system

Abstract

The system according to this embodiment aims to improve fairness in court judgments. [Solution] The system according to the embodiment comprises a collection unit, a storage unit, a learning unit, a proposal unit, and a provision unit. The collection unit collects the details of court cases. The storage unit encrypts the details of court cases collected by the collection unit and stores them on a blockchain. The learning unit learns about court cases from various countries, regions, and eras. The proposal unit proposes judgments based on the information learned by the learning unit. The provision unit provides the judgments proposed by the proposal unit to human judges.

Description

Technical Field

[0003]

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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, there is a risk that human emotions and prejudices may affect the judgment of a trial, and there may be a lack of fairness.

[0005] The system according to the embodiment aims to improve fairness in the judgment of a trial.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a storage unit, a learning unit, a proposal unit, and a provision unit. The collection unit collects details of court cases. The storage unit encrypts the details of court cases collected by the collection unit and stores them on a blockchain. The learning unit learns about court cases from various countries, regions, and eras. The proposal unit proposes judgments based on the information learned by the learning unit. The provision unit provides the judgments proposed by the proposal unit to human judges. [Effects of the Invention]

[0007] The system according to this embodiment can improve fairness in court judgments. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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). [[ID=第十九]]

[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 court support system according to an embodiment of the present invention is a system in which publicly available court details are encrypted and stored on a blockchain, and a generating AI judge, which has learned from court precedents from different countries, regions, and eras, proposes an appropriate judgment based on that information. This court support system collects court details, encrypts and stores them, and a generating AI judge, which has learned from court precedents from different countries, regions, and eras, proposes an appropriate judgment, thereby providing an impartial legal opinion. For example, court details (date and time of the trial, location, information of those involved, evidence, judgment content, etc.) are encrypted and stored on a blockchain. In this process, data such as the date and time of the trial, location, information of those involved, evidence, and judgment content are collected, encrypted, and stored on a blockchain. This ensures that court details are stored without being tampered with. Next, the generating AI judge learns from court precedents from different countries, regions, and eras. The generating AI judge collects data from national law and case law databases and learns based on it. This allows the generating AI judge to propose a judgment from a broader perspective. The judgment proposed by the generating AI judge is provided to the human judge in real time during the trial. This allows the human judge to make an impartial judgment while referring to the proposal of the generating AI judge. The judgments proposed by the generating AI judges are based on past precedents and laws, resulting in impartial legal opinions that transcend the boundaries of a single country or culture. This mechanism is expected to increase the transparency and fairness of trials, and to ensure that judgments are not influenced by human emotions or biases. For example, judgments proposed by generating AI judges that have learned from precedents in different countries, regions, and eras will not be biased towards any particular culture or values, resulting in more impartial legal opinions. In this way, the trial support system can enhance the transparency and fairness of trials and provide judgments that are not influenced by human emotions or biases.

[0029] The court support system according to this embodiment comprises a collection unit, a storage unit, a learning unit, a proposal unit, and a provision unit. The collection unit collects court details. Court details include, but are not limited to, the date and time of the trial, the location, information of those involved, evidence, and the content of the judgment. For example, the collection unit collects the date and time of the trial in YYYY-MM-DD format. The collection unit can also collect the location of the trial by address or GPS coordinates. Furthermore, the collection unit can also collect information of those involved by name, title, contact information, etc. For example, the collection unit collects evidence in the form of documents, photographs, videos, etc. The content of the judgment can be collected in the form of the judgment text and the reasoning of the judgment, etc. The storage unit encrypts the court details collected by the collection unit and stores them on a blockchain. For example, algorithms such as AES and RSA are used for encryption, but are not limited to such examples. For example, the storage unit encrypts the data using AES and stores it on a blockchain. The storage unit can also encrypt the data using RSA. Public blockchains and private blockchains are used as the blockchain. For example, the storage unit stores data using a public blockchain. The storage unit can also store data using a private blockchain. The learning unit uses generative AI to collect data from databases of laws and precedents in various countries and learns from it. Learning may use, but is not limited to, algorithms such as machine learning or deep learning. The learning unit may learn from data using, for example, machine learning. The learning unit can also learn from data using deep learning. The proposal unit proposes judgments based on the information learned by the learning unit. Proposals may include, but are not limited to, proposals based on past precedents or proposals based on legal interpretations. The proposal unit may propose judgments based on, for example, past precedents. The proposal unit can also propose judgments based on legal interpretations. The delivery unit provides the judgments proposed by the proposal unit to human judges in real time during the trial. Delivery may, but is not limited to, email or a dedicated application. The delivery unit may, for example, provide the judgments via email.Furthermore, the service provider can also deliver judgments using a dedicated application. This allows the court support system according to the embodiment to enhance the transparency and fairness of trials and provide judgments that are not influenced by human emotions or biases.

[0030] The data collection unit can collect data including the date and time of the trial, the location, information of those involved, evidence, and the content of the judgment. For example, the data collection unit can collect the date and time of the trial in YYYY-MM-DD format. For example, the data collection unit can collect the date and time of the trial as 2023-10-01. The data collection unit can also collect the location of the trial by address or GPS coordinates. For example, the data collection unit can collect the location of the trial as 123 Main St, City, Country or by GPS coordinates. Furthermore, the data collection unit can collect information of those involved by name, title, and contact information. For example, the data collection unit can collect the name of a person involved as John Doe, the title as Judge, and the contact information as john.doe@example.com. The data collection unit can also collect evidence in the form of documents, photographs, and videos. For example, the data collection unit can collect contracts, photographs of accident scenes, and surveillance camera videos as evidence. The content of the judgment can be collected in the form of the judgment text and the reasoning behind the judgment. For example, the data collection unit can collect the judgment text in text format and record the reasoning behind the judgment in detail. This allows for the collection of detailed court data, enabling the proposal of judgments based on accurate information.

[0031] The storage unit can encrypt the collected data and store it on the blockchain. For example, the storage unit can encrypt the data using AES and store it on the blockchain. For example, the storage unit can encrypt the data using the AES-256 algorithm and store it on the blockchain. The storage unit can also encrypt the data using RSA. For example, the storage unit can encrypt the data using the RSA-2048 algorithm and store it on the blockchain. Public and private blockchains can be used for the blockchain. For example, the storage unit can store data using a public blockchain. The storage unit can also store data using a private blockchain. By encrypting the data and storing it on the blockchain, it is securely stored without being tampered with.

