by the user through a camera and extracts necessary information from the voice and video. This allows the reception unit to flexibly receive input in various formats, thereby improving user convenience. Furthermore, the reception unit preprocesses the input content, removes noise, and standardizes the format, so that the generation unit can efficiently generate advice. For example, in the case of voice input, filtering is performed to remove background noise and improve the accuracy of voice recognition. In the case of video input, the resolution of the video is adjusted and only the necessary parts are extracted to improve the efficiency of processing. This allows the reception unit to process various user inputs with high precision and efficiency, thereby improving the performance of the entire system.

[0064] The generation unit generates advice of a great man based on the information received by the reception unit. The generation unit generates appropriate advice for the user's input, for example, using a generation AI. The generation AI generates advice based on the user's input, using a text generation AI (for example, LLM) or a multimodal generation AI. Specifically, the text generation AI has the ability to learn large amounts of text data and generate natural sentences for the user's input. For example, if a user inputs "I'm thinking of a new business idea," the generation AI generates advice such as "That idea is very interesting. It is important to conduct market research and clarify the target customers." The multimodal generation AI generates advice by integrating multiple data formats such as voice and images as well as text. For example, if a user inputs "I've been feeling stressed at work lately" in video format, the generation AI analyzes the user's facial expressions and tone of voice and generates advice such as "I recommend that you take a little time each day to take a deep breath to relax." This allows the generation unit to provide appropriate and personalized advice for the user's diverse input. In addition, the generation unit also has a function of evaluating the quality of the generated advice and making corrections and improvements as necessary. For example, if the generated advice does not meet the user's expectations, the AI ​​will continue to learn based on the feedback and reflect that in the next advice generation. This allows the generator to consistently provide high-quality advice and improve user satisfaction.

[0065] The providing unit provides the advice generated by the generating unit. The providing unit can provide the advice in a format such as text, audio, or video. Specifically, the advice in text format is sent to the user's device as a message, and the user can read it. The advice in audio format is provided as an audio message generated using speech synthesis technology, and the user can listen to it. The advice in video format is provided visually using animation or video, and the user can watch it. For example, the providing unit transmits the generated advice to the user as a text message. The providing unit can also provide the advice as an audio message. Furthermore, the providing unit can provide the advice in an optimal format according to the user's preferences and circumstances. For example, advice is provided in audio format to a user who is driving, and advice is provided in text format to a user who is doing desk work. This allows the providing unit to maximize the convenience of the user. Furthermore, the providing unit also has a function of managing the advice provision history and allowing the user to refer to advice received in the past at any time. This allows the user to look back on past advice and use it as a reference for self-improvement. The providing unit can also collect data regarding the provision of advice and use it as basic data for evaluating the performance of the entire system. This enables the providing unit to provide advice to the user effectively and efficiently, thereby improving the performance of the entire system.

[0066] The collection unit collects the user's feedback on the advice provided by the provision unit. The collection unit can collect the user's reaction in the form of, for example, clicks, comments, ratings, and the like. Specifically, if the user evaluates the provided advice as "useful," the collection unit collects the evaluation. In addition, if the user leaves a comment on the advice, the collection unit also collects the comment. Furthermore, the collection unit can analyze the user's behavioral data and evaluate the effectiveness of the advice. For example, if the user takes action in accordance with the advice, the collection unit collects the behavioral data and evaluates the effectiveness of the advice. This allows the collection unit to grasp in detail how the provided advice has affected the user. Furthermore, the collection unit provides data for improving the functions of the generation unit and the provision unit based on the collected feedback. For example, the collection unit analyzes the user's feedback, evaluates the quality of the generated advice, and updates the learning data of the generation AI as necessary. In addition, the collection unit reviews the method and timing of the provision unit and makes improvements to improve the convenience of the user. This allows the collection unit to continuously improve the performance of the entire system and increase the satisfaction of the user. Furthermore, the collection unit also has a mechanism for anonymizing the feedback data and utilizing the data while protecting privacy. This allows users to provide feedback with confidence and allows the system to continue to improve based on high-quality data.

