system

Abstract

The system according to this embodiment aims to estimate the emotions of the person seeking advice and provide appropriate legal advice based on that. [Solution] The system according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the person seeking advice. The emotion estimation unit estimates the emotions of the person seeking advice based on the emotion estimation information acquired by the acquisition unit. The provision unit provides legal advice based on the emotions estimated by the emotion estimation unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, legal advice considering the feelings of the counselor has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to estimate the feelings of the counselor and provide appropriate legal advice based on it.

Means for Solving the Problems

[0006] <00000The system according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of a client. The emotion estimation unit estimates the emotions of the client based on the emotion estimation information acquired by the acquisition unit. The provision unit provides legal advice based on the emotions estimated by the emotion estimation unit. [Effects of the Invention]

[0007] The system according to this embodiment can estimate the emotions of the person seeking advice and provide appropriate legal advice based on that. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The legal consultation support robot according to an embodiment of the present invention is a system that uses an emotion engine to estimate the emotions of a client and provides appropriate legal advice. This system estimates the emotions of the client and provides appropriate legal advice. For example, the client's facial expressions, tone of voice, and word choice are acquired as information for emotion estimation. Next, the system estimates the client's emotions based on the acquired information. The emotion engine uses AI to analyze the client's emotions and identify emotions such as anxiety, worry, anger, and sadness. For example, if the client is feeling anxious, the emotion engine detects that anxiety and takes appropriate action. Next, it provides legal advice based on the estimated emotions. For example, for a client who is feeling anxious, it provides reassurance by using calming language and giving specific and easy-to-understand advice. Also, if the client is feeling anger or sadness, it responds with a sympathetic yet constructive approach to reduce the client's anxiety. This provides highly reliable legal consultation. Furthermore, for clients with multiple problems, it advises prioritizing based on the urgency and relevance of each problem. For example, if a client has multiple legal problems, it advises addressing the most urgent problem first. This allows the client to solve problems efficiently. Furthermore, the system creates legal drama episodes based on the client's emotions and their specific case. The generating AI creates episodes tailored to the client's case, explaining legal issues in an easy-to-understand manner. For example, if the client is facing divorce issues, the system generates and provides a legal drama episode about divorce. This makes it easier for the client to understand the legal issues relevant to their case. In addition, the generated legal drama episodes may be provided in VR. This allows the client to understand legal issues through a more realistic experience. For example, by watching a legal drama episode using VR, the client can experience the atmosphere and flow of a real courtroom. Thus, the present invention is a system that uses an emotion engine and generating AI to estimate the client's emotions and provide appropriate legal advice, thereby reducing the client's anxiety and providing reliable legal consultations.This allows the legal consultation support robot to estimate the client's emotions and provide appropriate legal advice.

[0029] The legal consultation support robot according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the person seeking advice. Emotion estimation information includes, but is not limited to, voice data, facial expression data, and text data. For example, the acquisition unit can capture the person's facial expression with a camera and acquire facial expression data. The acquisition unit can also record the tone of the person's voice with a microphone and acquire voice data. Furthermore, the acquisition unit can acquire the person's speech as text data. For example, the acquisition unit can capture the person's facial expression with a high-resolution camera and acquire facial expression data using facial expression recognition technology. For voice data, high-quality voice is recorded using a microphone and data is acquired using voice analysis technology. For text data, the person's speech is transcribed in real time and data is acquired using natural language processing technology. The emotion estimation unit estimates the person's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the person's emotions using AI and identifies emotions such as anxiety, worry, anger, and sadness. The emotion estimation unit analyzes facial expression data using an AI model to estimate the client's emotions. It can also analyze voice data to estimate emotions from tone and speed. Furthermore, it can analyze text data to estimate emotions from word choice. For example, the emotion estimation unit uses a deep learning model to analyze facial expression data and identify emotions. Voice data is analyzed using speech recognition technology to estimate emotions from tone and speed. Text data is analyzed using natural language processing technology to estimate emotions from word choice. The service provider provides legal advice based on the emotions estimated by the emotion estimation unit. For example, the service provider can provide reassurance to an anxious client by using calming language and providing specific, easy-to-understand advice. If the client is feeling anger or sadness, the service provider can respond with a sympathetic yet constructive approach to alleviate the client's anxiety. Furthermore, the service provider can advise clients with multiple problems on prioritizing issues based on their urgency and relevance.For example, the service provider advises addressing the most urgent issues first. This allows the client to solve problems efficiently. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can provide advice using an AI model that takes emotions estimated by the emotion estimation unit as input and outputs legal advice. This allows the legal consultation support robot according to the embodiment to estimate the client's emotions and provide appropriate legal advice.

[0030] The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the person seeking advice. Emotion estimation information includes, but is not limited to, voice data, facial expression data, and text data. For example, the acquisition unit can capture the person's facial expression with a camera and acquire facial expression data. Specifically, it uses a high-resolution camera to capture subtle changes in the person's facial expression and analyzes the data using facial expression recognition technology. This allows for highly accurate detection of emotions such as smiles, anger, and surprise. The acquisition unit can also record the tone of the person's voice with a microphone and acquire voice data. By using a high-quality microphone and combining it with noise cancellation technology, clear voice data is collected. Voice analysis technology is used to extract features such as tone, pitch, speed, and volume of the voice, which are used to estimate emotions. Furthermore, the acquisition unit can acquire the person's speech as text data. For example, speech recognition technology is used to transcribe speech in real time. Natural language processing technology is used to acquire data for estimating emotions from the context of the speech and the choice of words. This allows the acquisition unit to efficiently collect diverse emotional information from the client and provide it to the emotional estimation unit.

[0031] The emotion estimation unit estimates the client's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit uses AI to analyze the client's emotions and identify emotions such as anxiety, worry, anger, and sadness. Specifically, it uses deep learning models to analyze facial expression data and estimate the client's emotions. For example, it uses a convolutional neural network (CNN) to extract and classify emotional features from facial expression data. Voice data is analyzed using speech recognition technology to estimate emotions from voice tone and speed. Recurrent neural networks (RNNs) and long-term short-term memory (LSTM) models are used to capture temporal changes in voice data and identify emotions. Text data is analyzed using natural language processing technology to estimate emotions from word choice. For example, a transformer model is used to understand the context of text data and classify emotions. As a result, the emotion estimation unit can comprehensively analyze the diverse data acquired and estimate the client's emotions with high accuracy. Furthermore, the emotion estimation unit can continuously improve its estimation accuracy by learning from past data and cases.

[0032] The service provider provides legal advice based on the emotions estimated by the emotion estimation unit. For example, to a client who is feeling anxious, the service provider provides reassurance by using calming language and giving specific, easy-to-understand advice. Specifically, it uses an AI model to generate optimal advice that matches the client's emotions. For example, it uses natural language generation technology to select language and expressions that resonate with the client's emotions and provide appropriate advice. Furthermore, if the client is feeling anger or sadness, the service provider can respond with a sympathetic yet constructive approach to alleviate the client's anxiety. For example, it can soothe the client's emotions by using empathetic language and presenting specific solutions. In addition, the service provider can advise clients with multiple problems on prioritizing them based on the urgency and relevance of each problem. For example, it can advise addressing the most urgent problem first. This allows the client to solve problems efficiently. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can provide advice using an AI model that takes the emotions estimated by the emotion estimation unit as input and outputs legal advice. This allows the service provider to quickly deliver appropriate legal advice tailored to the client's emotions, thereby improving client satisfaction.