[0032] The learning unit can collect data from national law and case law databases and perform learning based on that data. For example, the learning unit can learn data using machine learning. For instance, it can use machine learning algorithms to learn data collected from national law and case law databases. Furthermore, the learning unit can also learn data using deep learning. For example, it can use deep learning algorithms to learn data collected from national law and case law databases. National law and case law databases include both specific national law databases and international case law databases. For example, the learning unit can collect data from a specific national law database and perform learning based on that data. Furthermore, the learning unit can collect data from international case law databases and perform learning based on that data. This allows for judgment proposals from a broad perspective by learning from national law and case law databases.

[0033] The proposing unit can propose judgments based on learned information. For example, the proposing unit can propose judgments based on past precedents. For instance, it can propose judgments for similar cases based on past precedent data. Furthermore, the proposing unit can also propose judgments based on legal interpretations. For example, it can propose appropriate judgments based on legal provisions and interpretations of precedents. Proposed judgments include, but are not limited to, those based on past precedents and those based on legal interpretations. This allows for the provision of impartial legal opinions by proposing judgments based on learned information.

[0034] The service provider can provide proposed judgments to human judges in real time during the trial. The service provider can provide judgments, for example, via email. For example, the service provider can send proposed judgments to human judges via email. Alternatively, the service provider can provide judgments using a dedicated application. For example, the service provider can provide proposed judgments to human judges in real time through a dedicated application. Specific definitions and standards of "real time" include seconds and minutes. For example, the service provider can provide judgments in seconds. It can also provide judgments in minutes. This real-time provision of judgments allows human judges to make fair judgments with reference.

[0035] The data collection unit can analyze past court data and select the most efficient collection method. For example, the data collection unit can analyze past court data and select the most efficient collection method. For example, based on past court data, the data collection unit can identify data that takes a long time to collect and prioritize its collection. Furthermore, based on past court data, the data collection unit can optimize its collection methods to collect data efficiently. This allows for the selection of the most efficient collection method by analyzing past data.

[0036] The data collection department can filter data when collecting court details, taking into account the background information and social impact of those involved. For example, the data collection department can prioritize the collection of important data by considering the background information of those involved. For instance, the data collection department can prioritize the collection of important data by considering the occupational history, educational background, and past court records of those involved. The data collection department can also filter and collect highly relevant data by considering the social impact. For example, the data collection department can filter and collect highly relevant data based on media reports and public opinion polls. Furthermore, the data collection department can collect the most appropriate data by comprehensively considering the background information and social impact of those involved. For instance, the data collection department can collect the most appropriate data by comprehensively considering the background information and social impact of those involved. This allows for the priority collection of important data by considering the background information and social impact of those involved.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting details of a trial. For example, the data collection unit can prioritize the collection of highly relevant data based on geographical location information. For instance, the data collection unit can collect trial locations by address or GPS coordinates and prioritize the collection of highly relevant data. The data collection unit can also efficiently collect data by limiting the collection range while considering geographical location information. For example, the data collection unit can prioritize the collection of data from a specific region. Furthermore, the data collection unit can prioritize the collection of important data based on geographical location information. For example, the data collection unit can prioritize the collection of data from the region where a specific incident occurred. In this way, highly relevant data can be collected efficiently by considering geographical location information.

[0038] The data collection unit can analyze social media activity and collect relevant data when gathering details of a trial. For example, the data collection unit can analyze social media activity and prioritize the collection of relevant data. For example, the data collection unit can analyze social media posts, comments, and the number of likes, and prioritize the collection of relevant data. The data collection unit can also filter and collect important data based on social media activity. For example, the data collection unit can analyze social media posts and filter and collect important data. Furthermore, the data collection unit can make a comprehensive judgment on social media activity and collect the most appropriate data. For example, the data collection unit can make a comprehensive judgment on social media posts, comments, and the number of likes, and collect the most appropriate data. This allows for the efficient collection of relevant data by analyzing social media activity.

[0039] The storage unit can adjust the encryption strength based on the importance of the data during storage. For example, the storage unit can apply a strong encryption method to highly important data. For instance, it can apply the AES-256 algorithm to highly confidential data. Conversely, the storage unit can apply a simpler encryption method to less important data. For example, it can apply the AES-128 algorithm to general data. Furthermore, the storage unit can dynamically adjust the encryption strength according to the importance of the data. For example, it can dynamically adjust the encryption strength from AES-128 to AES-256 depending on the importance of the data. This allows for efficient data storage by adjusting the encryption strength according to the importance of the data.

[0040] The storage unit can apply different encryption algorithms depending on the data category during storage. For example, the storage unit can apply a specific encryption algorithm to evidentiary materials. For instance, the storage unit can apply the AES-256 algorithm to evidentiary materials. Furthermore, the storage unit can apply a different encryption algorithm to judgment content. For example, the storage unit can apply the RSA-2048 algorithm to judgment content. In addition, the storage unit can select the optimal encryption algorithm depending on the data category. For example, the storage unit can select encryption algorithms such as AES or RSA depending on the data category. This allows for efficient data storage by applying the optimal encryption algorithm according to the data category.

[0041] The storage unit can determine the priority of data storage based on the submission date. For example, the storage unit can prioritize saving data that was submitted earlier. The storage unit can also postpone saving data that was submitted later. Furthermore, the storage unit can dynamically adjust the storage priority based on the submission date. This allows for efficient data storage by determining the storage priority based on the data submission date.

[0042] The storage unit can adjust the order of saving data based on its relevance. For example, it can prioritize saving highly relevant data. It can also postpone saving less relevant data. Furthermore, the storage unit can dynamically adjust the order of saving data based on its relevance. This allows for efficient data storage by adjusting the order of saving based on data relevance.

[0043] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm by referring to past learning data. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also dynamically adjust the learning algorithm based on past learning data. For example, the learning unit can dynamically adjust the learning algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data and apply the most efficient learning algorithm. For example, the learning unit can analyze past learning data and apply the most efficient learning algorithm. This allows for efficient optimization of the learning algorithm by referring to past learning data.

[0044] The learning system can integrate legal and case law databases from different countries and regions during the learning process. For example, the learning system can integrate legal databases from various countries. Furthermore, the learning system can integrate case law databases from different regions. In addition, the learning system can comprehensively integrate legal and case law databases from different countries and regions for learning. This allows for learning from a broader perspective by integrating legal and case law databases from different countries and regions.

[0045] The learning unit can weight the training data based on the submission date of the court data during training. For example, the learning unit can assign higher weight to data submitted earlier. The learning unit can also assign lower weight to data submitted later. Furthermore, the learning unit can dynamically adjust the weighting of the training data based on the submission date. This allows for efficient training by weighting the training data based on the submission date of the court data.

