system

Abstract

The system according to this embodiment aims to efficiently create documents that capture the key points of laws and precedents. [Solution] The system according to the embodiment comprises an input unit, a generation unit, and a verification unit. The input unit inputs an outline of the case or evidence. The generation unit generates a document that highlights the key points of the law and precedents based on the information input by the input unit. The verification unit verifies and corrects the document generated by the generation unit.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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, the creation of investigation documents for new modus operandi and complex cybercrimes has not been carried out efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently create a document that grasps the points of laws and precedents.

Means for Solving the Problems

[0006] The system according to the embodiment includes an input unit, a generation unit, and a confirmation unit. The input unit inputs an outline of an event or evidence. The generation unit generates a document that grasps the points of laws and precedents based on the information input by the input unit. The confirmation unit confirms and corrects the document generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently create documents that capture the key points of laws and precedents. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 investigation document creation support system according to an embodiment of the present invention is a system for investigators who are struggling to prosecute new methods and complex cybercrimes. This system allows investigators to input the outline of the case and evidence, and a generating AI generates a document that incorporates key points of laws and precedents, which is then provided for review and revision by investigators. For example, investigators input the date and time of the incident, location, information of those involved, and type of evidence. This information is input into the generating AI. Next, the generating AI analyzes the input information and generates a document that incorporates key points of laws and precedents. The generating AI creates an appropriate document based on past precedents and legal knowledge. For example, a document is generated that takes into account the method of presenting evidence and the scope of application of the law. The generated document is created in a format that is easy for suspects to understand. For example, technical terms are avoided, and explanations are given in simple language. In addition, diagrams and tables are used to make it easy to understand visually. Furthermore, the generated document is provided for review and revision by investigators. Investigators can review the generated document and make revisions as needed. This improves the quality of the final document. This system will enable investigators to efficiently create investigative documents, which is expected to raise the overall investigative capabilities and improve productivity of police organizations nationwide. For example, it will enable quicker and more appropriate responses to new methods and complex cybercrimes. In this way, the investigative document creation support system will enable investigators to efficiently create investigative documents, thereby raising the overall investigative capabilities and improving productivity of police organizations nationwide.

[0029] The investigation document creation support system according to the embodiment comprises an input unit, a generation unit, and a verification unit. The input unit inputs the outline of the case or evidence. The outline of the case includes, but is not limited to, the date and time of the incident, the location, information of those involved, and the type of evidence. The input unit can input information in the form of, for example, text, images, and audio. The generation unit generates a document that incorporates the key points of laws and precedents based on the information input by the input unit. The generation unit generates a document based on past precedents and legal knowledge using a generation AI. The generation AI, for example, refers to the key points of specific legal provisions and past precedents to create an appropriate document. The generation unit also generates a document that takes into account the method of presenting evidence and the scope of application of the law. For example, it generates a document that takes into account the type of evidence, the order of presentation, and the conditions for application of the law. Furthermore, the generation unit generates a document that avoids technical jargon and explains in simple language. For example, it avoids legal and technical terms and explains using general everyday language. The generation unit also generates a document that is easy to understand visually using diagrams and tables. For example, it creates a document using flowcharts, graphs, statistical tables, etc. The verification unit verifies the document generated by the generation unit and makes corrections as necessary. The verification unit performs tasks such as grammatical checks and verification of the accuracy of the content. The verification unit also manages the revision history and can track the revision history. As a result, the investigation document creation support system according to the embodiment can efficiently input the outline of a case and evidence, generate documents that capture the key points of laws and precedents, and enable verification and revision. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs information entered by the input unit into the generation AI, and the generation AI generates the document. Some or all of the above-described processes in the verification unit may be performed using, for example, an AI, or without a AI. For example, the verification unit inputs the generated document into the AI, and the AI ​​performs grammatical checks and verification of the accuracy of the content.

[0030] The generation unit can generate documents based on past precedents and legal knowledge. For example, the generation unit generates documents based on past precedents and legal knowledge using specific case law databases or methods for referencing legal provisions. For example, the generation unit searches for relevant precedents in a case law database and reflects their key points in the document. The generation unit can also refer to legal provisions and include the points of applicable laws in the document. For example, the generation unit includes the application conditions and interpretations of specific legal provisions in the document. In this way, legally appropriate documents are generated by generating documents based on past precedents and legal knowledge. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs a case law database or legal provisions into a generation AI, and the generation AI generates the document.

[0031] The generation unit can generate documents that take into account the method of presenting evidence or the scope of application of the law. For example, the generation unit generates documents considering the type of evidence, the order of presentation, and the conditions for application of the law. For example, the generation unit selects an appropriate method of presentation according to the type of evidence and includes it in the document. The generation unit can also take into account the scope of application of the law and include applicable legal provisions and their interpretations in the document. For example, the generation unit includes the conditions for application and interpretation of specific legal provisions in the document. By generating documents that take into account the method of presenting evidence and the scope of application of the law, legally appropriate documents are generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the type of evidence, the order of presentation, and the conditions for application of the law into the generation AI, and the generation AI generates the document.