[0033] The generation unit can create legal drama episodes tailored to the client's case, based on the client's emotions and their specific circumstances. For example, the generation unit uses a generation AI to generate episodes relevant to the client's case. For instance, if the client is facing divorce issues, the generation unit generates and provides a legal drama episode about divorce. Similarly, if the client is facing labor issues, the generation unit can generate a legal drama episode about labor. Furthermore, if the client is facing inheritance issues, the generation unit can generate a legal drama episode about inheritance. For example, the generation unit uses a generation AI to generate a scenario related to divorce, constructing characters and a storyline. Episodes related to labor issues generate scenarios based on labor law, illustrating specific examples. Episodes related to inheritance issues generate scenarios based on inheritance law, explaining the procedures for estate division. This makes it easier for the client to understand the legal issues relevant to their own case. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without one. For example, the generation unit can input information about the client's case into the generation AI and have the generation AI generate the episodes. This allows us to create legal drama episodes tailored to each client's case, making legal issues easier to understand.

[0034] The service provider can provide legal dramas of episodes generated by the generation unit. For example, the service provider can provide episodes generated using a generation AI in video format. For example, the service provider can play the generated episode as a video and allow the client to watch it. The service provider can also provide the generated episode in audio format. For example, the service provider can play the generated episode as audio and allow the client to listen to it. Furthermore, the service provider can also provide the generated episode in text format. For example, the service provider can display the generated episode as text and allow the client to read it. In this way, by providing legal dramas of generated episodes, legal issues can be explained to clients in an easy-to-understand manner. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide episodes using an AI model that takes episodes generated by the generation unit as input and outputs them as video, audio, or text.

[0035] The service provider can provide legal drama episodes generated by the generation unit in VR. For example, the service provider can provide episodes generated using a generation AI in VR format. For example, the service provider can play the generated episode using a VR headset and allow the client to view it. The service provider can also provide the generated episode as a 360-degree video. For example, the service provider can play the generated episode as a 360-degree video and allow the client to view it. Furthermore, the service provider can provide the generated episode as interactive content. For example, the service provider can provide the generated episode in an interactive format, allowing the client to choose options for the scenario. By providing legal drama episodes in VR, the service provider can help clients understand legal issues through a more realistic experience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide episodes using an AI model that takes episodes generated by the generation unit as input and outputs them in VR format.

[0036] The data acquisition unit can analyze the client's past consultation history and select the optimal data acquisition method. For example, the data acquisition unit may prioritize selecting data acquisition methods for emotion estimation information that the client has used in the past. The data acquisition unit can also select the most effective data acquisition method from the client's past consultation history. Furthermore, the data acquisition unit can analyze the client's past consultation history and customize the data acquisition method. For example, the data acquisition unit may prioritize selecting data acquisition methods for emotion estimation information that have been used in the past and select an effective method. It may analyze past consultation history and select the optimal data acquisition method. It may customize the data acquisition method to acquire the most suitable information for the client. In this way, the optimal data acquisition method can be selected by analyzing past consultation history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit may input past consultation history data into a generating AI and have the generating AI select the optimal data acquisition method.

[0037] The acquisition unit can filter the information for emotion estimation based on the client's current living situation and areas of interest when acquiring the information. For example, the acquisition unit prioritizes acquiring highly relevant emotion estimation information based on the client's current living situation. The acquisition unit can also filter the emotion estimation information based on the client's areas of interest. Furthermore, the acquisition unit can combine the client's living situation and areas of interest to acquire the most suitable emotion estimation information. For example, the acquisition unit prioritizes acquiring highly relevant information based on the client's current living situation. It filters the emotion estimation information based on the areas of interest. It acquires the most suitable information by combining the living situation and areas of interest. This allows the acquisition of highly relevant information by filtering the information based on the client's living situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the client's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0038] The acquisition unit can prioritize the acquisition of highly relevant information by considering the client's geographical location when acquiring information for emotion estimation. For example, the acquisition unit can acquire highly relevant emotion estimation information based on the client's current location. The acquisition unit can also acquire emotion estimation information by referring to the client's past location information. Furthermore, the acquisition unit can analyze the relationship between the client's geographical location information and emotion estimation information and acquire the most suitable information. For example, the acquisition unit can acquire highly relevant information based on the client's current location. It can acquire emotion estimation information by referring to past location information. It can analyze the relationship between geographical location information and emotion estimation information and acquire the most suitable information. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the client's geographical location information into a generating AI and have the generating AI acquire highly relevant information.

[0039] The acquisition unit can analyze the client's social media activity and obtain relevant information when acquiring information for sentiment estimation. For example, the acquisition unit can analyze the client's social media posts and acquire information for sentiment estimation. The acquisition unit can also acquire information for sentiment estimation by referring to the client's social media activity history. Furthermore, the acquisition unit can analyze the client's social media friendships and acquire relevant information for sentiment estimation. For example, the acquisition unit can analyze the client's social media posts and acquire relevant information. It can acquire information for sentiment estimation by referring to the client's social media activity history. It can analyze friendships and acquire relevant information. In this way, relevant information can be acquired by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the client's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0040] The emotion estimation unit can improve the accuracy of its estimation by referring to the client's past emotion data during emotion estimation. For example, the emotion estimation unit can refer to the client's past emotion data to estimate the current emotion. The emotion estimation unit can also analyze the client's past emotion data and adjust the estimation algorithm. Furthermore, the emotion estimation unit can improve the accuracy of emotion estimation based on the client's past emotion data. For example, the emotion estimation unit can refer to past emotion data to estimate the current emotion. It can analyze past emotion data and adjust the algorithm. It can improve the accuracy of emotion estimation based on past emotion data. In this way, the accuracy of emotion estimation is improved by referring to past emotion data. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without using AI. For example, the emotion estimation unit can input past emotion data into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0041] The emotion estimation unit can perform emotion estimation while considering the client's attribute information (age, gender, etc.). For example, the emotion estimation unit can adjust the emotion estimation algorithm based on the client's age. It can also adjust the emotion estimation algorithm based on the client's gender. Furthermore, the emotion estimation unit can perform emotion estimation by comprehensively considering the client's attribute information. For example, the emotion estimation unit can adjust the algorithm based on age and estimate emotions. It can adjust the algorithm based on gender and estimate emotions. It can comprehensively consider attribute information and estimate emotions. This improves the accuracy of emotion estimation by considering attribute information. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without using AI. For example, the emotion estimation unit can input the client's attribute information into a generating AI and have the generating AI perform the estimation.

[0042] The emotion estimation unit can perform emotion estimation while considering the geographical distribution of the client. For example, the emotion estimation unit adjusts the emotion estimation algorithm based on the client's place of residence. The emotion estimation unit can also analyze the geographical distribution of the client to improve the accuracy of emotion estimation. Furthermore, the emotion estimation unit can perform estimation while considering the relationship between the client's geographical distribution and emotion. For example, the emotion estimation unit adjusts the algorithm based on place of residence and estimates emotion. It analyzes the geographical distribution to improve the accuracy of emotion estimation. It considers the relationship between geographical distribution and emotion and performs estimation. As a result, the accuracy of emotion estimation is improved by considering geographical distribution. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the client's geographical distribution data into a generating AI and have the generating AI perform the estimation.