[0046] The learning unit can improve the accuracy of its learning by referring to relevant legal documents during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to relevant legal documents. Furthermore, the learning unit can optimize its learning algorithm based on legal documents. For example, the learning unit can optimize its learning algorithm based on legal documents. In addition, the learning unit can improve the accuracy of its learning by comprehensively referring to relevant legal documents. For example, the learning unit can improve the accuracy of its learning by comprehensively referring to relevant legal documents. This allows for improved learning accuracy by referring to relevant legal documents.

[0047] The proposal department can adjust the level of detail in its proposals based on the importance of the trial. For example, it can provide detailed proposals for highly important trials. It can also provide concise proposals for less important trials. Furthermore, the proposal department can dynamically adjust the level of detail in its proposals according to the importance of the trial. This allows for efficient proposal of judgments by adjusting the level of detail in proposals according to the importance of the trial.

[0048] The proposal department can apply different proposal algorithms depending on the category of the trial when making a proposal. For example, the proposal department can apply a specific proposal algorithm to criminal trials. For example, the proposal department can apply a specific proposal algorithm to criminal trials. The proposal department can also apply a different proposal algorithm to civil trials. For example, the proposal department can apply a different proposal algorithm to civil trials. Furthermore, the proposal department can select the optimal proposal algorithm depending on the category of the trial. For example, the proposal department can select the optimal proposal algorithm depending on the category of the trial. This allows for efficient proposal of judgments by applying the optimal proposal algorithm according to the category of the trial.

[0049] The proposal department can determine the priority of proposals based on the filing date of the cases. For example, the proposal department can prioritize proposals for cases that are filed earlier. The proposal department can also postpone proposals for cases that are filed later. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the filing date. This allows for efficient proposal of judgments by determining the priority of proposals based on the filing date of the cases.

[0050] The proposal department can adjust the order of proposals based on the relevance of the cases when making a proposal. For example, the proposal department can prioritize proposals for highly relevant cases. The proposal department can also postpone proposals for less relevant cases. Furthermore, the proposal department can dynamically adjust the order of proposals based on the relevance of the cases. This allows for efficient proposal of judgments by adjusting the order of proposals based on the relevance of the cases.

[0051] The service provider can monitor the progress of the trial in real time and provide the judgment at the optimal timing. For example, the service provider can monitor the progress of the trial in real time and provide the judgment at the optimal timing. For example, the service provider can monitor the progress of the trial in real time and provide the judgment at the optimal timing. Furthermore, the service provider can dynamically adjust the timing of judgment delivery according to the progress of the trial. For example, the service provider can dynamically adjust the timing of judgment delivery according to the progress of the trial. In addition, the service provider can provide the judgment at the optimal timing based on the progress of the trial. For example, the service provider can provide the judgment at the optimal timing based on the progress of the trial. This allows for the provision of the judgment at the optimal timing by monitoring the progress of the trial in real time.

[0052] The service provider can select the optimal service provider method by referring to the judge's past judgment history at the time of service provision. For example, the service provider can select the optimal service provider method by referring to the judge's past judgment history. Furthermore, the service provider can dynamically adjust the service provider method based on the judge's past judgment history. For example, the service provider can dynamically adjust the service provider method based on the judge's past judgment history. In addition, the service provider can analyze the judge's past judgment history and select the most efficient service provider method. This allows the service provider to select the optimal service provider method by referring to the judge's past judgment history.

[0053] The service provider can determine the priority of service provision based on the progress of the trial at the time of provision. For example, the service provider can determine the priority of service provision based on the progress of the trial. For example, the service provider can determine the priority of service provision based on the progress of the trial. The service provider can also dynamically adjust the priority of service provision based on the progress of the trial. For example, the service provider can dynamically adjust the priority of service provision based on the progress of the trial. Furthermore, the service provider can monitor the progress of the trial in real time and determine the priority of service provision. For example, the service provider can monitor the progress of the trial in real time and determine the priority of service provision. This allows for efficient service provision of judgments by determining the priority of service provision based on the progress of the trial.

[0054] The providing department can adjust the order of provision based on the relevance of the cases at the time of provision. For example, the providing department can prioritize provision of highly relevant cases. The providing department can also postpone provision of less relevant cases. For example, the providing department can postpone provision of less relevant cases. Furthermore, the providing department can dynamically adjust the order of provision based on the relevance of the cases. For example, the providing department can dynamically adjust the order of provision based on the relevance of the cases. This allows for efficient provision of judgments by adjusting the order of provision based on the relevance of the cases.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can filter data when collecting court details, taking into account the social background of those involved. For example, it can prioritize the collection of important data by considering the occupation and social status of those involved. It can also appropriately process culturally sensitive information by considering the cultural background of those involved. Furthermore, it can prioritize the collection of highly relevant data by considering the past behavioral history of those involved. This allows for the efficient collection of important data by considering the social background of those involved.

[0057] The storage unit can determine storage priorities based on data importance when encrypting and storing collected data. For example, the storage unit can prioritize the encryption and storage of highly important data. It can also postpone the storage of less important data. Furthermore, the storage unit can dynamically adjust the encryption strength according to the importance of the data. This allows for efficient data storage by determining storage priorities based on data importance.

[0058] The learning unit can collect data from national legal and case law databases and evaluate the reliability of that data when performing training. For example, the learning unit can evaluate the source and update frequency of the data and prioritize training on highly reliable data. It can also evaluate the consistency and accuracy of the data and exclude unreliable data. Furthermore, the learning unit can dynamically adjust the training algorithm based on the reliability of the data. This allows for more accurate training by evaluating the reliability of the data.

[0059] The proposal unit can monitor the progress of the trial in real time and make proposals at the optimal time when proposing judgments based on learned information. For example, the proposal unit can monitor the progress of the trial in real time and propose a judgment at the appropriate time. Furthermore, the proposal unit can dynamically adjust the content of its proposals according to the progress of the trial. In addition, the proposal unit can make proposals at the optimal time based on the progress of the trial. This allows for the proposal of a judgment at the optimal time by monitoring the progress of the trial in real time.