[0067] The generation unit may have a function for the user to select a specific great person. The generation unit may provide, for example, an interface for the user to select a specific great person. For example, the generation unit may display a list from which the user can select from historical figures and modern celebrities. By the user selecting a specific great person, the generation unit may generate advice based on the characteristics of the great person. For example, if the great person selected by the user is a scientist, the generation unit may generate advice including a scientific approach. Also, if the great person selected by the user is an artist, the generation unit may generate advice including a creative approach. This allows the user to provide more personalized advice by selecting a specific great person. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit may input the characteristics of the great person selected by the user to AI and generate advice based on the characteristics.

[0068] The generation unit can have a function of learning past input data. The generation unit has, for example, an algorithm for learning past input data. For example, the generation unit can collect input data of the user for the past year and improve the accuracy of advice based on the data. The generation unit can also learn data of a specific category. For example, the generation unit can learn data related to a project input by the user in the past and generate advice related to the project based on the data. As a result, the accuracy of advice is improved by learning the past input data. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past input data into AI and generate advice based on the data.

[0069] The providing unit may have a function of providing a specific action plan. The providing unit may provide, for example, a specific action plan to a user. For example, the providing unit may provide a step-by-step guide or a checklist. When a user sets a goal, the providing unit may provide a specific action plan for achieving the goal. For example, the providing unit may present a specific step such as "Let's start by learning basic algorithms." This allows the user's behavior to be specifically supported by providing a specific action plan. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit may input a user's goal into AI and generate a specific action plan based on the goal.

[0070] The reception unit can estimate the emotional state of the user and adjust the timing of receiving the input based on the estimated emotional state of the user. The reception unit includes, for example, an algorithm for estimating the emotional state of the user. For example, the reception unit can estimate the emotional state of the user using facial expression recognition technology or voice analysis technology. If the user is stressed, the reception unit can prompt the user to input at a timing when the user can relax. If the user is concentrating, the reception unit can immediately accept the input. Furthermore, if the user is tired, the reception unit can prompt the user to input after a break. In this way, by adjusting the timing of receiving the input according to the user's emotion, the input can be accepted at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the emotional state.

[0071] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, includes an algorithm for analyzing the user's past input history. For example, the reception unit can collect the user's input history for the past year and select the optimal reception method based on the data. The reception unit can also analyze the input history of a specific category. For example, the reception unit can preferentially suggest an input method that the user has frequently used in the past. Furthermore, the reception unit can select the most efficient input method from the user's past input history. This allows the optimal reception method to be selected by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and select the optimal reception method based on the data.

[0072] The reception unit can filter the input contents based on the user's current situation or field of interest. The reception unit includes, for example, an algorithm for grasping the user's current situation or field of interest. For example, the reception unit can collect information such as the user's time zone, location, and activity content, and filter the input contents based on the information. If the user is at work, the reception unit can prioritize work-related input contents. Also, if the user is spending time on a hobby, the reception unit can prioritize input contents related to the hobby. Furthermore, if the user is on vacation, the reception unit can prioritize relaxing content. This makes it possible to accept more relevant inputs by filtering the input contents based on the user's current situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and field of interest into AI, and filter the input contents based on the data.

[0073] The reception unit can estimate the user's emotion and determine the priority of the input to be received based on the estimated user's emotion. The reception unit includes, for example, an algorithm for estimating the user's emotional state. For example, the reception unit can estimate the user's emotional state using facial expression recognition technology or voice analysis technology. If the user is nervous, the reception unit can prioritize relaxing content. If the user is excited, the reception unit can prioritize interesting content. If the user is tired, the reception unit can prioritize simple content. This allows more appropriate input to be preferentially received by determining the priority of the input based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the emotional state.

[0074] The reception unit can preferentially receive inputs with high relevance based on the user's location information. The reception unit includes, for example, a technology for acquiring the user's geographic location information. For example, the reception unit can acquire the user's location information using GPS data or an IP address. When the user is in a specific location, the reception unit can prioritize inputs related to the location. When the user is traveling, the reception unit can prioritize inputs related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputs related to the home. In this way, by taking into account the user's geographic location information, it is possible to preferentially accept inputs with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's location information to AI and select inputs with high relevance based on the information.