[0032] The generation unit can generate documents that explain things in simple language without using technical jargon. For example, the generation unit can generate documents that avoid legal and technical terms and use common, everyday language. For example, the generation unit can replace legal terms with simple language and create a document. The generation unit can also replace technical terms with simple explanations and create a document. As a result, by generating documents that avoid technical jargon and explain things in simple language, documents that are easy for suspects to understand are generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input legal or technical terms into a generation AI, and the generation AI can generate a document in simple language.

[0033] The generation unit can generate documents that are easy to understand visually using diagrams and tables. For example, the generation unit creates documents using flowcharts, graphs, and statistical tables. For example, the generation unit can show the flow of an incident using a flowchart and generate a document that is easy to understand visually. The generation unit can also show statistical data of evidence using graphs and statistical tables and generate a document that is easy to understand visually. As a result, by generating documents that are easy to understand visually using diagrams and tables, documents that are easy for suspects to understand are generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs flowcharts, graphs, and statistical tables into a generation AI, and the generation AI generates the document.

[0034] The verification unit can review the generated document and make corrections as needed. For example, the verification unit can perform grammatical checks and verify the accuracy of the content. For instance, the verification unit checks the grammar of the generated document and corrects errors. It can also review the content of the generated document and make corrections to ensure accuracy. This process, by reviewing the generated document and making corrections as needed, improves the quality of the final document. Some or all of the above-described processes in the verification unit may be performed using AI, or not. For example, the verification unit inputs the generated document into an AI, which then performs grammatical checks and verifies the accuracy of the content.

[0035] The verification unit can manage the revision history. For example, the verification unit can record the date and time of revisions and the content of the revisions, and manage the revision history. For example, the verification unit can save the revision history of the generated document to a database so that it can be referenced later. The verification unit can also search the revision history and check specific revisions. This makes it easier to track the revision history by managing the revision history. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit inputs the revision history into the AI, and the AI ​​manages the revision history.

[0036] The system can be deployed to each police organization and provide the necessary training or support for its operation. The system provides training and support to each police organization, for example, through the provision of online courses and manuals. For example, the system explains how to use and operate the system through online courses. The system can also provide manuals to support each police organization in effectively operating the system. This enables the effective operation of the system by deploying it to each police organization and providing the necessary training and support for its operation. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system inputs the content of the training and support into the AI, and the AI ​​provides the training and support.

[0037] The input unit can analyze past input history and select the optimal input method. For example, the input unit can store past input history in a database and analyze that data. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the input unit can automatically complete input fields based on information the user has entered in the past. For example, the input unit can automatically complete information such as addresses and names that the user has entered in the past, thereby streamlining the input process. In this way, by analyzing past input history, the input unit can provide the user with the most suitable input method. Some or all of the above processes in the input unit may be performed using AI, for example, or not. For example, the input unit can input past input history data into AI, and the AI ​​can select the optimal input method.

[0038] The input unit can automatically customize input fields based on the type and severity of the incident during input. For example, the input unit can store the type and severity of the incident in a database and customize the input fields based on that data. For instance, in the case of a serious incident, the input unit can display detailed input fields to cover all necessary information. Conversely, in the case of a minor incident, the input unit can display simplified input fields, allowing for quick completion of the input. Furthermore, the input unit can automatically add relevant input fields depending on the specific incident category. For example, in the case of cybercrime, the input unit can add input fields related to specific technical terms and methods. This enables efficient input by customizing input fields based on the type and severity of the incident. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input incident type and severity data into the AI, and the AI ​​can customize the input fields.

[0039] The input unit can prioritize inputting highly relevant information by considering the user's geographical location during input. For example, the input unit can obtain the user's geographical location using GPS data or location services. For instance, if the user is in a specific region, the input unit will prioritize displaying input items related to that region. Furthermore, if the user is on the move, the input unit can automatically update input items based on their current location. Additionally, if the user is in a specific location, the input unit can automatically input information related to that location. For example, if the user is at a police station, the input unit will prioritize inputting information related to that location. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can input geographical location data into AI, which then prioritizes inputting highly relevant information.

[0040] The input unit can analyze the user's social media activity during input and automatically input relevant information. For example, the input unit can analyze the content of social media posts and followers. For example, the input unit can automatically complete input fields based on information shared by the user on social media. The input unit can also automatically input relevant incident information from the user's social media activity. Furthermore, the input unit can automatically set input fields based on the location and date mentioned by the user on social media. For example, the input unit can automatically input details of incidents mentioned by the user on social media. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input social media data into AI, and the AI ​​can automatically input relevant information.