[0060] The delivery unit can select the optimal delivery method by referring to the judge's past judgment history when providing proposed judgments to human judges in real time during the trial. For example, the delivery unit can refer to the judge's past judgment history and select the optimal delivery method. Furthermore, the delivery unit can dynamically adjust the delivery method based on the judge's past judgment history. In addition, the delivery unit can analyze the judge's past judgment history and select the most efficient delivery method. This allows for the selection of the optimal delivery method by referring to the judge's past judgment history.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection team collects details of the trial. These details include the date and time of the trial, the location, information about those involved, evidence, and the content of the judgment. For example, the date and time of the trial are collected in YYYY-MM-DD format, and the location is collected as an address or GPS coordinate. Information about those involved is collected as names, titles, and contact information, and evidence is collected as documents, photographs, videos, etc. The content of the judgment is collected as the judgment text and the reasoning behind the judgment. Step 2: The storage unit encrypts the trial details collected by the collection unit and stores them on the blockchain. Algorithms such as AES or RSA are used for encryption, and either a public or private blockchain is used. For example, data can be encrypted using AES and stored on a public blockchain. Step 3: The learning unit uses generative AI to collect data from databases of laws and precedents in various countries and uses this data for learning. Algorithms such as machine learning and deep learning are used for learning. For example, it is possible to learn data using machine learning and to learn data using deep learning. Step 4: The proposing unit proposes a judgment based on the information learned by the learning unit. Proposed judgments may include those based on past precedents or those based on legal interpretations. For example, a judgment may be proposed based on past precedents, or a judgment may be proposed based on legal interpretations. Step 5: The providing department provides the judgment proposed by the proposing department to a human judge in real time during the trial. This provision can be done via email or a dedicated application. For example, the judgment can be provided via email or a dedicated application.

[0063] (Example of form 2) The court support system according to an embodiment of the present invention is a system in which publicly available court details are encrypted and stored on a blockchain, and a generating AI judge, which has learned from court precedents from different countries, regions, and eras, proposes an appropriate judgment based on that information. This court support system collects court details, encrypts and stores them, and a generating AI judge, which has learned from court precedents from different countries, regions, and eras, proposes an appropriate judgment, thereby providing an impartial legal opinion. For example, court details (date and time of the trial, location, information of those involved, evidence, judgment content, etc.) are encrypted and stored on a blockchain. In this process, data such as the date and time of the trial, location, information of those involved, evidence, and judgment content are collected, encrypted, and stored on a blockchain. This ensures that court details are stored without being tampered with. Next, the generating AI judge learns from court precedents from different countries, regions, and eras. The generating AI judge collects data from national law and case law databases and learns based on it. This allows the generating AI judge to propose a judgment from a broader perspective. The judgment proposed by the generating AI judge is provided to the human judge in real time during the trial. This allows the human judge to make an impartial judgment while referring to the proposal of the generating AI judge. The judgments proposed by the generating AI judges are based on past precedents and laws, resulting in impartial legal opinions that transcend the boundaries of a single country or culture. This mechanism is expected to increase the transparency and fairness of trials, and to ensure that judgments are not influenced by human emotions or biases. For example, judgments proposed by generating AI judges that have learned from precedents in different countries, regions, and eras will not be biased towards any particular culture or values, resulting in more impartial legal opinions. In this way, the trial support system can enhance the transparency and fairness of trials and provide judgments that are not influenced by human emotions or biases.

[0064] The court support system according to this embodiment comprises a collection unit, a storage unit, a learning unit, a proposal unit, and a provision unit. The collection unit collects court details. Court details include, but are not limited to, the date and time of the trial, the location, information of those involved, evidence, and the content of the judgment. For example, the collection unit collects the date and time of the trial in YYYY-MM-DD format. The collection unit can also collect the location of the trial by address or GPS coordinates. Furthermore, the collection unit can also collect information of those involved by name, title, contact information, etc. For example, the collection unit collects evidence in the form of documents, photographs, videos, etc. The content of the judgment can be collected in the form of the judgment text and the reasoning of the judgment, etc. The storage unit encrypts the court details collected by the collection unit and stores them on a blockchain. For example, algorithms such as AES and RSA are used for encryption, but are not limited to such examples. For example, the storage unit encrypts the data using AES and stores it on a blockchain. The storage unit can also encrypt the data using RSA. Public blockchains and private blockchains are used as the blockchain. For example, the storage unit stores data using a public blockchain. The storage unit can also store data using a private blockchain. The learning unit uses generative AI to collect data from databases of laws and precedents in various countries and learns from it. Learning may use, but is not limited to, algorithms such as machine learning or deep learning. The learning unit may learn from data using, for example, machine learning. The learning unit can also learn from data using deep learning. The proposal unit proposes judgments based on the information learned by the learning unit. Proposals may include, but are not limited to, proposals based on past precedents or proposals based on legal interpretations. The proposal unit may propose judgments based on, for example, past precedents. The proposal unit can also propose judgments based on legal interpretations. The delivery unit provides the judgments proposed by the proposal unit to human judges in real time during the trial. Delivery may, but is not limited to, email or a dedicated application. The delivery unit may, for example, provide the judgments via email.Furthermore, the service provider can also deliver judgments using a dedicated application. This allows the court support system according to the embodiment to enhance the transparency and fairness of trials and provide judgments that are not influenced by human emotions or biases.

[0065] The data collection unit can collect data including the date and time of the trial, the location, information of those involved, evidence, and the content of the judgment. For example, the data collection unit can collect the date and time of the trial in YYYY-MM-DD format. For example, the data collection unit can collect the date and time of the trial as 2023-10-01. The data collection unit can also collect the location of the trial by address or GPS coordinates. For example, the data collection unit can collect the location of the trial as 123 Main St, City, Country or by GPS coordinates. Furthermore, the data collection unit can collect information of those involved by name, title, and contact information. For example, the data collection unit can collect the name of a person involved as John Doe, the title as Judge, and the contact information as john.doe@example.com. The data collection unit can also collect evidence in the form of documents, photographs, and videos. For example, the data collection unit can collect contracts, photographs of accident scenes, and surveillance camera videos as evidence. The content of the judgment can be collected in the form of the judgment text and the reasoning behind the judgment. For example, the data collection unit can collect the judgment text in text format and record the reasoning behind the judgment in detail. This allows for the collection of detailed court data, enabling the proposal of judgments based on accurate information.

[0066] The storage unit can encrypt the collected data and store it on the blockchain. For example, the storage unit can encrypt the data using AES and store it on the blockchain. For example, the storage unit can encrypt the data using the AES-256 algorithm and store it on the blockchain. The storage unit can also encrypt the data using RSA. For example, the storage unit can encrypt the data using the RSA-2048 algorithm and store it on the blockchain. Public and private blockchains can be used for the blockchain. For example, the storage unit can store data using a public blockchain. The storage unit can also store data using a private blockchain. By encrypting the data and storing it on the blockchain, it is securely stored without being tampered with.