[0075] The reception unit can analyze the user's online activities and receive relevant inputs. The reception unit includes, for example, an algorithm for analyzing the user's social media activities and website browsing history. For example, the reception unit can suggest inputs based on the content the user has shared on social media and the topics the user follows. The reception unit can also prompt the user for input based on the user's social media activity time. In this way, the reception unit can accept relevant inputs by analyzing the user's social media activities. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and select relevant inputs based on the data.

[0076] The generation unit can estimate the emotional state of the user and adjust the way of expressing advice based on the estimated emotional state of the user. The generation unit includes, for example, an algorithm for estimating the emotional state of the user. For example, the generation unit can estimate the emotional state of the user using facial expression recognition technology or voice analysis technology. If the user is depressed, the generation unit can generate advice that includes many encouraging words. If the user is confident, the generation unit can generate advice that includes a specific action plan. Furthermore, if the user is feeling anxious, the generation unit can generate advice that includes words that give a sense of security. This allows more appropriate advice to be provided by adjusting the way of expressing advice based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotional data to the generation AI and cause the generation AI to adjust the way of expressing advice.

[0077] The generation unit can adjust the level of detail of the advice based on the user's past actions and achievements when generating the advice. The generation unit includes, for example, an algorithm for analyzing the user's past actions and achievements. For example, the generation unit can provide specific advice based on projects in which the user has been successful in the past. In addition, the generation unit can provide advice including improvements in consideration of the user's past failures. Furthermore, the generation unit can suggest a next step based on the user's past achievements. As a result, more specific advice can be provided by adjusting the level of detail of the advice based on the user's past actions and achievements. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past action data into AI and adjust the level of detail of the advice based on the data.

[0078] The generation unit can apply different advice algorithms according to the characteristics of the great person selected by the user when generating advice. The generation unit has a function for applying different advice algorithms based on the characteristics of the great person selected by the user, for example. For example, if the great person selected by the user is a scientist, the generation unit can generate advice including a scientific approach. Also, if the great person selected by the user is an artist, advice including a creative approach can be generated. Furthermore, if the great person selected by the user is a leader, advice regarding leadership can be generated. This allows more personalized advice to be provided by applying different advice algorithms according to the characteristics of the great person selected by the user. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the characteristics of the great person selected by the user into AI and generate advice based on the characteristics.

[0079] The generation unit can estimate the emotional state of the user and adjust the length of the advice based on the estimated emotional state of the user. The generation unit includes, for example, an algorithm for estimating the emotional state of the user. For example, the generation unit can estimate the emotional state of the user using facial expression recognition technology or voice analysis technology. If the user is in a hurry, the generation unit can generate short, to-the-point advice. If the user is relaxed, the generation unit can generate longer advice including detailed explanations. Furthermore, if the user is excited, the generation unit can generate advice with a visually stimulating effect. This allows more appropriate advice to be provided by adjusting the length of the advice based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotional data to the generation AI and cause the generation AI to adjust the length of the advice.

[0080] The generating unit can determine the priority of advice based on the input time of the user when generating advice. The generating unit includes, for example, an algorithm for considering the input time of the user. For example, when the user inputs in the morning, the generating unit can prioritize advice to make a plan for the day. Also, when the user inputs in the evening, the generating unit can prioritize advice to review the day. Furthermore, when the user inputs on the weekend, the generating unit can prioritize advice to make a plan for the week. In this way, by determining the priority of advice based on the input time of the user, more appropriate advice can be provided. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the input time data of the user to AI and determine the priority of advice based on the data.

[0081] The generating unit can adjust the order of advice based on the relevance of the user when generating advice. The generating unit includes, for example, an algorithm for considering the relevance of the user. For example, the generating unit can prioritize advice related to a specific project by the user. Also, the generating unit can prioritize advice related to a specific skill by the user. Furthermore, the generating unit can prioritize advice related to a specific goal by the user. This allows more appropriate advice to be provided by adjusting the order of advice based on the relevance of the user. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can input the relevance data of the user into AI and adjust the order of advice based on the data.