[0041] The generation unit can adjust the level of detail in a document based on the severity of the case when generating it. For example, the generation unit can store the severity of the case in a database and adjust the level of detail based on that data. For example, in the case of a serious case, the generation unit can generate a document that includes detailed evidence and the scope of application of the law. In the case of a minor case, the generation unit can generate a simplified document. Furthermore, the generation unit can also generate a document with the necessary level of detail adjusted according to a specific case category. For example, in the case of cybercrime, the generation unit can generate a document that includes detailed explanations of specific technical terms and methods. This ensures that an appropriate document is generated by adjusting the level of detail based on the severity of the case. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not. For example, the generation unit inputs the severity of the case data into the generation AI, and the generation AI adjusts the level of detail in the document.

[0042] The generation unit can apply different generation algorithms depending on the category of the case when generating documents. For example, the generation unit can store the category of the case in a database and apply different generation algorithms based on that data. For example, in the case of cybercrime, the generation unit can generate documents that include specific technical terms and methods. In the case of violent crime, the generation unit can generate documents that emphasize victim testimony and evidence. Furthermore, in the case of economic crime, the generation unit can generate documents that describe financial data and transaction history in detail. In this way, appropriate documents are generated by applying different generation algorithms depending on the category of the case. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the category of the case data into the generation AI, and the generation AI applies different generation algorithms.

[0043] The generation unit can determine the priority of documents based on the timing of the events when generating documents. For example, the generation unit can store the timing of events in a database and determine the priority of documents based on that data. For example, the generation unit can prioritize the generation of documents for recently occurring events. The generation unit can also generate documents for past events as needed. Furthermore, the generation unit can generate appropriate documents for events that occurred at a specific time. For example, the generation unit can generate appropriate documents for events related to a specific season or event. In this way, appropriate documents are generated by determining the priority of documents based on the timing of the events. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the timing of events data into the generation AI, and the generation AI determines the priority of documents.

[0044] The generation unit can adjust the order of documents based on the relevance of the cases when generating documents. For example, the generation unit can store the relevance of the cases in a database and adjust the order of documents based on that data. For example, the generation unit can list important evidence and information first and organize the documents in order of relevance. The generation unit can also adjust the order of documents by placing less relevant information later. Furthermore, the generation unit can dynamically change the order of documents according to the progress of the cases. For example, the generation unit can add newly obtained information as the cases progress and reconstruct the order of the documents. In this way, appropriate documents are generated by adjusting the order of documents based on the relevance of the cases. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the relevance of the cases data into the generation AI, and the generation AI adjusts the order of the documents.

[0045] The verification unit can select the optimal verification method by referring to past verification history during the verification process. For example, the verification unit can store past verification history in a database and select the optimal verification method based on that data. For example, the verification unit can prioritize suggesting verification methods that the user has used in the past. The verification unit can also predict and suggest verification methods to be used during a specific time period based on the user's past verification history. Furthermore, the verification unit can automatically complete verification items based on information that the user has previously verified. For example, the verification unit can automatically add relevant verification items based on content that the user has previously verified. This allows the system to provide the optimal verification method by referring to past verification history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past verification history data into AI, and the AI ​​can select the optimal verification method.

[0046] The verification unit can determine the priority of verification based on the severity of the case during verification. For example, the verification unit can store the severity of the case in a database and determine the priority of verification based on that data. For example, in the case of a serious case, the verification unit will display important verification items first, allowing for quick verification completion. In the case of a minor case, the verification unit will display simplified verification items, allowing for quick verification completion. Furthermore, the verification unit can automatically add relevant verification items depending on the specific case category. For example, in the case of cybercrime, the verification unit will add verification items related to specific technical terms and methods. This enables efficient verification by determining the priority of verification based on the severity of the case. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the severity of the case data into the AI, and the AI ​​will determine the priority of verification.

[0047] The verification unit can select the optimal verification method by considering the user's device information during verification. For example, the verification unit can store the user's device information in a database and select the optimal verification method based on that data. For example, if the user is using a smartphone, the verification unit can provide a verification method that is appropriate for the screen size. Also, if the user is using a tablet, the verification unit can provide a verification method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the verification unit can provide a simple and highly visible verification method. In this way, the optimal verification method can be provided by considering the user's device information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit inputs the user's device information data into the AI, and the AI ​​selects the optimal verification method.

[0048] The verification unit can suggest corrections by referring to the user's past revision history during the verification process. For example, the verification unit can save past revision history to a database and suggest corrections based on that data. For example, the verification unit can suggest corrections based on content previously modified by the user. The verification unit can also predict and suggest specific correction methods from the user's past revision history. Furthermore, the verification unit can automatically complete correction items based on information previously modified by the user. For example, the verification unit can automatically add relevant correction items based on content previously modified by the user. This makes it possible to suggest appropriate corrections by referring to the user's past revision history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past revision history data into AI, and the AI ​​can suggest corrections.