[0067] The learning unit can collect data from national law and case law databases and perform learning based on that data. For example, the learning unit can learn data using machine learning. For instance, it can use machine learning algorithms to learn data collected from national law and case law databases. Furthermore, the learning unit can also learn data using deep learning. For example, it can use deep learning algorithms to learn data collected from national law and case law databases. National law and case law databases include both specific national law databases and international case law databases. For example, the learning unit can collect data from a specific national law database and perform learning based on that data. Furthermore, the learning unit can collect data from international case law databases and perform learning based on that data. This allows for judgment proposals from a broad perspective by learning from national law and case law databases.

[0068] The proposing unit can propose judgments based on learned information. For example, the proposing unit can propose judgments based on past precedents. For instance, it can propose judgments for similar cases based on past precedent data. Furthermore, the proposing unit can also propose judgments based on legal interpretations. For example, it can propose appropriate judgments based on legal provisions and interpretations of precedents. Proposed judgments include, but are not limited to, those based on past precedents and those based on legal interpretations. This allows for the provision of impartial legal opinions by proposing judgments based on learned information.

[0069] The service provider can provide proposed judgments to human judges in real time during the trial. The service provider can provide judgments, for example, via email. For example, the service provider can send proposed judgments to human judges via email. Alternatively, the service provider can provide judgments using a dedicated application. For example, the service provider can provide proposed judgments to human judges in real time through a dedicated application. Specific definitions and standards of "real time" include seconds and minutes. For example, the service provider can provide judgments in seconds. It can also provide judgments in minutes. This real-time provision of judgments allows human judges to make fair judgments with reference.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of collecting trial details based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the data when the user is relaxed. For example, the data collection unit can recognize the user's facial expressions and, if it determines that the user is stressed, delay the collection timing. Also, if the user is relaxed, the data collection unit can immediately collect trial details to efficiently acquire data. For example, the data collection unit can analyze the user's voice and, if it determines that the user is relaxed, immediately collect the data. Furthermore, if the user is in a hurry, the data collection unit can speed up the collection timing to quickly acquire trial details. For example, the data collection unit can analyze the user's behavior patterns and, if it determines that the user is in a hurry, speed up the collection timing. In this way, data can be efficiently collected by adjusting the collection timing according to the user's emotions.

[0071] The data collection unit can analyze past court data and select the most efficient collection method. For example, the data collection unit can analyze past court data and select the most efficient collection method. For example, based on past court data, the data collection unit can identify data that takes a long time to collect and prioritize its collection. Furthermore, based on past court data, the data collection unit can optimize its collection methods to collect data efficiently. This allows for the selection of the most efficient collection method by analyzing past data.

[0072] The data collection department can filter data when collecting court details, taking into account the background information and social impact of those involved. For example, the data collection department can prioritize the collection of important data by considering the background information of those involved. For instance, the data collection department can prioritize the collection of important data by considering the occupational history, educational background, and past court records of those involved. The data collection department can also filter and collect highly relevant data by considering the social impact. For example, the data collection department can filter and collect highly relevant data based on media reports and public opinion polls. Furthermore, the data collection department can collect the most appropriate data by comprehensively considering the background information and social impact of those involved. For instance, the data collection department can collect the most appropriate data by comprehensively considering the background information and social impact of those involved. This allows for the priority collection of important data by considering the background information and social impact of those involved.

[0073] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit can prioritize collecting high-priority data and postpone collecting low-priority data. For instance, by recognizing the user's facial expressions and determining that they are stressed, the unit can postpone collecting low-priority data. Furthermore, if the user is relaxed, the unit can collect all data equally. For example, by analyzing the user's voice and determining that they are relaxed, the unit can collect all data equally. Additionally, if the user is in a hurry, the unit can prioritize collecting the most important data. For example, by analyzing the user's behavior patterns and determining that they are in a hurry, the unit can prioritize collecting the most important data. This allows for efficient collection of important data by prioritizing data according to the user's emotions.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information when collecting details of a trial. For example, the data collection unit can prioritize the collection of highly relevant data based on geographical location information. For instance, the data collection unit can collect trial locations by address or GPS coordinates and prioritize the collection of highly relevant data. The data collection unit can also efficiently collect data by limiting the collection range while considering geographical location information. For example, the data collection unit can prioritize the collection of data from a specific region. Furthermore, the data collection unit can prioritize the collection of important data based on geographical location information. For example, the data collection unit can prioritize the collection of data from the region where a specific incident occurred. In this way, highly relevant data can be collected efficiently by considering geographical location information.

[0075] The data collection unit can analyze social media activity and collect relevant data when gathering details of a trial. For example, the data collection unit can analyze social media activity and prioritize the collection of relevant data. For example, the data collection unit can analyze social media posts, comments, and the number of likes, and prioritize the collection of relevant data. The data collection unit can also filter and collect important data based on social media activity. For example, the data collection unit can analyze social media posts and filter and collect important data. Furthermore, the data collection unit can make a comprehensive judgment on social media activity and collect the most appropriate data. For example, the data collection unit can make a comprehensive judgment on social media posts, comments, and the number of likes, and collect the most appropriate data. This allows for the efficient collection of relevant data by analyzing social media activity.

[0076] The storage unit can estimate the user's emotions and adjust the data encryption method based on the estimated emotions. For example, if the user is stressed, the storage unit can use a simple encryption method and quickly save the data. For instance, the storage unit can recognize the user's facial expressions and, if it determines that the user is stressed, use a simple encryption method. Furthermore, if the user is relaxed, the storage unit can use a strong encryption method to ensure data security. For example, the storage unit can analyze the user's voice and, if it determines that the user is relaxed, use a strong encryption method. Additionally, if the user is in a hurry, the storage unit can quickly encrypt and save the data. For example, the storage unit can analyze the user's behavior patterns and, if it determines that the user is in a hurry, quickly encrypt the data. This allows for efficient data storage by adjusting the encryption method according to the user's emotions.

[0077] The storage unit can adjust the encryption strength based on the importance of the data during storage. For example, the storage unit can apply a strong encryption method to highly important data. For instance, it can apply the AES-256 algorithm to highly confidential data. Conversely, the storage unit can apply a simpler encryption method to less important data. For example, it can apply the AES-128 algorithm to general data. Furthermore, the storage unit can dynamically adjust the encryption strength according to the importance of the data. For example, it can dynamically adjust the encryption strength from AES-128 to AES-256 depending on the importance of the data. This allows for efficient data storage by adjusting the encryption strength according to the importance of the data.

[0078] The storage unit can apply different encryption algorithms depending on the data category during storage. For example, the storage unit can apply a specific encryption algorithm to evidentiary materials. For instance, the storage unit can apply the AES-256 algorithm to evidentiary materials. Furthermore, the storage unit can apply a different encryption algorithm to judgment content. For example, the storage unit can apply the RSA-2048 algorithm to judgment content. In addition, the storage unit can select the optimal encryption algorithm depending on the data category. For example, the storage unit can select encryption algorithms such as AES or RSA depending on the data category. This allows for efficient data storage by applying the optimal encryption algorithm according to the data category.