[0082] The providing unit can estimate the emotional state of the user and adjust the advice providing method based on the estimated emotional state of the user. The providing unit includes, for example, an algorithm for estimating the emotional state of the user. For example, the providing unit can estimate the emotional state of the user using facial expression recognition technology or voice analysis technology. When the user is nervous, the providing unit can provide a display method that is simple and highly visible. When the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows more appropriate advice to be provided by adjusting the advice providing method based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data to the generation AI and cause the generation AI to adjust the advice providing method.

[0083] When providing advice, the providing unit can select the optimal providing method by referring to the user's past feedback. The providing unit includes, for example, an algorithm for analyzing the user's past feedback. For example, the providing unit can prioritize the providing method that the user has preferred in the past. The providing unit can also select the most effective providing method from the user's past feedback. Furthermore, the providing unit can eliminate the providing method that the user has avoided in the past. This allows the optimal providing method to be selected by referring to the user's past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI and select the optimal providing method based on the data.

[0084] The providing unit can customize the means of providing advice based on the user's current situation when providing advice. The providing unit includes, for example, an algorithm for grasping the user's current situation. For example, the providing unit can collect information such as the user's time zone, location, and activity content, and customize the means of providing advice based on the information. When the user is at work, the providing unit can provide advice in the form of a short message. Also, when the user is on vacation, the providing unit can provide advice including detailed explanations. Furthermore, when the user is on the move, the providing unit can provide advice by voice. As a result, more appropriate advice can be provided by customizing the means of providing advice based on the user's current situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into AI and customize the means of providing advice based on the data.

[0085] The providing unit can estimate the emotional state of the user and determine the order of providing advice based on the estimated emotional state of the user. The providing unit includes, for example, an algorithm for estimating the emotional state of the user. For example, the providing unit can estimate the emotional state of the user using facial expression recognition technology or voice analysis technology. If the user is nervous, the providing unit can provide relaxing advice first. Also, if the user is excited, the providing unit can provide interesting advice first. Furthermore, if the user is tired, the providing unit can provide simple advice first. This allows more appropriate advice to be provided by determining the order of providing advice based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data to the generation AI and cause the generation AI to determine the order of providing advice.

[0086] The providing unit can select the optimal providing method by considering the geographical location information of the user when providing advice. The providing unit includes, for example, a technology for acquiring the geographical location information of the user. For example, the providing unit can acquire the user's location information using GPS data or an IP address. When the user is in a specific location, the providing unit can provide advice related to the location. When the user is traveling, the providing unit can provide advice related to the travel destination. Furthermore, when the user is at home, the providing unit can provide advice related to the home. This allows the optimal providing method to be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information into AI and select the optimal providing method based on the information.

[0087] The providing unit can analyze the user's social media activity and suggest a means of providing the advice when providing the advice. The providing unit includes, for example, an algorithm for analyzing the user's social media activity. For example, the providing unit can provide advice based on the content shared by the user on social media. In addition, the providing unit can provide advice related to topics the user follows on social media. Furthermore, the providing unit can provide advice based on the time the user is active on social media. In this way, the optimal means of providing the advice can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and suggest the optimal means of providing the advice based on the data.

[0088] The collection unit can estimate the emotional state of the user and adjust the feedback collection method based on the estimated emotional state of the user. The collection unit includes, for example, an algorithm for estimating the emotional state of the user. For example, the collection unit can estimate the emotional state of the user using facial expression recognition technology or voice analysis technology. If the user is relaxed, the collection unit can request detailed feedback. If the user is in a hurry, the collection unit can request simple feedback. Furthermore, if the user is feeling anxious, the collection unit can request feedback that gives a sense of security. This allows more appropriate feedback to be collected by adjusting the feedback collection method based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data to the generation AI and cause the generation AI to adjust the feedback collection method.

[0089] The collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. The collection unit includes, for example, an algorithm for analyzing the user's past feedback history. For example, the collection unit can prioritize collection methods that the user has preferred in the past. The collection unit can also select the most effective collection method from the user's past feedback history. Furthermore, the collection unit can eliminate collection methods that the user has avoided in the past. This allows the optimal collection method to be selected by referring to the user's past feedback history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and select the optimal collection method based on the data.