[0049] The system can provide customized support based on the characteristics of each police organization during system deployment. For example, the system can store the size, personnel, and regional characteristics of each police organization in a database and provide customized support based on that data. For example, the system can provide an optimal support plan according to the size and personnel of each police organization. The system can also provide customized support based on the regional characteristics of each police organization. Furthermore, the system can provide optimal support based on the past operational history of each police organization. For example, the system can analyze past operational history and provide optimal support for each police organization. This enables effective system deployment by providing customized support based on the characteristics of each police organization. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system inputs characteristic data of each police organization into AI, and the AI ​​provides customized support.

[0050] The system can select the optimal deployment method by referring to past deployment history during system deployment. For example, the system can store past deployment history in a database and select the optimal deployment method based on that data. For example, the system can formulate an optimal deployment plan based on past successful deployment methods. The system can also extract problems from past deployment history and select a deployment method that incorporates improvement measures. Furthermore, the system can analyze past deployment history and propose the optimal deployment method for each police organization. For example, the system proposes the optimal deployment method for each police organization based on past deployment history. In this way, the system can provide the optimal deployment method by referring to past deployment history. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system can input past deployment history data into AI, and the AI ​​will select the optimal deployment method.

[0051] The system can select the optimal deployment method when deploying the system, taking into account the geographical location information of each police organization. For example, the system can store the geographical location information of each police organization in a database and select the optimal deployment method based on that data. For example, the system can formulate an optimal deployment schedule based on the geographical location of each police organization. The system can also assign the most suitable support team to each police organization based on the geographical location information. Furthermore, the system can provide the most suitable training plan to each police organization, taking into account the geographical location information. For example, the system can provide the most suitable training plan to each police organization based on the geographical location information. In this way, the system can provide the optimal deployment method by taking into account the geographical location information of each police organization. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system can input geographical location data into AI, and the AI ​​can select the optimal deployment method.

[0052] The system can propose training content by referring to the past training history of each police organization during system deployment. For example, the system can store the past training history of each police organization in a database and propose training content based on that data. For example, the system can propose the optimal training content based on the past training history of each police organization. The system can also extract and propose effective training methods from past training history. Furthermore, the system can analyze the training history of each police organization and provide customized training content as needed. For example, the system can propose the optimal training content for each police organization based on past training history. This allows the system to provide optimal training content by referring to the past training history of each police organization. Some or all of the above processes in the system may be performed using AI, or not. For example, the system inputs past training history data into AI, and the AI ​​proposes training content.

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

[0054] The input unit can analyze user input in real time and automatically suggest relevant laws and precedents based on that input. For example, if a user inputs information about a specific crime, the input unit will instantly display relevant legal articles and past precedents. The input unit can also suggest appropriate methods of presenting evidence depending on the type of evidence entered by the user. Furthermore, the input unit can automatically supplement relevant additional information based on the information entered by the user. This allows users to efficiently input necessary information and quickly refer to relevant laws and precedents.

[0055] The generation unit can customize the content of a document by referring to the user's past input history during document generation. For example, the generation unit can automatically incorporate relevant laws and precedents into the document based on information previously entered by the user. Furthermore, the generation unit can learn specific expressions and writing styles from the user's past input history and generate documents accordingly. In addition, the generation unit can automatically include specialized and technical terms previously used by the user in the document. This allows for the generation of more appropriate documents by referencing the user's past input history.

[0056] The input section can automatically suggest region-specific laws and regulations, taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize displaying laws and regulations relevant to that region. Furthermore, if the user is on the move, the input section can automatically update relevant laws and regulations based on their current location. In addition, if the user is in a specific location, the input section can automatically input information relevant to that location. This allows for the prioritization of highly relevant information by considering the user's geographical location.

[0057] The verification unit can improve the efficiency of verification by referring to the user's past verification history. For example, the verification unit can automatically add relevant verification items based on what the user has previously verified. Furthermore, the verification unit can learn specific verification methods from the user's past verification history and suggest the optimal verification method. In addition, the verification unit can suggest corrections based on what the user has previously modified. This enables efficient verification by referring to the user's past verification history.

[0058] The input unit can analyze the user's social media activity and automatically input relevant information. For example, the input unit can automatically complete input fields based on information the user has shared on social media. It can also automatically input relevant incident information from the user's social media activity. Furthermore, the input unit can automatically set input fields based on the location and date mentioned by the user on social media. This allows for the automatic input of relevant information by analyzing the user's social media activity.