[0043] The emotion estimation unit can improve the accuracy of its estimation by referring to the client's relevant literature during the emotion estimation process. For example, the emotion estimation unit can adjust its emotion estimation algorithm by referring to the client's relevant literature. The emotion estimation unit can also improve the accuracy of its emotion estimation by analyzing the client's relevant literature. Furthermore, the emotion estimation unit can improve the accuracy of its emotion estimation based on the client's relevant literature. For example, the emotion estimation unit refers to relevant literature, adjusts its algorithm, and estimates emotions. It analyzes relevant literature to improve the accuracy of emotion estimation. It improves the accuracy of emotion estimation based on relevant literature. Thus, the accuracy of emotion estimation is improved by referring to relevant literature. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the client's relevant literature data into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0044] The service provider can provide optimal advice by referring to the client's past consultations when providing legal advice. For example, the service provider can refer to the client's past consultations and provide advice based on similar cases. The service provider can also analyze the client's past consultations and provide optimal advice. Furthermore, the service provider can provide customized advice based on the client's past consultations. For example, the service provider can refer to past consultations and provide advice based on similar cases. It can analyze past consultations and provide optimal advice. It can provide customized advice based on past consultations. In this way, optimal advice can be provided by referring to past consultations. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past consultation data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0045] The service provider can customize legal advice by taking into account the client's attribute information (age, gender, etc.). For example, the service provider can provide appropriate advice based on the client's age. It can also provide appropriate advice based on the client's gender. Furthermore, the service provider can provide customized advice by comprehensively considering the client's attribute information. For example, the service provider can provide appropriate advice based on age. It can provide appropriate advice based on gender. It can provide customized advice by comprehensively considering attribute information. This allows for the provision of more appropriate advice by taking attribute information into account. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the client's attribute information into a generating AI and have the generating AI perform the customization of the advice.

[0046] The service provider can provide optimal legal advice by considering the client's geographical location. For example, the service provider can provide advice on local laws based on the client's current location. The service provider can also provide optimal advice by referring to the client's past location information. Furthermore, the service provider can analyze the relationship between the client's geographical location information and legal advice to provide optimal advice. For example, the service provider can provide advice on local laws based on the client's current location. It can provide optimal advice by referring to past location information. It can analyze the relationship between geographical location information and legal advice to provide optimal advice. In this way, by considering geographical location information, it is possible to provide advice that is appropriate for the region. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the client's geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0047] The service provider can analyze the client's social media activity and propose methods of advice when providing legal advice. For example, the service provider can analyze the content of the client's social media posts and propose the most suitable method of advice. The service provider can also propose methods of advice by referring to the client's social media activity history. Furthermore, the service provider can analyze the client's social media friendships and propose the most suitable method of advice. For example, the service provider can analyze the content of the client's social media posts and propose the most suitable method of advice. It can propose methods of advice by referring to the client's social media activity history. It can analyze friendships and propose the most suitable method of advice. In this way, by analyzing social media activity, the service provider can propose the most suitable method of advice. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the client's social media activity data into a generating AI and have the generating AI execute the proposal of methods of advice.

[0048] The generation unit can generate the most suitable episode for a legal drama by referring to the client's past consultations. For example, the generation unit can refer to the client's past consultations and generate an episode based on similar cases. The generation unit can also analyze the client's past consultations and generate the most suitable episode. Furthermore, the generation unit can generate a customized episode based on the client's past consultations. For example, the generation unit can refer to past consultations and generate an episode based on similar cases. It can analyze past consultations and generate the most suitable episode. It can generate a customized episode based on past consultations. In this way, the most suitable episode can be generated by referring to past consultations. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past consultation data into a generation AI and have the generation AI perform the generation of the most suitable episode.

[0049] The generation unit can customize episodes for legal dramas by considering the client's attribute information (age, gender, etc.). For example, the generation unit can generate an appropriate episode based on the client's age. It can also generate an appropriate episode based on the client's gender. Furthermore, the generation unit can generate a customized episode by comprehensively considering the client's attribute information. For example, the generation unit can generate an appropriate episode based on age. It can generate an appropriate episode based on gender. It can generate a customized episode by comprehensively considering attribute information. This allows for the generation of more appropriate episodes by considering attribute information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the client's attribute information into a generation AI and have the generation AI perform the episode customization.

[0050] The generation unit can generate the most suitable episode for a legal drama by considering the client's geographical location information. For example, the generation unit can generate an episode related to local law based on the client's current location. The generation unit can also generate the most suitable episode by referring to the client's past location information. Furthermore, the generation unit can analyze the relationship between the client's geographical location information and legal episodes to generate the most suitable episode. For example, the generation unit can generate an episode related to local law based on the client's current location. It can generate the most suitable episode by referring to past location information. It can analyze the relationship between geographical location information and legal episodes to generate the most suitable episode. In this way, by considering geographical location information, it is possible to generate episodes that are appropriate for the region. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the client's geographical location information into a generation AI and have the generation AI perform the generation of the most suitable episode.

[0051] The generation unit can analyze the client's social media activity and suggest episode content when generating legal drama episodes. For example, the generation unit can analyze the client's social media posts and suggest the most suitable episode content. The generation unit can also suggest episode content by referring to the client's social media activity history. Furthermore, the generation unit can analyze the client's social media friendships and suggest the most suitable episode content. For example, the generation unit can analyze the client's social media posts and suggest the most suitable episode content. It can suggest episode content by referring to the client's social media activity history. It can analyze friendships and suggest the most suitable episode content. In this way, the optimal episode content can be suggested by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the client's social media activity data into a generation AI and have the generation AI suggest episode content.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The legal consultation support robot can analyze a client's past consultation history and provide optimal legal advice. For example, the acquisition unit retrieves the client's past consultation history from a database, and the emotion estimation unit analyzes that data to estimate the client's emotions. The provision unit can then provide optimal legal advice based on the past consultation history and current emotions. For example, if the client has faced a similar problem in the past, the robot can use the solution from that problem as a reference to provide advice. It can also understand the client's tendencies from past consultation history and provide more personalized advice. This allows the robot to utilize past consultation history to provide more appropriate legal advice.

[0054] The legal consultation support robot can provide legal advice while considering the client's current living situation. For example, the acquisition unit acquires information about the client's current living situation, and the emotion estimation unit analyzes that information to estimate the client's emotions. The provision unit can then provide optimal legal advice based on the client's current living situation and emotions. For example, if the client is busy with work, it can provide advice that can be implemented in a short amount of time. Furthermore, if the client is facing family problems, it can provide specific advice on how to address those problems. This allows the robot to provide appropriate legal advice tailored to the client's current living situation.

[0055] The legal consultation support robot can provide legal advice while taking into account the geographical location of the person seeking advice. For example, the acquisition unit acquires information about the person's current location, and the emotion estimation unit analyzes that information to estimate the person's emotions. The provision unit can then provide optimal legal advice based on the geographical location and emotions. For example, if the person lives in a specific area, it can provide advice on the laws of that area. If the person is traveling, it can also provide advice on the laws of their travel destination. This allows the robot to provide appropriate legal advice tailored to the person's geographical location.

[0056] The legal consultation support robot can provide legal advice by analyzing the social media activity of the person seeking advice. For example, the acquisition unit acquires the content of the person's social media posts, and the sentiment estimation unit analyzes that content to estimate the person's emotions. The provision unit can then provide optimal legal advice based on the social media activity and emotions. For example, if the person frequently posts about a particular issue on social media, the robot can provide advice on how to address that issue. It can also analyze the person's social media friendships and provide advice on how to gain support from friends. This allows the robot to provide appropriate legal advice tailored to the person's social media activity.