[0090] The collection unit can customize the collection means based on the user's current situation when collecting feedback. The collection unit includes, for example, an algorithm for grasping the user's current situation. For example, the collection unit can collect information such as the user's time zone, location, and activity content, and customize the collection means based on the information. If the user is at work, the collection unit can ask for feedback in the form of a short message. Also, if the user is on vacation, the collection unit can ask for detailed feedback. Furthermore, if the user is on the move, the collection unit can ask for feedback by voice. This allows more appropriate feedback to be collected by customizing the collection means based on the user's current situation. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's current situation data into AI and customize the collection means based on the data.

[0091] The collection unit can estimate the emotional state of the user and determine the priority of the feedback based on the estimated emotional state of the user. The collection unit includes, for example, an algorithm for estimating the emotional state of the user. For example, the collection unit can estimate the emotional state of the user using facial expression recognition technology or voice analysis technology. If the user is nervous, the collection unit can prioritize feedback that allows the user to relax. Also, if the user is excited, the collection unit can prioritize feedback that attracts interest. Furthermore, if the user is tired, the collection unit can prioritize simple feedback. This allows more appropriate feedback to be collected by determining the priority of feedback based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data to the generation AI and cause the generation AI to execute the determination of the priority of the feedback.

[0092] The collection unit can select the optimal collection method in consideration of the geographical location information of the user when collecting feedback. The collection unit includes, for example, a technology for acquiring the geographical location information of the user. For example, the collection unit can acquire the user's location information using GPS data or an IP address. When the user is in a specific location, the collection unit can collect feedback related to the location. When the user is traveling, the collection unit can collect feedback related to the travel destination. Furthermore, when the user is at home, the collection unit can collect feedback related to the home. This allows the optimal collection method to be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location information into AI and select the optimal collection method based on the information.

[0093] The collection unit can analyze the user's social media activity and suggest a collection means when collecting feedback. The collection unit includes, for example, an algorithm for analyzing the user's social media activity. For example, the collection unit can collect feedback based on the content shared by the user on social media. The collection unit can also collect feedback related to topics the user follows on social media. Furthermore, the collection unit can collect feedback based on the user's activity time on social media. This makes it possible to suggest an optimal collection means by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and suggest an optimal collection means based on the data.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The reception unit can automatically search for related past advice based on the user's input content and present it to the user. For example, if the user inputs "I want to start a new project," the reception unit can search for advice generated at the time of a similar input in the past and present it to the user. This allows the user to refer to past advice and receive more effective advice. The reception unit can also save a history of advice the user has received in the past, and provide advice based on that history when the user inputs the same content again. Furthermore, the reception unit can preferentially present the most highly rated advice based on evaluations of advice the user has received in the past.

[0096] The generator can automatically search for related external resources based on the user's input and include them in the advice. For example, if the user inputs "I want to improve my programming skills," the generator can search for related online courses and tutorials and include links to them in the advice. This allows the user to improve their skills using specific resources. The generator can also search for books and interviews of famous people selected by the user and reflect the contents of those in the advice. Furthermore, the generator can search for the latest research papers and news articles related to the user's field of interest and include them in the advice.

[0097] The generator may suggest relevant communities and forums based on the user's input. For example, if the user inputs "I want to start a new project," the generator may search for relevant online communities and forums and include links to them in the recommendation. This allows the user to interact with and share information with people who share the same interests. The generator may also suggest communities and forums in which the great person selected by the user participated. Additionally, the generator may suggest events and workshops related to the user's field of interest.

[0098] The provider can suggest relevant video content based on the user's input. For example, if the user inputs "I want to improve my programming skills," the provider can search for related video tutorials and webinars and include links to them in the advice. This allows the user to learn visually. The provider can also suggest documentaries and interview videos related to the great person selected by the user. Furthermore, the provider can suggest YouTube channels and video series related to the user's field of interest.