[0079] The data storage unit can estimate the user's emotions and adjust the data storage order based on the estimated emotions. For example, if the user is stressed, the storage unit will prioritize saving high-priority data. For instance, by recognizing the user's facial expressions and determining that they are stressed, it can prioritize saving high-priority data. Furthermore, if the user is relaxed, the storage unit can save all data evenly. For example, by analyzing the user's voice and determining that they are relaxed, it can save all data evenly. Additionally, if the user is in a hurry, the storage unit can prioritize saving the most important data. For example, by analyzing the user's behavior patterns and determining that they are in a hurry, it can prioritize saving the most important data. This allows for efficient data storage by adjusting the storage order according to the user's emotions.

[0080] The storage unit can determine the priority of data storage based on the submission date. For example, the storage unit can prioritize saving data that was submitted earlier. The storage unit can also postpone saving data that was submitted later. Furthermore, the storage unit can dynamically adjust the storage priority based on the submission date. This allows for efficient data storage by determining the storage priority based on the data submission date.

[0081] The storage unit can adjust the order of saving data based on its relevance. For example, it can prioritize saving highly relevant data. It can also postpone saving less relevant data. Furthermore, the storage unit can dynamically adjust the order of saving data based on its relevance. This allows for efficient data storage by adjusting the order of saving based on data relevance.

[0082] The learning unit can estimate the user's emotions and select training data based on those emotions. For example, if the user is stressed, the learning unit can select simple training data. For instance, by recognizing the user's facial expressions and determining that they are stressed, it can select simple training data. The learning unit can also select detailed training data if the user is relaxed. For example, by analyzing the user's voice and determining that they are relaxed, it can select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select data that allows for rapid learning. For example, by analyzing the user's behavior patterns and determining that they are in a hurry, it can select data that allows for rapid learning. This allows for efficient learning by selecting training data according to the user's emotions.

[0083] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm by referring to past learning data. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also dynamically adjust the learning algorithm based on past learning data. For example, the learning unit can dynamically adjust the learning algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data and apply the most efficient learning algorithm. For example, the learning unit can analyze past learning data and apply the most efficient learning algorithm. This allows for efficient optimization of the learning algorithm by referring to past learning data.

[0084] The learning system can integrate legal and case law databases from different countries and regions during the learning process. For example, the learning system can integrate legal databases from various countries. Furthermore, the learning system can integrate case law databases from different regions. In addition, the learning system can comprehensively integrate legal and case law databases from different countries and regions for learning. This allows for learning from a broader perspective by integrating legal and case law databases from different countries and regions.

[0085] The learning unit can estimate the user's emotions and adjust the learning frequency based on those emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency. For instance, it can recognize the user's facial expressions and, if it determines the user is stressed, reduce the learning frequency. The learning unit can also increase the learning frequency if the user is relaxed. For example, it can analyze the user's voice and, if it determines the user is relaxed, increase the learning frequency. Furthermore, the learning unit can dynamically adjust the learning frequency if the user is in a hurry. For example, it can analyze the user's behavior patterns and, if it determines the user is in a hurry, dynamically adjust the learning frequency. This allows for efficient learning by adjusting the learning frequency according to the user's emotions.

[0086] The learning unit can weight the training data based on the submission date of the court data during training. For example, the learning unit can assign higher weight to data submitted earlier. The learning unit can also assign lower weight to data submitted later. Furthermore, the learning unit can dynamically adjust the weighting of the training data based on the submission date. This allows for efficient training by weighting the training data based on the submission date of the court data.

[0087] The learning unit can improve the accuracy of its learning by referring to relevant legal documents during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to relevant legal documents. Furthermore, the learning unit can optimize its learning algorithm based on legal documents. For example, the learning unit can optimize its learning algorithm based on legal documents. In addition, the learning unit can improve the accuracy of its learning by comprehensively referring to relevant legal documents. For example, the learning unit can improve the accuracy of its learning by comprehensively referring to relevant legal documents. This allows for improved learning accuracy by referring to relevant legal documents.

[0088] The suggestion unit can estimate the user's emotions and adjust the method of suggesting a judgment based on those emotions. For example, if the user is stressed, the suggestion unit will use a concise and easy-to-understand method of suggestion. For instance, if the suggestion unit recognizes the user's facial expressions and determines that the user is stressed, it can use a concise and easy-to-understand method of suggestion. The suggestion unit can also use a detailed method of suggestion if the user is relaxed. For example, if the suggestion unit analyzes the user's voice and determines that the user is relaxed, it can use a detailed method of suggestion. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions quickly. For example, if the suggestion unit analyzes the user's behavior patterns and determines that the user is in a hurry, it can provide suggestions quickly. This allows for efficient judgment suggestions by adjusting the method of suggestion according to the user's emotions.

[0089] The proposal department can adjust the level of detail in its proposals based on the importance of the trial. For example, it can provide detailed proposals for highly important trials. It can also provide concise proposals for less important trials. Furthermore, the proposal department can dynamically adjust the level of detail in its proposals according to the importance of the trial. This allows for efficient proposal of judgments by adjusting the level of detail in proposals according to the importance of the trial.

[0090] The proposal department can apply different proposal algorithms depending on the category of the trial when making a proposal. For example, the proposal department can apply a specific proposal algorithm to criminal trials. For example, the proposal department can apply a specific proposal algorithm to criminal trials. The proposal department can also apply a different proposal algorithm to civil trials. For example, the proposal department can apply a different proposal algorithm to civil trials. Furthermore, the proposal department can select the optimal proposal algorithm depending on the category of the trial. For example, the proposal department can select the optimal proposal algorithm depending on the category of the trial. This allows for efficient proposal of judgments by applying the optimal proposal algorithm according to the category of the trial.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is stressed, the suggestion unit can provide a short, concise suggestion. For instance, it can recognize the user's facial expressions and, if it determines the user is stressed, provide a short, concise suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a detailed suggestion. For example, it can analyze the user's voice and, if it determines the user is relaxed, provide a detailed suggestion. Additionally, if the user is in a hurry, the suggestion unit can provide a quick suggestion. For example, it can analyze the user's behavior patterns and, if it determines the user is in a hurry, provide a quick suggestion. This allows for efficient suggestion of judgments by adjusting the length of the suggestion according to the user's emotions.