[0057] The legal consultation support robot can provide legal advice while considering the client's attribute information (age, gender, etc.). For example, the acquisition unit acquires the client's attribute information, and the emotion estimation unit analyzes that information to estimate the client's emotions. The provision unit can then provide optimal legal advice based on the attribute information and emotions. For example, if the client is young, it can provide advice on future careers. If the client is elderly, it can also provide advice on inheritance. This allows for the provision of appropriate legal advice tailored to the client's attribute information.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the client's emotions. Emotion estimation information includes audio data, facial expression data, and text data. For example, the acquisition unit captures the client's facial expression with a camera and acquires facial expression data. It can also record the client's voice tone with a microphone and acquire audio data. Furthermore, it can acquire the client's speech as text data. Thus, the acquisition unit captures the client's facial expression with a high-resolution camera and acquires facial expression data using facial expression recognition technology. Audio data is acquired by recording high-quality audio with a microphone and acquiring data using audio analysis technology. Text data is acquired by transcribing the client's statements in real time and acquiring data using natural language processing technology. Step 2: The emotion estimation unit estimates the client's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit uses AI to analyze the client's emotions and identify emotions such as anxiety, worry, anger, and sadness. For example, the emotion estimation unit uses an AI model to analyze facial expression data and estimate the client's emotions. It can also analyze voice data and estimate emotions from the tone and speed of the voice. Furthermore, it can analyze text data and estimate emotions from the word choice. Thus, the emotion estimation unit uses a deep learning model to analyze facial expression data and identify emotions. Voice data is analyzed using speech recognition technology to estimate emotions from the tone and speed of the voice. Text data is analyzed using natural language processing technology to estimate emotions from the word choice. Step 3: The service provider provides legal advice based on the emotions estimated by the emotion estimation unit. For clients who are feeling anxious, the service provider provides reassurance by using calming language and providing specific, easy-to-understand advice. If the client is feeling anger or sadness, the service provider can respond with a sympathetic yet constructive approach to alleviate the client's anxiety. Furthermore, for clients with multiple problems, the service provider can advise on prioritizing each problem based on its urgency and relevance. For example, the service provider will advise addressing the most urgent problems first. This allows the client to resolve problems efficiently. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can provide advice using an AI model that takes the emotions estimated by the emotion estimation unit as input and outputs legal advice.

[0060] (Example of form 2) The legal consultation support robot according to an embodiment of the present invention is a system that uses an emotion engine to estimate the emotions of a client and provides appropriate legal advice. This system estimates the emotions of the client and provides appropriate legal advice. For example, the client's facial expressions, tone of voice, and word choice are acquired as information for emotion estimation. Next, the system estimates the client's emotions based on the acquired information. The emotion engine uses AI to analyze the client's emotions and identify emotions such as anxiety, worry, anger, and sadness. For example, if the client is feeling anxious, the emotion engine detects that anxiety and takes appropriate action. Next, it provides legal advice based on the estimated emotions. For example, for a client who is feeling anxious, it provides reassurance by using calming language and giving specific and easy-to-understand advice. Also, if the client is feeling anger or sadness, it responds with a sympathetic yet constructive approach to reduce the client's anxiety. This provides highly reliable legal consultation. Furthermore, for clients with multiple problems, it advises prioritizing based on the urgency and relevance of each problem. For example, if a client has multiple legal problems, it advises addressing the most urgent problem first. This allows the client to solve problems efficiently. Furthermore, the system creates legal drama episodes based on the client's emotions and their specific case. The generating AI creates episodes tailored to the client's case, explaining legal issues in an easy-to-understand manner. For example, if the client is facing divorce issues, the system generates and provides a legal drama episode about divorce. This makes it easier for the client to understand the legal issues relevant to their case. In addition, the generated legal drama episodes may be provided in VR. This allows the client to understand legal issues through a more realistic experience. For example, by watching a legal drama episode using VR, the client can experience the atmosphere and flow of a real courtroom. Thus, the present invention is a system that uses an emotion engine and generating AI to estimate the client's emotions and provide appropriate legal advice, thereby reducing the client's anxiety and providing reliable legal consultations.This allows the legal consultation support robot to estimate the client's emotions and provide appropriate legal advice.

[0061] The legal consultation support robot according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the person seeking advice. Emotion estimation information includes, but is not limited to, voice data, facial expression data, and text data. For example, the acquisition unit can capture the person's facial expression with a camera and acquire facial expression data. The acquisition unit can also record the tone of the person's voice with a microphone and acquire voice data. Furthermore, the acquisition unit can acquire the person's speech as text data. For example, the acquisition unit can capture the person's facial expression with a high-resolution camera and acquire facial expression data using facial expression recognition technology. For voice data, high-quality voice is recorded using a microphone and data is acquired using voice analysis technology. For text data, the person's speech is transcribed in real time and data is acquired using natural language processing technology. The emotion estimation unit estimates the person's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the person's emotions using AI and identifies emotions such as anxiety, worry, anger, and sadness. The emotion estimation unit analyzes facial expression data using an AI model to estimate the client's emotions. It can also analyze voice data to estimate emotions from tone and speed. Furthermore, it can analyze text data to estimate emotions from word choice. For example, the emotion estimation unit uses a deep learning model to analyze facial expression data and identify emotions. Voice data is analyzed using speech recognition technology to estimate emotions from tone and speed. Text data is analyzed using natural language processing technology to estimate emotions from word choice. The service provider provides legal advice based on the emotions estimated by the emotion estimation unit. For example, the service provider can provide reassurance to an anxious client by using calming language and providing specific, easy-to-understand advice. If the client is feeling anger or sadness, the service provider can respond with a sympathetic yet constructive approach to alleviate the client's anxiety. Furthermore, the service provider can advise clients with multiple problems on prioritizing issues based on their urgency and relevance.For example, the service provider advises addressing the most urgent issues first. This allows the client to solve problems efficiently. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can provide advice using an AI model that takes emotions estimated by the emotion estimation unit as input and outputs legal advice. This allows the legal consultation support robot according to the embodiment to estimate the client's emotions and provide appropriate legal advice.

[0062] The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the person seeking advice. Emotion estimation information includes, but is not limited to, voice data, facial expression data, and text data. For example, the acquisition unit can capture the person's facial expression with a camera and acquire facial expression data. Specifically, it uses a high-resolution camera to capture subtle changes in the person's facial expression and analyzes the data using facial expression recognition technology. This allows for highly accurate detection of emotions such as smiles, anger, and surprise. The acquisition unit can also record the tone of the person's voice with a microphone and acquire voice data. By using a high-quality microphone and combining it with noise cancellation technology, clear voice data is collected. Voice analysis technology is used to extract features such as tone, pitch, speed, and volume of the voice, which are used to estimate emotions. Furthermore, the acquisition unit can acquire the person's speech as text data. For example, speech recognition technology is used to transcribe speech in real time. Natural language processing technology is used to acquire data for estimating emotions from the context of the speech and the choice of words. This allows the acquisition unit to efficiently collect diverse emotional information from the client and provide it to the emotional estimation unit.

[0063] The emotion estimation unit estimates the client's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit uses AI to analyze the client's emotions and identify emotions such as anxiety, worry, anger, and sadness. Specifically, it uses deep learning models to analyze facial expression data and estimate the client's emotions. For example, it uses a convolutional neural network (CNN) to extract and classify emotional features from facial expression data. Voice data is analyzed using speech recognition technology to estimate emotions from voice tone and speed. Recurrent neural networks (RNNs) and long-term short-term memory (LSTM) models are used to capture temporal changes in voice data and identify emotions. Text data is analyzed using natural language processing technology to estimate emotions from word choice. For example, a transformer model is used to understand the context of text data and classify emotions. As a result, the emotion estimation unit can comprehensively analyze the diverse data acquired and estimate the client's emotions with high accuracy. Furthermore, the emotion estimation unit can continuously improve its estimation accuracy by learning from past data and cases.

[0064] The service provider provides legal advice based on the emotions estimated by the emotion estimation unit. For example, to a client who is feeling anxious, the service provider provides reassurance by using calming language and giving specific, easy-to-understand advice. Specifically, it uses an AI model to generate optimal advice that matches the client's emotions. For example, it uses natural language generation technology to select language and expressions that resonate with the client's emotions and provide appropriate advice. Furthermore, if the client is feeling anger or sadness, the service provider can respond with a sympathetic yet constructive approach to alleviate the client's anxiety. For example, it can soothe the client's emotions by using empathetic language and presenting specific solutions. In addition, the service provider can advise clients with multiple problems on prioritizing them based on the urgency and relevance of each problem. For example, it can advise addressing the most urgent problem first. This allows the client to solve problems efficiently. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can provide advice using an AI model that takes the emotions estimated by the emotion estimation unit as input and outputs legal advice. This allows the service provider to quickly deliver appropriate legal advice tailored to the client's emotions, thereby improving client satisfaction.