[0099] The reception unit can suggest related books and articles based on the user's input. For example, if the user inputs "I want to start a new project," the reception unit can search for related books and articles and include links to them in the advice. This allows the user to learn more deeply. The reception unit can also suggest works and biographies of great people selected by the user. Furthermore, the reception unit can suggest specialized books and academic papers related to the user's field of interest.

[0100] The generation unit can estimate the emotional state of the user and adjust the tone of the advice based on the estimated emotional state of the user. For example, if the user is depressed, the generation unit can generate advice that includes many encouraging words. If the user is confident, the generation unit can generate advice that includes a specific action plan. Furthermore, if the user is anxious, the generation unit can generate advice that includes words that give a sense of security. In this way, more appropriate advice can be provided by adjusting the tone of the advice based on the user's emotions.

[0101] The providing unit can estimate the emotional state of the user and adjust the advice providing format based on the estimated emotional state of the user. For example, if the user is nervous, the providing unit can provide a simple and highly visible display method. If the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. In this way, more appropriate advice can be provided by adjusting the advice providing format based on the user's emotions.

[0102] The collection unit can estimate the user's emotional state and adjust the feedback collection method based on the estimated emotional state of the user. For example, if the user is relaxed, the collection unit can request detailed feedback. If the user is in a hurry, the collection unit can request simple feedback. If the user is anxious, the collection unit can request reassuring feedback. In this way, by adjusting the feedback collection method based on the user's emotions, more appropriate feedback can be collected.

[0103] The providing unit can estimate the emotional state of the user and determine the order in which advice is provided based on the estimated emotional state of the user. For example, if the user is nervous, the providing unit can provide relaxing advice first. Also, if the user is excited, the providing unit can provide interesting advice first. Furthermore, if the user is tired, the providing unit can provide simple advice first. In this way, by determining the order in which advice is provided based on the user's emotions, more appropriate advice can be provided.

[0104] The generation unit can estimate the user's emotional state and adjust the length of the advice based on the estimated emotional state of the user. For example, if the user is in a hurry, the generation unit can generate short, to-the-point advice. If the user is relaxed, the generation unit can generate longer advice including detailed explanations. If the user is excited, the generation unit can generate advice with visually stimulating effects. This allows more appropriate advice to be provided by adjusting the length of the advice based on the user's emotions.

[0105] The process flow of the second embodiment will be briefly described below.

[0106] Step 1: The reception unit receives a user input. The user input includes, but is not limited to, ideas, actions, emotions, goals, etc. The reception unit can receive input in, for example, text format, audio format, video format, etc. Step 2: The generation unit generates advice from a great person based on the information received by the reception unit. The generation unit generates appropriate advice for the user's input, for example, using a generation AI. The generation AI generates advice based on the user's input, using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI generates a response to the user's input, such as, "Your idea is great. Please give it a try." Step 3: The providing unit provides the advice generated by the generating unit. The providing unit can provide the advice in a format such as a text format, an audio format, or a video format. For example, the providing unit transmits the generated advice to the user as a text message. The providing unit can also provide the advice as an audio message. Step 4: The collecting unit collects user feedback on the advice provided by the providing unit. The collecting unit can collect user responses in the form of, for example, clicks, comments, ratings, etc. For example, if a user rates the provided advice as "helpful," the collecting unit collects the rating.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and collection 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 reception device 38 of the smart device 14 and receives user input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice of a great person using a generation AI. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated advice to the user. The collection unit is realized, for example, by the control unit 46A of the smart device 14 and collects user feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0118] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and collection 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 microphone 238 of the smart glasses 214 and receives user input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice of a great person using a generation AI. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated advice to the user. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects user feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and collection 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 microphone 238 of the headset type terminal 314 and receives user input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice of a great person using a generation AI. The provision unit is realized, for example, by the speaker 240 of the headset type terminal 314 and provides the generated advice to the user. The collection unit is realized, for example, by the control unit 46A of the headset type terminal 314 and collects user feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] 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.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] 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.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0154] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and collection 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 microphone 238 of the robot 414 and receives user input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice of a great person using a generation AI. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated advice to the user. The collection unit is realized, for example, by the control unit 46A of the robot 414 and collects user feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0161] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0164] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0165] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0167] 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.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0170] 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.