[0092] The proposal department can determine the priority of proposals based on the filing date of the cases. For example, the proposal department can prioritize proposals for cases that are filed earlier. The proposal department can also postpone proposals for cases that are filed later. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the filing date. This allows for efficient proposal of judgments by determining the priority of proposals based on the filing date of the cases.

[0093] The proposal department can adjust the order of proposals based on the relevance of the cases when making a proposal. For example, the proposal department can prioritize proposals for highly relevant cases. The proposal department can also postpone proposals for less relevant cases. Furthermore, the proposal department can dynamically adjust the order of proposals based on the relevance of the cases. This allows for efficient proposal of judgments by adjusting the order of proposals based on the relevance of the cases.

[0094] The service provider can estimate the user's emotions and adjust the method of delivering the judgment based on those emotions. For example, if the user is stressed, the service provider can deliver the judgment in a concise and easy-to-understand manner. For instance, if the service provider recognizes the user's facial expressions and determines that the user is stressed, it can deliver the judgment in a concise and easy-to-understand manner. The service provider can also deliver the judgment in a detailed manner if the user is relaxed. For example, if the service provider analyzes the user's voice and determines that the user is relaxed, it can deliver the judgment in a detailed manner. Furthermore, if the service provider is in a hurry, it can deliver the judgment quickly. For example, if the service provider analyzes the user's behavior patterns and determines that the user is in a hurry, it can deliver the judgment quickly. This allows for efficient judgment delivery by adjusting the delivery method according to the user's emotions.

[0095] The service provider can monitor the progress of the trial in real time and provide the judgment at the optimal timing. For example, the service provider can monitor the progress of the trial in real time and provide the judgment at the optimal timing. For example, the service provider can monitor the progress of the trial in real time and provide the judgment at the optimal timing. Furthermore, the service provider can dynamically adjust the timing of judgment delivery according to the progress of the trial. For example, the service provider can dynamically adjust the timing of judgment delivery according to the progress of the trial. In addition, the service provider can provide the judgment at the optimal timing based on the progress of the trial. For example, the service provider can provide the judgment at the optimal timing based on the progress of the trial. This allows for the provision of the judgment at the optimal timing by monitoring the progress of the trial in real time.

[0096] The service provider can select the optimal service provider method by referring to the judge's past judgment history at the time of service provision. For example, the service provider can select the optimal service provider method by referring to the judge's past judgment history. Furthermore, the service provider can dynamically adjust the service provider method based on the judge's past judgment history. For example, the service provider can dynamically adjust the service provider method based on the judge's past judgment history. In addition, the service provider can analyze the judge's past judgment history and select the most efficient service provider method. This allows the service provider to select the optimal service provider method by referring to the judge's past judgment history.

[0097] The system can estimate the user's emotions and adjust the order in which judgments are delivered based on that estimation. For example, if the user is stressed, the system will prioritize delivering high-importance judgments. For instance, if the system recognizes the user's facial expressions and determines that the user is stressed, it can prioritize delivering high-importance judgments. Furthermore, if the user is relaxed, the system can deliver all judgments equally. For example, if the system analyzes the user's voice and determines that the user is relaxed, it can deliver all judgments equally. Additionally, if the user is in a hurry, the system can prioritize delivering the most important judgments. For example, if the system analyzes the user's behavioral patterns and determines that the user is in a hurry, it can prioritize delivering the most important judgments. This allows for efficient judgment delivery by adjusting the order of delivery according to the user's emotions.

[0098] The service provider can determine the priority of service provision based on the progress of the trial at the time of provision. For example, the service provider can determine the priority of service provision based on the progress of the trial. For example, the service provider can determine the priority of service provision based on the progress of the trial. The service provider can also dynamically adjust the priority of service provision based on the progress of the trial. For example, the service provider can dynamically adjust the priority of service provision based on the progress of the trial. Furthermore, the service provider can monitor the progress of the trial in real time and determine the priority of service provision. For example, the service provider can monitor the progress of the trial in real time and determine the priority of service provision. This allows for efficient service provision of judgments by determining the priority of service provision based on the progress of the trial.

[0099] The providing department can adjust the order of provision based on the relevance of the cases at the time of provision. For example, the providing department can prioritize provision of highly relevant cases. The providing department can also postpone provision of less relevant cases. For example, the providing department can postpone provision of less relevant cases. Furthermore, the providing department can dynamically adjust the order of provision based on the relevance of the cases. For example, the providing department can dynamically adjust the order of provision based on the relevance of the cases. This allows for efficient provision of judgments by adjusting the order of provision based on the relevance of the cases. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, storage unit, learning unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects trial details using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 by the control unit 46A. The storage unit encrypts the collected data by the specific processing unit 290 of the data processing unit 12 and stores it on a blockchain. The learning unit collects data from national law and case law databases by the specific processing unit 290 of the data processing unit 12 and learns using machine learning or deep learning. The proposal unit proposes a judgment based on the information learned by the specific processing unit 290 of the data processing unit 12. The provision unit provides the judgment proposed by the control unit 46A of the smart device 14 to a human judge in real time during the trial. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, storage unit, learning unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects trial details using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 by the control unit 46A. The storage unit encrypts the collected data by the specific processing unit 290 of the data processing unit 12 and stores it on a blockchain. The learning unit collects data from national law and case law databases by the specific processing unit 290 of the data processing unit 12 and learns using machine learning or deep learning. The proposal unit proposes a judgment based on the information learned by the specific processing unit 290 of the data processing unit 12. The provision unit provides the judgment proposed by the control unit 46A of the smart glasses 214 to a human judge in real time during the trial. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, storage unit, learning unit, proposal unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects trial details using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 by the control unit 46A. The storage unit encrypts the collected data by the specific processing unit 290 of the data processing unit 12 and stores it on a blockchain. The learning unit collects data from national law and case law databases by the specific processing unit 290 of the data processing unit 12 and learns using machine learning or deep learning. The proposal unit proposes a judgment based on the information learned by the specific processing unit 290 of the data processing unit 12. The provision unit provides the judgment proposed by the control unit 46A of the headset terminal 314 to a human judge in real time during the trial. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, storage unit, learning unit, proposal unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects trial details using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 by the control unit 46A. The storage unit encrypts the collected data by the specific processing unit 290 of the data processing unit 12 and stores it on a blockchain. The learning unit collects data from national law and case law databases by the specific processing unit 290 of the data processing unit 12 and learns using machine learning or deep learning. The proposal unit proposes a judgment based on the information learned by the specific processing unit 290 of the data processing unit 12. The provision unit provides the judgment proposed by the control unit 46A of the robot 414 to a human judge in real time during the trial.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can estimate the psychological state of those involved when gathering details of a trial and adjust the collection method based on that estimate. For example, if a participant is tense, the unit can change the interview format to help them relax. Also, if a participant is uncooperative, the unit can use a psychological approach to gain their cooperation. Furthermore, if a participant is emotional, the unit can temporarily suspend the collection and resume it later. By adjusting the collection method according to the psychological state of those involved, more accurate data can be collected.