[0065] The generation unit can create legal drama episodes tailored to the client's case, based on the client's emotions and their specific circumstances. For example, the generation unit uses a generation AI to generate episodes relevant to the client's case. For instance, if the client is facing divorce issues, the generation unit generates and provides a legal drama episode about divorce. Similarly, if the client is facing labor issues, the generation unit can generate a legal drama episode about labor. Furthermore, if the client is facing inheritance issues, the generation unit can generate a legal drama episode about inheritance. For example, the generation unit uses a generation AI to generate a scenario related to divorce, constructing characters and a storyline. Episodes related to labor issues generate scenarios based on labor law, illustrating specific examples. Episodes related to inheritance issues generate scenarios based on inheritance law, explaining the procedures for estate division. This makes it easier for the client to understand the legal issues relevant to their own case. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without one. For example, the generation unit can input information about the client's case into the generation AI and have the generation AI generate the episodes. This allows us to create legal drama episodes tailored to each client's case, making legal issues easier to understand.

[0066] The service provider can provide legal dramas of episodes generated by the generation unit. For example, the service provider can provide episodes generated using a generation AI in video format. For example, the service provider can play the generated episode as a video and allow the client to watch it. The service provider can also provide the generated episode in audio format. For example, the service provider can play the generated episode as audio and allow the client to listen to it. Furthermore, the service provider can also provide the generated episode in text format. For example, the service provider can display the generated episode as text and allow the client to read it. In this way, by providing legal dramas of generated episodes, legal issues can be explained to clients in an easy-to-understand manner. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide episodes using an AI model that takes episodes generated by the generation unit as input and outputs them as video, audio, or text.

[0067] The service provider can provide legal drama episodes generated by the generation unit in VR. For example, the service provider can provide episodes generated using a generation AI in VR format. For example, the service provider can play the generated episode using a VR headset and allow the client to view it. The service provider can also provide the generated episode as a 360-degree video. For example, the service provider can play the generated episode as a 360-degree video and allow the client to view it. Furthermore, the service provider can provide the generated episode as interactive content. For example, the service provider can provide the generated episode in an interactive format, allowing the client to choose options for the scenario. By providing legal drama episodes in VR, the service provider can help clients understand legal issues through a more realistic experience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide episodes using an AI model that takes episodes generated by the generation unit as input and outputs them in VR format.

[0068] The acquisition unit can estimate the client's emotions and adjust the timing of acquiring emotion estimation information based on the estimated emotions. For example, if the client is tense, the acquisition unit waits for time to allow the client to relax before acquiring emotion estimation information. The acquisition unit can also quickly acquire emotion estimation information if the client is in a hurry. Furthermore, if the client is calm, the acquisition unit can acquire detailed emotion estimation information. For example, the acquisition unit waits for time to allow the client to relax and acquires facial expression data and voice data. If the client is in a hurry, it acquires the necessary information in a short time and performs rapid analysis. If the client is calm, it collects detailed data and performs more accurate emotion estimation. In this way, by adjusting the timing of acquiring emotion estimation information according to the client's emotions, more appropriate information can be obtained. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data for estimating the client's emotions into a generating AI and have the generating AI adjust the acquisition timing.

[0069] The data acquisition unit can analyze the client's past consultation history and select the optimal data acquisition method. For example, the data acquisition unit may prioritize selecting data acquisition methods for emotion estimation information that the client has used in the past. The data acquisition unit can also select the most effective data acquisition method from the client's past consultation history. Furthermore, the data acquisition unit can analyze the client's past consultation history and customize the data acquisition method. For example, the data acquisition unit may prioritize selecting data acquisition methods for emotion estimation information that have been used in the past and select an effective method. It may analyze past consultation history and select the optimal data acquisition method. It may customize the data acquisition method to acquire the most suitable information for the client. In this way, the optimal data acquisition method can be selected by analyzing past consultation history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit may input past consultation history data into a generating AI and have the generating AI select the optimal data acquisition method.

[0070] The acquisition unit can filter the information for emotion estimation based on the client's current living situation and areas of interest when acquiring the information. For example, the acquisition unit prioritizes acquiring highly relevant emotion estimation information based on the client's current living situation. The acquisition unit can also filter the emotion estimation information based on the client's areas of interest. Furthermore, the acquisition unit can combine the client's living situation and areas of interest to acquire the most suitable emotion estimation information. For example, the acquisition unit prioritizes acquiring highly relevant information based on the client's current living situation. It filters the emotion estimation information based on the areas of interest. It acquires the most suitable information by combining the living situation and areas of interest. This allows the acquisition of highly relevant information by filtering the information based on the client's living situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the client's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0071] The data acquisition unit can estimate the client's emotions and determine the priority of emotion estimation information to acquire based on the estimated emotions. For example, if the client is feeling anxious, the data acquisition unit will prioritize acquiring emotion estimation information related to anxiety. The data acquisition unit can also prioritize acquiring emotion estimation information related to anger if the client is feeling angry. Furthermore, if the client is feeling sad, the data acquisition unit can also prioritize acquiring emotion estimation information related to sadness. For example, if the client is feeling anxious, the data acquisition unit will prioritize acquiring information related to anxiety. If the client is feeling angry, it will prioritize acquiring information related to anger. If the client is feeling sad, it will prioritize acquiring information related to sadness. In this way, by determining the priority of information based on the client's emotions, important information can be acquired preferentially. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without using AI. For example, the data acquisition unit can input the client's emotion data into a generating AI and have the generating AI perform the priority determination.

[0072] The acquisition unit can prioritize the acquisition of highly relevant information by considering the client's geographical location when acquiring information for emotion estimation. For example, the acquisition unit can acquire highly relevant emotion estimation information based on the client's current location. The acquisition unit can also acquire emotion estimation information by referring to the client's past location information. Furthermore, the acquisition unit can analyze the relationship between the client's geographical location information and emotion estimation information and acquire the most suitable information. For example, the acquisition unit can acquire highly relevant information based on the client's current location. It can acquire emotion estimation information by referring to past location information. It can analyze the relationship between geographical location information and emotion estimation information and acquire the most suitable information. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the client's geographical location information into a generating AI and have the generating AI acquire highly relevant information.

[0073] The acquisition unit can analyze the client's social media activity and obtain relevant information when acquiring information for sentiment estimation. For example, the acquisition unit can analyze the client's social media posts and acquire information for sentiment estimation. The acquisition unit can also acquire information for sentiment estimation by referring to the client's social media activity history. Furthermore, the acquisition unit can analyze the client's social media friendships and acquire relevant information for sentiment estimation. For example, the acquisition unit can analyze the client's social media posts and acquire relevant information. It can acquire information for sentiment estimation by referring to the client's social media activity history. It can analyze friendships and acquire relevant information. In this way, relevant information can be acquired by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input the client's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0074] The emotion estimation unit can estimate the client's emotions and adjust the emotion estimation algorithm based on the estimated emotions. For example, if the client is feeling anxious, the emotion estimation unit can use an emotion estimation algorithm specialized for anxiety. It can also use an emotion estimation algorithm specialized for anger if the client is feeling anger. Furthermore, if the client is feeling sadness, the emotion estimation unit can use an emotion estimation algorithm specialized for sadness. For example, if the client is feeling anxious, the emotion estimation unit uses an anxiety-specific algorithm to estimate the emotion. If the client is feeling anger, it uses an anger-specific algorithm to estimate the emotion. If the client is feeling sadness, it uses a sadness-specific algorithm to estimate the emotion. This improves the accuracy of emotion estimation by adjusting the algorithm based on the client's emotions. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the client's emotion data into a generating AI and have the generating AI adjust the algorithm.