[0059] The verification unit can select the optimal verification method by considering the user's device information. For example, if the user is using a smartphone, the verification unit can provide a verification method that matches the screen size. Furthermore, if the user is using a tablet, the verification unit can provide a verification method optimized for a larger screen. Additionally, if the user is using a smartwatch, the verification unit can provide a concise and highly visible verification method. In this way, the system can provide the optimal verification method by considering the user's device information.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The input section is used to enter the case summary or evidence. The case summary includes the date and time of the incident, location, information about those involved, and the type of evidence. Information can be entered in the input section in various formats, such as text, images, and audio. Step 2: The generation unit generates documents that highlight key points of laws and precedents based on the information entered by the input unit. The generation unit uses generation AI to generate documents based on past precedents and legal knowledge, and generates documents that take into account methods of presenting evidence and the scope of application of the law. It also generates documents that avoid technical jargon and explain in simple language, as well as documents that are easy to understand visually using diagrams and tables. Step 3: The verification unit reviews the document generated by the generation unit and makes corrections as needed. The verification unit performs grammatical checks and verifies the accuracy of the content, manages the revision history, and can track the revision history.

[0062] (Example of form 2) The investigation document creation support system according to an embodiment of the present invention is a system for investigators who are struggling to prosecute new methods and complex cybercrimes. This system allows investigators to input the outline of the case and evidence, and a generating AI generates a document that incorporates key points of laws and precedents, which is then provided for review and revision by investigators. For example, investigators input the date and time of the incident, location, information of those involved, and type of evidence. This information is input into the generating AI. Next, the generating AI analyzes the input information and generates a document that incorporates key points of laws and precedents. The generating AI creates an appropriate document based on past precedents and legal knowledge. For example, a document is generated that takes into account the method of presenting evidence and the scope of application of the law. The generated document is created in a format that is easy for suspects to understand. For example, technical terms are avoided, and explanations are given in simple language. In addition, diagrams and tables are used to make it easy to understand visually. Furthermore, the generated document is provided for review and revision by investigators. Investigators can review the generated document and make revisions as needed. This improves the quality of the final document. This system will enable investigators to efficiently create investigative documents, which is expected to raise the overall investigative capabilities and improve productivity of police organizations nationwide. For example, it will enable quicker and more appropriate responses to new methods and complex cybercrimes. In this way, the investigative document creation support system will enable investigators to efficiently create investigative documents, thereby raising the overall investigative capabilities and improving productivity of police organizations nationwide.

[0063] The investigation document creation support system according to the embodiment comprises an input unit, a generation unit, and a verification unit. The input unit inputs the outline of the case or evidence. The outline of the case includes, but is not limited to, the date and time of the incident, the location, information of those involved, and the type of evidence. The input unit can input information in the form of, for example, text, images, and audio. The generation unit generates a document that incorporates the key points of laws and precedents based on the information input by the input unit. The generation unit generates a document based on past precedents and legal knowledge using a generation AI. The generation AI, for example, refers to the key points of specific legal provisions and past precedents to create an appropriate document. The generation unit also generates a document that takes into account the method of presenting evidence and the scope of application of the law. For example, it generates a document that takes into account the type of evidence, the order of presentation, and the conditions for application of the law. Furthermore, the generation unit generates a document that avoids technical jargon and explains in simple language. For example, it avoids legal and technical terms and explains using general everyday language. The generation unit also generates a document that is easy to understand visually using diagrams and tables. For example, it creates a document using flowcharts, graphs, statistical tables, etc. The verification unit verifies the document generated by the generation unit and makes corrections as necessary. The verification unit performs tasks such as grammatical checks and verification of the accuracy of the content. The verification unit also manages the revision history and can track the revision history. As a result, the investigation document creation support system according to the embodiment can efficiently input the outline of a case and evidence, generate documents that capture the key points of laws and precedents, and enable verification and revision. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs information entered by the input unit into the generation AI, and the generation AI generates the document. Some or all of the above-described processes in the verification unit may be performed using, for example, an AI, or without a AI. For example, the verification unit inputs the generated document into the AI, and the AI ​​performs grammatical checks and verification of the accuracy of the content.

[0064] The generation unit can generate documents based on past precedents and legal knowledge. For example, the generation unit generates documents based on past precedents and legal knowledge using specific case law databases or methods for referencing legal provisions. For example, the generation unit searches for relevant precedents in a case law database and reflects their key points in the document. The generation unit can also refer to legal provisions and include the points of applicable laws in the document. For example, the generation unit includes the application conditions and interpretations of specific legal provisions in the document. In this way, legally appropriate documents are generated by generating documents based on past precedents and legal knowledge. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs a case law database or legal provisions into a generation AI, and the generation AI generates the document.