[0102] The data collection unit can filter data when collecting court details, taking into account the social background of those involved. For example, it can prioritize the collection of important data by considering the occupation and social status of those involved. It can also appropriately process culturally sensitive information by considering the cultural background of those involved. Furthermore, it can prioritize the collection of highly relevant data by considering the past behavioral history of those involved. This allows for the efficient collection of important data by considering the social background of those involved.

[0103] The storage unit can determine storage priorities based on data importance when encrypting and storing collected data. For example, the storage unit can prioritize the encryption and storage of highly important data. It can also postpone the storage of less important data. Furthermore, the storage unit can dynamically adjust the encryption strength according to the importance of the data. This allows for efficient data storage by determining storage priorities based on data importance.

[0104] The learning unit can collect data from national legal and case law databases and evaluate the reliability of that data when performing training. For example, the learning unit can evaluate the source and update frequency of the data and prioritize training on highly reliable data. It can also evaluate the consistency and accuracy of the data and exclude unreliable data. Furthermore, the learning unit can dynamically adjust the training algorithm based on the reliability of the data. This allows for more accurate training by evaluating the reliability of the data.

[0105] The proposal unit can monitor the progress of the trial in real time and make proposals at the optimal time when proposing judgments based on learned information. For example, the proposal unit can monitor the progress of the trial in real time and propose a judgment at the appropriate time. Furthermore, the proposal unit can dynamically adjust the content of its proposals according to the progress of the trial. In addition, the proposal unit can make proposals at the optimal time based on the progress of the trial. This allows for the proposal of a judgment at the optimal time by monitoring the progress of the trial in real time.

[0106] The delivery unit can select the optimal delivery method by referring to the judge's past judgment history when providing proposed judgments to human judges in real time during the trial. For example, the delivery unit can refer to the judge's past judgment history and select the optimal delivery method. Furthermore, the delivery unit can dynamically adjust the delivery method based on the judge's past judgment history. In addition, the delivery unit can analyze the judge's past judgment history and select the most efficient delivery method. This allows for the selection of the optimal delivery method by referring to the judge's past judgment history.

[0107] The data collection unit can estimate the user's emotions and prioritize the court data to collect based on those estimated emotions. For example, if the user is stressed, the unit can postpone collecting less important data and prioritize collecting more important data. If the user is relaxed, the unit can collect all data evenly. Furthermore, if the user is in a hurry, the unit can prioritize collecting the most important data. This allows for the efficient collection of important data by prioritizing data according to the user's emotions.

[0108] The storage unit can estimate the user's emotions and adjust the data encryption method based on those emotions. For example, if the user is stressed, the storage unit can use a simple encryption method and quickly save the data. If the user is relaxed, the storage unit can use a strong encryption method to ensure data security. Furthermore, if the user is in a hurry, the storage unit can quickly encrypt and save the data. This allows for efficient data storage by adjusting the encryption method according to the user's emotions.

[0109] The learning unit can estimate the user's emotions and select training data based on those emotions. For example, if the user is feeling stressed, the learning unit can select simpler training data. If the user is relaxed, it can select more detailed training data. Furthermore, if the user is in a hurry, it can select data that allows for rapid learning. This enables efficient learning by selecting training data according to the user's emotions.

[0110] The suggestion function can estimate the user's emotions and adjust the way it suggests a judgment based on those emotions. For example, if the user is stressed, the suggestion function can use a concise and easy-to-understand suggestion method. If the user is relaxed, it can use a more detailed suggestion method. Furthermore, if the user is in a hurry, the suggestion function can provide a quick suggestion. This allows for efficient judgment suggestions by adjusting the suggestion method according to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection team collects details of the trial. These details include the date and time of the trial, the location, information about those involved, evidence, and the content of the judgment. For example, the date and time of the trial are collected in YYYY-MM-DD format, and the location is collected as an address or GPS coordinate. Information about those involved is collected as names, titles, and contact information, and evidence is collected as documents, photographs, videos, etc. The content of the judgment is collected as the judgment text and the reasoning behind the judgment. Step 2: The storage unit encrypts the trial details collected by the collection unit and stores them on the blockchain. Algorithms such as AES or RSA are used for encryption, and either a public or private blockchain is used. For example, data can be encrypted using AES and stored on a public blockchain. Step 3: The learning unit uses generative AI to collect data from databases of laws and precedents in various countries and uses this data for learning. Algorithms such as machine learning and deep learning are used for learning. For example, it is possible to learn data using machine learning and to learn data using deep learning. Step 4: The proposing unit proposes a judgment based on the information learned by the learning unit. Proposed judgments may include those based on past precedents or those based on legal interpretations. For example, a judgment may be proposed based on past precedents, or a judgment may be proposed based on legal interpretations. Step 5: The providing department provides the judgment proposed by the proposing department to a human judge in real time during the trial. This provision can be done via email or a dedicated application. For example, the judgment can be provided via email or a dedicated application.

[0113] 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.

[0114] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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. 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.

[0131] 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.

[0132] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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. 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.

[0147] 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.

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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. 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.

[0164] 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.

[0165] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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. The collection department gathers details of the trial, A storage unit encrypts the court details collected by the aforementioned collection unit and stores them on the blockchain, The learning section is for studying court precedents from various countries, regions, and eras. A proposal unit proposes a judgment based on the information learned by the aforementioned learning unit, The system comprises: a provisioning unit that provides the judgment proposed by the proposal unit to a human judge; A system characterized by the following features.

2. The aforementioned collection unit is We collect data including the date, time, location, information of those involved, evidence, and the content of the judgment. The system according to feature 1.

3. The aforementioned storage unit is The data collected by the aforementioned collection unit is encrypted and stored on the blockchain. The system according to feature 1.

4. The aforementioned learning unit, We collect data from legal and case law databases of various countries and use that data for learning. The system according to feature 1.

5. The aforementioned proposal section is, Based on the information learned by the aforementioned learning unit, a judgment is proposed. The system according to feature 1.

6. The aforementioned supply unit is, The proposed judgment by the aforementioned proposal unit is provided to human judges in real time during the trial. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting court details based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past court data and select the most efficient data collection method. The system according to feature 1.

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