[0075] The emotion estimation unit can improve the accuracy of its estimation by referring to the client's past emotion data during emotion estimation. For example, the emotion estimation unit can refer to the client's past emotion data to estimate the current emotion. The emotion estimation unit can also analyze the client's past emotion data and adjust the estimation algorithm. Furthermore, the emotion estimation unit can improve the accuracy of emotion estimation based on the client's past emotion data. For example, the emotion estimation unit can refer to past emotion data to estimate the current emotion. It can analyze past emotion data and adjust the algorithm. It can improve the accuracy of emotion estimation based on past emotion data. In this way, the accuracy of emotion estimation is improved by referring to past emotion data. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without using AI. For example, the emotion estimation unit can input past emotion data into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0076] The emotion estimation unit can perform emotion estimation while considering the client's attribute information (age, gender, etc.). For example, the emotion estimation unit can adjust the emotion estimation algorithm based on the client's age. It can also adjust the emotion estimation algorithm based on the client's gender. Furthermore, the emotion estimation unit can perform emotion estimation by comprehensively considering the client's attribute information. For example, the emotion estimation unit can adjust the algorithm based on age and estimate emotions. It can adjust the algorithm based on gender and estimate emotions. It can comprehensively consider attribute information and estimate emotions. This improves the accuracy of emotion estimation by considering attribute information. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without using AI. For example, the emotion estimation unit can input the client's attribute information into a generating AI and have the generating AI perform the estimation.

[0077] The emotion estimation unit can estimate the client's emotions and adjust the display method of the emotion estimation result based on the estimated emotions of the client. For example, if the client is feeling anxious, the emotion estimation unit can provide a simple and highly visible display method. It can also provide a calm display method if the client is feeling angry. Furthermore, it can provide a gentle display method if the client is feeling sad. For example, if the client is feeling anxious, the emotion estimation unit provides a simple and highly visible display method. If the client is feeling angry, it provides a calm display method. If the client is feeling sad, it provides a gentle display method. This allows for a highly visible display by adjusting the display method based on the client's emotions. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the client's emotion data into a generating AI and have the generating AI perform the adjustment of the display method.

[0078] The emotion estimation unit can perform emotion estimation while considering the geographical distribution of the client. For example, the emotion estimation unit adjusts the emotion estimation algorithm based on the client's place of residence. The emotion estimation unit can also analyze the geographical distribution of the client to improve the accuracy of emotion estimation. Furthermore, the emotion estimation unit can perform estimation while considering the relationship between the client's geographical distribution and emotion. For example, the emotion estimation unit adjusts the algorithm based on place of residence and estimates emotion. It analyzes the geographical distribution to improve the accuracy of emotion estimation. It considers the relationship between geographical distribution and emotion and performs estimation. As a result, the accuracy of emotion estimation is improved by considering geographical distribution. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the client's geographical distribution data into a generating AI and have the generating AI perform the estimation.

[0079] The emotion estimation unit can improve the accuracy of its estimation by referring to the client's relevant literature during the emotion estimation process. For example, the emotion estimation unit can adjust its emotion estimation algorithm by referring to the client's relevant literature. The emotion estimation unit can also improve the accuracy of its emotion estimation by analyzing the client's relevant literature. Furthermore, the emotion estimation unit can improve the accuracy of its emotion estimation based on the client's relevant literature. For example, the emotion estimation unit refers to relevant literature, adjusts its algorithm, and estimates emotions. It analyzes relevant literature to improve the accuracy of emotion estimation. It improves the accuracy of emotion estimation based on relevant literature. Thus, the accuracy of emotion estimation is improved by referring to relevant literature. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the client's relevant literature data into a generating AI and have the generating AI perform the estimation accuracy improvement.

[0080] The service provider can estimate the client's emotions and adjust the way legal advice is expressed based on those estimated emotions. For example, if the client is feeling anxious, the service provider can provide advice using calming language. Similarly, if the client is feeling angry, the service provider can provide advice using calm and constructive language. Furthermore, if the client is feeling sad, the service provider can provide advice using sympathetic and gentle language. In this way, by adjusting the way advice is expressed based on the client's emotions, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the client's emotional data into a generating AI and have the generating AI adjust the expression.

[0081] The service provider can provide optimal advice by referring to the client's past consultations when providing legal advice. For example, the service provider can refer to the client's past consultations and provide advice based on similar cases. The service provider can also analyze the client's past consultations and provide optimal advice. Furthermore, the service provider can provide customized advice based on the client's past consultations. For example, the service provider can refer to past consultations and provide advice based on similar cases. It can analyze past consultations and provide optimal advice. It can provide customized advice based on past consultations. In this way, optimal advice can be provided by referring to past consultations. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past consultation data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0082] The service provider can customize legal advice by taking into account the client's attribute information (age, gender, etc.). For example, the service provider can provide appropriate advice based on the client's age. It can also provide appropriate advice based on the client's gender. Furthermore, the service provider can provide customized advice by comprehensively considering the client's attribute information. For example, the service provider can provide appropriate advice based on age. It can provide appropriate advice based on gender. It can provide customized advice by comprehensively considering attribute information. This allows for the provision of more appropriate advice by taking attribute information into account. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the client's attribute information into a generating AI and have the generating AI perform the customization of the advice.

[0083] The service provider can estimate the client's emotions and determine the priority of legal advice based on those estimated emotions. For example, if the client is feeling anxious, the service provider will prioritize providing advice to alleviate that anxiety. Similarly, if the client is feeling angry, the service provider can prioritize providing advice to calm their anger. Furthermore, if the client is feeling sad, the service provider can prioritize providing advice to alleviate their sadness. In this way, by prioritizing advice based on the client's emotions, important advice can be provided preferentially. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the client's emotional data into a generating AI and have the generating AI perform the priority determination.

[0084] The service provider can provide optimal legal advice by considering the client's geographical location. For example, the service provider can provide advice on local laws based on the client's current location. The service provider can also provide optimal advice by referring to the client's past location information. Furthermore, the service provider can analyze the relationship between the client's geographical location information and legal advice to provide optimal advice. For example, the service provider can provide advice on local laws based on the client's current location. It can provide optimal advice by referring to past location information. It can analyze the relationship between geographical location information and legal advice to provide optimal advice. In this way, by considering geographical location information, it is possible to provide advice that is appropriate for the region. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the client's geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0085] The service provider can analyze the client's social media activity and propose methods of advice when providing legal advice. For example, the service provider can analyze the content of the client's social media posts and propose the most suitable method of advice. The service provider can also propose methods of advice by referring to the client's social media activity history. Furthermore, the service provider can analyze the client's social media friendships and propose the most suitable method of advice. For example, the service provider can analyze the content of the client's social media posts and propose the most suitable method of advice. It can propose methods of advice by referring to the client's social media activity history. It can analyze friendships and propose the most suitable method of advice. In this way, by analyzing social media activity, the service provider can propose the most suitable method of advice. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the client's social media activity data into a generating AI and have the generating AI execute the proposal of methods of advice.

[0086] The generation unit can estimate the client's emotions and adjust the content of the legal drama episodes based on the estimated emotions. For example, if the client is feeling anxious, the generation unit can generate an episode that alleviates that anxiety. It can also generate an episode that calms anger if the client is feeling angry. Furthermore, if the client is feeling sadness, the generation unit can generate an episode that soothes that sadness. For example, if the client is feeling anxious, the generation unit generates an episode that alleviates that anxiety. If the client is feeling angry, it generates an episode that calms that anger. If the client is feeling sadness, it generates an episode that soothes that sadness. This allows for the provision of more appropriate episodes by adjusting the content based on the client's emotions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the client's emotional data into a generation AI and have the generation AI perform the adjustment of the episode content.