[0171] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0172] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0176] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0178] (Appendix 1) A reception unit that receives input from a user; a generator for generating a specific advice based on the information received by the receiver; a providing unit that provides the advice generated by the generating unit; A collection unit that collects user reactions to the advice provided by the provision unit. A system characterized by: (Appendix 2) The generation unit is Allows users to choose specific great people 2. The system of claim 1, (Appendix 3) The generation unit is Equipped with a function to learn from past input data 2. The system of claim 1, (Appendix 4) The providing unit is Equipped with the function of providing specific action plans 2. The system of claim 1, (Appendix 5) The reception unit is To estimate a user's emotional state and adjust the timing of receiving input based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past input history and select the optimal reception method 2. The system of claim 1, (Appendix 7) The reception unit is Filtering input based on the user's current situation or interests 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Prioritize relevant input based on the user's location 2. The system of claim 1, (Appendix 10) The reception unit is Analyze your online activities and receive relevant input 2. The system of claim 1, (Appendix 11) The generation unit is The emotional state of the user is estimated, and the manner in which advice is presented is adjusted based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 12) The generation unit is When generating advice, adjust the level of detail based on the user's past actions and achievements. 2. The system of claim 1, (Appendix 13) The generation unit is When generating advice, different advice algorithms are applied depending on the characteristics of the great person selected by the user. 2. The system of claim 1, (Appendix 14) The generation unit is Estimating a user's emotional state and adjusting the length of advice based on the estimated emotional state of the user 2. The system of claim 1, (Appendix 15) The generation unit is When generating advice, prioritize advice based on the time of user input 2. The system of claim 1, (Appendix 16) The generation unit is Adjusting the order of advice based on user relevance during advice generation 2. The system of claim 1, (Appendix 17) The providing unit is The emotional state of a user is estimated, and a method of providing advice is adjusted based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 18) The providing unit is When providing advice, the system refers to the user's past feedback to select the best way to provide it. 2. The system of claim 1, (Appendix 19) The providing unit is When providing advice, customize the way you provide it based on the user's current situation 2. The system of claim 1, (Appendix 20) The providing unit is The emotional state of the user is estimated, and the order of providing advice is determined based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 21) The providing unit is When providing advice, the system selects the optimal method of providing advice by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 22) The providing unit is When providing advice, the system analyzes the user's social media activity to suggest ways to provide the advice. 2. The system of claim 1, (Appendix 23) The collecting unit includes: Estimating a user's emotional state and adjusting a feedback collection method based on the estimated user's emotional state 2. The system of claim 1, (Appendix 24) The collecting unit includes: When collecting feedback, choose the best method by looking at the user's past feedback history 2. The system of claim 1, (Appendix 25) The collecting unit includes: When collecting feedback, customize the collection method based on the user's current situation 2. The system of claim 1, (Appendix 26) The collecting unit includes: Estimating a user's emotional state and prioritizing feedback based on the estimated emotional state of the user 2. The system of claim 1, (Appendix 27) The collecting unit includes: When collecting feedback, choose the best way to collect it by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 28) The collecting unit includes: When collecting feedback, analyze users' social media activity to suggest collection methods 2. The system of claim 1, [Explanation of symbols]

[0179] 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 that receives input from a user; a generator for generating a specific advice based on the information received by the receiver; a providing unit that provides the advice generated by the generating unit; A collection unit that collects user reactions to the advice provided by the provision unit. A system characterized by:

2. The generation unit is Allows users to choose specific great people 2. The system of claim 1 .

3. The generation unit is Equipped with a function to learn from past input data 2. The system of claim 1 .

4. The providing unit is Equipped with the function of providing specific action plans 2. The system of claim 1 .

5. The reception unit is To estimate a user's emotional state and adjust the timing of receiving input based on the estimated emotional state of the user.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past input history and select the optimal reception method 2. The system of claim 1 .

7. The reception unit is Filtering input based on the user's current situation or interests 2. The system of claim 1 .

8. The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1 .

9. The reception unit is Prioritize relevant input based on the user's location 2. The system of claim 1 .

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