[0065] The generation unit can generate documents that take into account the method of presenting evidence or the scope of application of the law. For example, the generation unit generates documents considering the type of evidence, the order of presentation, and the conditions for application of the law. For example, the generation unit selects an appropriate method of presentation according to the type of evidence and includes it in the document. The generation unit can also take into account the scope of application of the law and include applicable legal provisions and their interpretations in the document. For example, the generation unit includes the conditions for application and interpretation of specific legal provisions in the document. By generating documents that take into account the method of presenting evidence and the scope of application of the law, legally appropriate documents are generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the type of evidence, the order of presentation, and the conditions for application of the law into the generation AI, and the generation AI generates the document.

[0066] The generation unit can generate documents that explain things in simple language without using technical jargon. For example, the generation unit can generate documents that avoid legal and technical terms and use common, everyday language. For example, the generation unit can replace legal terms with simple language and create a document. The generation unit can also replace technical terms with simple explanations and create a document. As a result, by generating documents that avoid technical jargon and explain things in simple language, documents that are easy for suspects to understand are generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input legal or technical terms into a generation AI, and the generation AI can generate a document in simple language.

[0067] The generation unit can generate documents that are easy to understand visually using diagrams and tables. For example, the generation unit creates documents using flowcharts, graphs, and statistical tables. For example, the generation unit can show the flow of an incident using a flowchart and generate a document that is easy to understand visually. The generation unit can also show statistical data of evidence using graphs and statistical tables and generate a document that is easy to understand visually. As a result, by generating documents that are easy to understand visually using diagrams and tables, documents that are easy for suspects to understand are generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs flowcharts, graphs, and statistical tables into a generation AI, and the generation AI generates the document.

[0068] The verification unit can review the generated document and make corrections as needed. For example, the verification unit can perform grammatical checks and verify the accuracy of the content. For instance, the verification unit checks the grammar of the generated document and corrects errors. It can also review the content of the generated document and make corrections to ensure accuracy. This process, by reviewing the generated document and making corrections as needed, improves the quality of the final document. Some or all of the above-described processes in the verification unit may be performed using AI, or not. For example, the verification unit inputs the generated document into an AI, which then performs grammatical checks and verifies the accuracy of the content.

[0069] The verification unit can manage the revision history. For example, the verification unit can record the date and time of revisions and the content of the revisions, and manage the revision history. For example, the verification unit can save the revision history of the generated document to a database so that it can be referenced later. The verification unit can also search the revision history and check specific revisions. This makes it easier to track the revision history by managing the revision history. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit inputs the revision history into the AI, and the AI ​​manages the revision history.

[0070] The system can be deployed to each police organization and provide the necessary training or support for its operation. The system provides training and support to each police organization, for example, through the provision of online courses and manuals. For example, the system explains how to use and operate the system through online courses. The system can also provide manuals to support each police organization in effectively operating the system. This enables the effective operation of the system by deploying it to each police organization and providing the necessary training and support for its operation. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system inputs the content of the training and support into the AI, and the AI ​​provides the training and support.

[0071] The input unit can estimate the user's emotions and adjust the timing of input based on the estimated emotions. For example, the input unit can estimate the user's emotions using facial recognition or voice analysis. For instance, the input unit can capture the user's facial expressions with a camera and estimate emotions using a facial recognition algorithm. Alternatively, the input unit can record the user's voice with a microphone and estimate emotions using voice analysis technology. For example, if the user is stressed, the input unit can delay the timing of input to provide a relaxing environment. Conversely, if the user is concentrating, the input unit can speed up the timing of input to enable efficient information input. Furthermore, if the user is tired, the input unit can adjust the timing of input and display a message encouraging a break. This allows for more appropriate input by adjusting the timing of input according to the user's emotions. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can input user facial expression data or voice data into an AI, which then estimates emotions and adjusts the timing of input.

[0072] The input unit can analyze past input history and select the optimal input method. For example, the input unit can store past input history in a database and analyze that data. For example, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the input unit can automatically complete input fields based on information the user has entered in the past. For example, the input unit can automatically complete information such as addresses and names that the user has entered in the past, thereby streamlining the input process. In this way, by analyzing past input history, the input unit can provide the user with the most suitable input method. Some or all of the above processes in the input unit may be performed using AI, for example, or not. For example, the input unit can input past input history data into AI, and the AI ​​can select the optimal input method.

[0073] The input unit can automatically customize input fields based on the type and severity of the incident during input. For example, the input unit can store the type and severity of the incident in a database and customize the input fields based on that data. For instance, in the case of a serious incident, the input unit can display detailed input fields to cover all necessary information. Conversely, in the case of a minor incident, the input unit can display simplified input fields, allowing for quick completion of the input. Furthermore, the input unit can automatically add relevant input fields depending on the specific incident category. For example, in the case of cybercrime, the input unit can add input fields related to specific technical terms and methods. This enables efficient input by customizing input fields based on the type and severity of the incident. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input incident type and severity data into the AI, and the AI ​​can customize the input fields.