[0087] The generation unit can generate the most suitable episode for a legal drama by referring to the client's past consultations. For example, the generation unit can refer to the client's past consultations and generate an episode based on similar cases. The generation unit can also analyze the client's past consultations and generate the most suitable episode. Furthermore, the generation unit can generate a customized episode based on the client's past consultations. For example, the generation unit can refer to past consultations and generate an episode based on similar cases. It can analyze past consultations and generate the most suitable episode. It can generate a customized episode based on past consultations. In this way, the most suitable episode can be generated by referring to past consultations. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past consultation data into a generation AI and have the generation AI perform the generation of the most suitable episode.

[0088] The generation unit can customize episodes for legal dramas by considering the client's attribute information (age, gender, etc.). For example, the generation unit can generate an appropriate episode based on the client's age. It can also generate an appropriate episode based on the client's gender. Furthermore, the generation unit can generate a customized episode by comprehensively considering the client's attribute information. For example, the generation unit can generate an appropriate episode based on age. It can generate an appropriate episode based on gender. It can generate a customized episode by comprehensively considering attribute information. This allows for the generation of more appropriate episodes by considering attribute information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the client's attribute information into a generation AI and have the generation AI perform the episode customization.

[0089] The generation unit can estimate the client's emotions and adjust the display method of the legal drama episodes based on the estimated emotions. For example, if the client is feeling anxious, the generation unit can provide a simple and highly visible display method. It can also provide a calm display method if the client is feeling angry. Furthermore, it can provide a gentle display method if the client is feeling sad. For example, if the client is feeling anxious, the generation unit provides a simple and highly visible display method. If the client is feeling angry, it provides a calm display method. If the client is feeling sad, it provides a gentle display method. This allows for highly visible displays by adjusting the display method based on the client's emotions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the client's emotion data into a generation AI and have the generation AI adjust the display method.

[0090] The generation unit can generate the most suitable episode for a legal drama by considering the client's geographical location information. For example, the generation unit can generate an episode related to local law based on the client's current location. The generation unit can also generate the most suitable episode by referring to the client's past location information. Furthermore, the generation unit can analyze the relationship between the client's geographical location information and legal episodes to generate the most suitable episode. For example, the generation unit can generate an episode related to local law based on the client's current location. It can generate the most suitable episode by referring to past location information. It can analyze the relationship between geographical location information and legal episodes to generate the most suitable episode. In this way, by considering geographical location information, it is possible to generate episodes that are appropriate for the region. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the client's geographical location information into a generation AI and have the generation AI perform the generation of the most suitable episode.

[0091] The generation unit can analyze the client's social media activity and suggest episode content when generating legal drama episodes. For example, the generation unit can analyze the client's social media posts and suggest the most suitable episode content. The generation unit can also suggest episode content by referring to the client's social media activity history. Furthermore, the generation unit can analyze the client's social media friendships and suggest the most suitable episode content. For example, the generation unit can analyze the client's social media posts and suggest the most suitable episode content. It can suggest episode content by referring to the client's social media activity history. It can analyze friendships and suggest the most suitable episode content. In this way, the optimal episode content can be suggested by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the client's social media activity data into a generation AI and have the generation AI suggest episode content.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The legal consultation support robot can estimate the client's emotions and assess their stress level based on those emotions. For example, the emotion estimation unit estimates the client's emotions from their facial expressions, tone of voice, and word choice, and then assesses their stress level based on those emotions. If the stress level is high, the service provider can offer advice for relaxation and resources for stress reduction. If the stress level is low, the service provider can offer more detailed legal advice. Furthermore, the robot can monitor changes in the stress level and adjust the content and method of advice as needed. This allows the robot to provide appropriate support tailored to the client's stress level.

[0094] The legal consultation support robot can estimate the client's emotions and assess their health based on those emotions. For example, the emotion estimation unit estimates emotions from the client's facial expressions, tone of voice, and word choice, and assesses their health based on those emotions. If the health is poor, the service provider can offer advice and resources for improving their health. If the health is good, the service provider can offer more proactive legal advice. Furthermore, it can monitor changes in the health and adjust the content and method of advice as needed. This allows for the provision of appropriate support tailored to the client's health condition.

[0095] The legal consultation support robot can estimate the client's emotions and evaluate their communication style based on those emotions. For example, the emotion estimation unit estimates the client's emotions from their facial expressions, tone of voice, and word choice, and evaluates their communication style based on those emotions. If the communication style is positive, the service provider can provide more detailed legal advice. If the communication style is negative, the service provider can provide concise and easy-to-understand advice that is easy for the client to comprehend. Furthermore, the robot can monitor changes in the communication style and adjust the content and method of advice as needed. This allows for the provision of appropriate support tailored to the client's communication style.

[0096] The legal consultation support robot can estimate the client's emotions and evaluate their learning style based on those emotions. For example, the emotion estimation unit estimates emotions from the client's facial expressions, tone of voice, and word choice, and evaluates their learning style based on those emotions. If the learning style is visual, the service provider can provide legal advice using diagrams and graphs. If the learning style is auditory, the service provider can provide legal advice using voice. Furthermore, it can monitor changes in the learning style and adjust the content and method of advice as needed. This allows for the provision of appropriate support tailored to the client's learning style.

[0097] The legal consultation support robot can estimate the client's emotions and evaluate their motivation level based on those emotions. For example, the emotion estimation unit estimates the client's emotions from their facial expressions, tone of voice, and word choice, and evaluates their motivation level based on those emotions. If the motivation level is high, the service provider can offer more challenging legal advice. If the motivation level is low, the service provider can offer concise and specific advice that is easy for the client to implement. Furthermore, the robot can monitor changes in the motivation level and adjust the content and method of advice as needed. This allows for the provision of appropriate support tailored to the client's motivation level.

[0098] The legal consultation support robot can analyze a client's past consultation history and provide optimal legal advice. For example, the acquisition unit retrieves the client's past consultation history from a database, and the emotion estimation unit analyzes that data to estimate the client's emotions. The provision unit can then provide optimal legal advice based on the past consultation history and current emotions. For example, if the client has faced a similar problem in the past, the robot can use the solution from that problem as a reference to provide advice. It can also understand the client's tendencies from past consultation history and provide more personalized advice. This allows the robot to utilize past consultation history to provide more appropriate legal advice.

[0099] The legal consultation support robot can provide legal advice while considering the client's current living situation. For example, the acquisition unit acquires information about the client's current living situation, and the emotion estimation unit analyzes that information to estimate the client's emotions. The provision unit can then provide optimal legal advice based on the client's current living situation and emotions. For example, if the client is busy with work, it can provide advice that can be implemented in a short amount of time. Furthermore, if the client is facing family problems, it can provide specific advice on how to address those problems. This allows the robot to provide appropriate legal advice tailored to the client's current living situation.

[0100] The legal consultation support robot can provide legal advice while taking into account the geographical location of the person seeking advice. For example, the acquisition unit acquires information about the person's current location, and the emotion estimation unit analyzes that information to estimate the person's emotions. The provision unit can then provide optimal legal advice based on the geographical location and emotions. For example, if the person lives in a specific area, it can provide advice on the laws of that area. If the person is traveling, it can also provide advice on the laws of their travel destination. This allows the robot to provide appropriate legal advice tailored to the person's geographical location.

[0101] The legal consultation support robot can provide legal advice by analyzing the social media activity of the person seeking advice. For example, the acquisition unit acquires the content of the person's social media posts, and the sentiment estimation unit analyzes that content to estimate the person's emotions. The provision unit can then provide optimal legal advice based on the social media activity and emotions. For example, if the person frequently posts about a particular issue on social media, the robot can provide advice on how to address that issue. It can also analyze the person's social media friendships and provide advice on how to gain support from friends. This allows the robot to provide appropriate legal advice tailored to the person's social media activity.

[0102] The legal consultation support robot can provide legal advice while considering the client's attribute information (age, gender, etc.). For example, the acquisition unit acquires the client's attribute information, and the emotion estimation unit analyzes that information to estimate the client's emotions. The provision unit can then provide optimal legal advice based on the attribute information and emotions. For example, if the client is young, it can provide advice on future careers. If the client is elderly, it can also provide advice on inheritance. This allows for the provision of appropriate legal advice tailored to the client's attribute information.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the client's emotions. Emotion estimation information includes audio data, facial expression data, and text data. For example, the acquisition unit captures the client's facial expression with a camera and acquires facial expression data. It can also record the client's voice tone with a microphone and acquire audio data. Furthermore, it can acquire the client's speech as text data. Thus, the acquisition unit captures the client's facial expression with a high-resolution camera and acquires facial expression data using facial expression recognition technology. Audio data is acquired by recording high-quality audio with a microphone and acquiring data using audio analysis technology. Text data is acquired by transcribing the client's statements in real time and acquiring data using natural language processing technology. Step 2: The emotion estimation unit estimates the client's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit uses AI to analyze the client's emotions and identify emotions such as anxiety, worry, anger, and sadness. For example, the emotion estimation unit uses an AI model to analyze facial expression data and estimate the client's emotions. It can also analyze voice data and estimate emotions from the tone and speed of the voice. Furthermore, it can analyze text data and estimate emotions from the word choice. Thus, the emotion estimation unit uses a deep learning model to analyze facial expression data and identify emotions. Voice data is analyzed using speech recognition technology to estimate emotions from the tone and speed of the voice. Text data is analyzed using natural language processing technology to estimate emotions from the word choice. Step 3: The service provider provides legal advice based on the emotions estimated by the emotion estimation unit. For clients who are feeling anxious, the service provider provides reassurance by using calming language and providing specific, easy-to-understand advice. If the client is feeling anger or sadness, the service provider can respond with a sympathetic yet constructive approach to alleviate the client's anxiety. Furthermore, for clients with multiple problems, the service provider can advise on prioritizing each problem based on its urgency and relevance. For example, the service provider will advise addressing the most urgent problems first. This allows the client to resolve problems efficiently. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can provide advice using an AI model that takes the emotions estimated by the emotion estimation unit as input and outputs legal advice.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] For example, the acquisition unit can acquire the client's facial expressions and tone of voice using the camera 42 and microphone 38B of the smart device 14. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the client's emotions by analyzing the emotion estimation information obtained from the acquisition unit. The provision unit is implemented by the control unit 46A of the smart device 14 and provides appropriate legal advice based on the estimated emotions. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates episodes of a legal drama tailored to the client's case using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] For example, the acquisition unit can acquire the client's facial expressions and tone of voice using the camera 42 and microphone 238 of the smart glasses 214. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the client's emotions by analyzing the emotion estimation information obtained from the acquisition unit. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides appropriate legal advice based on the estimated emotions. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates episodes of a legal drama tailored to the client's case using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] For example, the acquisition unit can acquire the client's facial expressions and tone of voice using the camera 42 and microphone 238 of the headset terminal 314. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the client's emotions by analyzing the emotion estimation information obtained from the acquisition unit. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides appropriate legal advice based on the estimated emotions. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates episodes of a legal drama tailored to the client's case using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] For example, the acquisition unit can acquire the client's facial expressions and tone of voice using the camera 42 and microphone 238 of the robot 414. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the client's emotions by analyzing the emotion estimation information obtained from the acquisition unit. The provision unit is implemented by the control unit 46A of the robot 414 and provides appropriate legal advice based on the estimated emotions. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates episodes of a legal drama tailored to the client's case using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of the person seeking advice, An emotion estimation unit estimates the emotions of the person seeking advice based on the emotion estimation information acquired by the acquisition unit, The system comprises: a provisioning unit that provides legal advice based on the emotions estimated by the emotion estimation unit; A system characterized by the following features. (Note 2) The system includes a generation unit that creates legal drama episodes tailored to the client's case, based on the client's emotions and the client's specific case. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The generation unit provides a legal drama of episodes generated by the aforementioned generation unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The legal drama episodes generated by the aforementioned generation unit are provided in VR. The system described in Appendix 2, characterized by the features described herein. (Note 5) The acquisition unit is, The system estimates the client's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, Analyze the client's past consultation history and select the appropriate method for obtaining the information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring information for emotion estimation, filtering is performed based on the client's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system estimates the client's emotions and determines the priority of emotion estimation information to be obtained based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring information for emotion estimation, the system prioritizes acquiring highly relevant information by considering the geographical location of the person seeking advice. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring information for emotion estimation, the social media activity of the person seeking advice is analyzed, and relevant information is obtained. The system described in Appendix 1, characterized by the features described herein. (Note 11) The emotion estimation unit, The system estimates the client's emotions and adjusts the emotion estimation algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The emotion estimation unit, When estimating emotions, the accuracy of the estimation is improved by referring to the client's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The emotion estimation unit, When estimating emotions, the client's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The emotion estimation unit, The system estimates the client's emotions and adjusts the display method of the emotion estimation results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The emotion estimation unit, When estimating emotions, the geographical distribution of the people seeking advice is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The emotion estimation unit, When estimating emotions, we improve the accuracy of the estimation by referring to relevant literature on the person seeking advice. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We estimate the client's emotions and adjust the way legal advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing legal advice, we refer to the client's past consultations to provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing legal advice, we customize the advice by taking into account the client's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the client's emotions and prioritize legal advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing legal advice, we take into account the client's geographical location to provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing legal advice, we analyze the client's social media activity and propose methods for providing advice. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is The program estimates the client's emotions and adjusts the content of the legal drama episodes based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The generating unit is When generating episodes for a legal drama, the system references the client's past consultations to generate appropriate episodes. The system described in Appendix 2, characterized by the features described herein. (Note 25) The generating unit is When generating episodes for legal dramas, customize the episodes by taking into account the attribute information of the people seeking advice. The system described in Appendix 2, characterized by the features described herein. (Note 26) The generating unit is The system estimates the client's emotions and adjusts how the legal drama episodes are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The generating unit is When generating episodes for a legal drama, consider the geographical location of the person seeking advice to generate an appropriate episode. The system described in Appendix 2, characterized by the features described herein. (Note 28) The generating unit is When generating episodes for a legal drama, we analyze the social media activity of clients to suggest episode content. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of the person seeking advice, An emotion estimation unit estimates the emotions of the person seeking advice based on the emotion estimation information acquired by the acquisition unit, The system comprises: a provisioning unit that provides legal advice based on the emotions estimated by the emotion estimation unit; A system characterized by the following features.

2. The system includes a generating unit that creates legal drama episodes tailored to the client's case, based on the client's emotions and their specific circumstances. The system according to feature 1.

3. The aforementioned supply unit is, The generation unit provides a legal drama of episodes generated by the aforementioned generation unit. The system according to feature 2.

4. The aforementioned supply unit is, The legal drama episodes generated by the aforementioned generation unit are provided in VR. The system according to feature 2.

5. The acquisition unit is, The system estimates the emotions of the person seeking advice and adjusts the timing of acquiring emotion estimation information based on the estimated emotions of the person seeking advice. The system according to feature 1.

6. The acquisition unit is, Analyze the past consultation history of the aforementioned client and select an appropriate method for obtaining the information. The system according to feature 1.

7. The acquisition unit is, When acquiring information for emotion estimation, filtering is performed based on the client's current living situation and areas of interest. The system according to feature 1.

8. The acquisition unit is, The system estimates the emotions of the person seeking advice and determines the priority of emotion estimation information to be obtained based on the estimated emotions of the person seeking advice. The system according to feature 1.

9. The acquisition unit is, When acquiring information for emotion estimation, the system prioritizes acquiring highly relevant information by considering the geographical location of the person seeking advice. The system according to feature 1.

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