[0074] The input unit can estimate the user's emotions and determine the priority of input items based on the estimated emotions. The input unit can estimate the user's emotions using, for example, facial recognition or voice analysis. For example, the input unit can capture the user's facial expressions with a camera and estimate emotions using a facial recognition algorithm. Alternatively, the input unit can record the user's voice with a microphone and estimate emotions using voice analysis technology. For example, if the user is tense, the input unit can display important input items first to allow for quick completion. If the user is relaxed, the input unit can postpone detailed input items to make the input process easier. Furthermore, if the user is tired, the input unit can display simpler input items first to reduce the input workload. This enables efficient input by determining the priority of input items according to the user's emotions. Some or all of the above processing in the input unit may be performed using, for example, AI, or without AI. For example, the input unit can input the user's facial expression data or voice data into an AI, which will estimate emotions and determine the priority of input items.

[0075] The input unit can prioritize inputting highly relevant information by considering the user's geographical location during input. For example, the input unit can obtain the user's geographical location using GPS data or location services. For instance, if the user is in a specific region, the input unit will prioritize displaying input items related to that region. Furthermore, if the user is on the move, the input unit can automatically update input items based on their current location. Additionally, if the user is in a specific location, the input unit can automatically input information related to that location. For example, if the user is at a police station, the input unit will prioritize inputting information related to that location. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can input geographical location data into AI, which then prioritizes inputting highly relevant information.

[0076] The input unit can analyze the user's social media activity during input and automatically input relevant information. For example, the input unit can analyze the content of social media posts and followers. For example, the input unit can automatically complete input fields based on information shared by the user on social media. The input unit can also automatically input relevant incident information from the user's social media activity. Furthermore, the input unit can automatically set input fields based on the location and date mentioned by the user on social media. For example, the input unit can automatically input details of incidents mentioned by the user on social media. In this way, relevant information can be automatically input by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input social media data into AI, and the AI ​​can automatically input relevant information.

[0077] The generation unit can estimate the user's emotions and adjust the way the document is written based on those emotions. For example, the generation unit can estimate the user's emotions using facial recognition or voice analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. Alternatively, the generation unit can record the user's voice with a microphone and estimate their emotions using voice analysis technology. For example, if the user is nervous, the generation unit can generate a concise and clear document. If the user is relaxed, the generation unit can generate a document that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a short document that gets straight to the point. By adjusting the way the document is written according to the user's emotions, a more appropriate document is generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's facial expression data or voice data into a generation AI, which will estimate the emotions and adjust the way the document is written.

[0078] The generation unit can adjust the level of detail in a document based on the severity of the case when generating it. For example, the generation unit can store the severity of the case in a database and adjust the level of detail based on that data. For example, in the case of a serious case, the generation unit can generate a document that includes detailed evidence and the scope of application of the law. In the case of a minor case, the generation unit can generate a simplified document. Furthermore, the generation unit can also generate a document with the necessary level of detail adjusted according to a specific case category. For example, in the case of cybercrime, the generation unit can generate a document that includes detailed explanations of specific technical terms and methods. This ensures that an appropriate document is generated by adjusting the level of detail based on the severity of the case. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not. For example, the generation unit inputs the severity of the case data into the generation AI, and the generation AI adjusts the level of detail in the document.

[0079] The generation unit can apply different generation algorithms depending on the category of the case when generating documents. For example, the generation unit can store the category of the case in a database and apply different generation algorithms based on that data. For example, in the case of cybercrime, the generation unit can generate documents that include specific technical terms and methods. In the case of violent crime, the generation unit can generate documents that emphasize victim testimony and evidence. Furthermore, in the case of economic crime, the generation unit can generate documents that describe financial data and transaction history in detail. In this way, appropriate documents are generated by applying different generation algorithms depending on the category of the case. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the category of the case data into the generation AI, and the generation AI applies different generation algorithms.

[0080] The generation unit can estimate the user's emotions and adjust the length of the document based on the estimated emotions. The generation unit can estimate the user's emotions using, for example, facial recognition or voice analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using a facial recognition algorithm. Alternatively, the generation unit can record the user's voice with a microphone and estimate emotions using voice analysis technology. For example, if the user is in a hurry, the generation unit can generate a short, concise document. If the user is relaxed, the generation unit can generate a longer document with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a document with visually stimulating effects. By adjusting the length of the document according to the user's emotions, a more appropriate document can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's facial expression data or voice data into the generation AI, which estimates emotions and adjusts the length of the document.

[0081] The generation unit can determine the priority of documents based on the timing of the events when generating documents. For example, the generation unit can store the timing of events in a database and determine the priority of documents based on that data. For example, the generation unit can prioritize the generation of documents for recently occurring events. The generation unit can also generate documents for past events as needed. Furthermore, the generation unit can generate appropriate documents for events that occurred at a specific time. For example, the generation unit can generate appropriate documents for events related to a specific season or event. In this way, appropriate documents are generated by determining the priority of documents based on the timing of the events. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the timing of events data into the generation AI, and the generation AI determines the priority of documents.

[0082] The generation unit can adjust the order of documents based on the relevance of the cases when generating documents. For example, the generation unit can store the relevance of the cases in a database and adjust the order of documents based on that data. For example, the generation unit can list important evidence and information first and organize the documents in order of relevance. The generation unit can also adjust the order of documents by placing less relevant information later. Furthermore, the generation unit can dynamically change the order of documents according to the progress of the cases. For example, the generation unit can add newly obtained information as the cases progress and reconstruct the order of the documents. In this way, appropriate documents are generated by adjusting the order of documents based on the relevance of the cases. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the relevance of the cases data into the generation AI, and the generation AI adjusts the order of the documents.

[0083] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated emotions. For example, the verification unit can estimate the user's emotions using facial recognition or voice analysis. For example, the verification unit can capture the user's facial expressions with a camera and estimate emotions using a facial recognition algorithm. Alternatively, the verification unit can record the user's voice with a microphone and estimate emotions using voice analysis technology. For example, if the user is nervous, the verification unit can provide a simple and highly visual verification method. If the user is relaxed, the verification unit can provide a verification method that includes detailed information. Furthermore, if the user is in a hurry, the verification unit can provide a concise verification method. By adjusting the verification method according to the user's emotions, more appropriate verification becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's facial expression data and voice data into AI, which will estimate emotions and adjust the verification method.

[0084] The verification unit can select the optimal verification method by referring to past verification history during the verification process. For example, the verification unit can store past verification history in a database and select the optimal verification method based on that data. For example, the verification unit can prioritize suggesting verification methods that the user has used in the past. The verification unit can also predict and suggest verification methods to be used during a specific time period based on the user's past verification history. Furthermore, the verification unit can automatically complete verification items based on information that the user has previously verified. For example, the verification unit can automatically add relevant verification items based on content that the user has previously verified. This allows the system to provide the optimal verification method by referring to past verification history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past verification history data into AI, and the AI ​​can select the optimal verification method.

[0085] The verification unit can determine the priority of verification based on the severity of the case during verification. For example, the verification unit can store the severity of the case in a database and determine the priority of verification based on that data. For example, in the case of a serious case, the verification unit will display important verification items first, allowing for quick verification completion. In the case of a minor case, the verification unit will display simplified verification items, allowing for quick verification completion. Furthermore, the verification unit can automatically add relevant verification items depending on the specific case category. For example, in the case of cybercrime, the verification unit will add verification items related to specific technical terms and methods. This enables efficient verification by determining the priority of verification based on the severity of the case. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the severity of the case data into the AI, and the AI ​​will determine the priority of verification.

[0086] The verification unit can estimate the user's emotions and adjust the verification procedure based on the estimated emotions. For example, the verification unit can estimate the user's emotions using facial recognition or voice analysis. For example, the verification unit can capture the user's facial expressions with a camera and estimate emotions using a facial recognition algorithm. Alternatively, the verification unit can record the user's voice with a microphone and estimate emotions using voice analysis technology. For example, if the user is nervous, the verification unit can provide a simple and highly visual verification procedure. If the user is relaxed, the verification unit can provide a verification procedure that includes detailed information. Furthermore, if the user is in a hurry, the verification unit can provide a concise verification procedure. By adjusting the verification procedure according to the user's emotions, more appropriate verification becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's facial expression data and voice data into AI, which will estimate emotions and adjust the verification procedure.

[0087] The verification unit can select the optimal verification method by considering the user's device information during verification. For example, the verification unit can store the user's device information in a database and select the optimal verification method based on that data. For example, if the user is using a smartphone, the verification unit can provide a verification method that is appropriate for the screen size. Also, if the user is using a tablet, the verification unit can provide a verification method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the verification unit can provide a simple and highly visible verification method. In this way, the optimal verification method can be provided by considering the user's device information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit inputs the user's device information data into the AI, and the AI ​​selects the optimal verification method.

[0088] The verification unit can suggest corrections by referring to the user's past revision history during the verification process. For example, the verification unit can save past revision history to a database and suggest corrections based on that data. For example, the verification unit can suggest corrections based on content previously modified by the user. The verification unit can also predict and suggest specific correction methods from the user's past revision history. Furthermore, the verification unit can automatically complete correction items based on information previously modified by the user. For example, the verification unit can automatically add relevant correction items based on content previously modified by the user. This makes it possible to suggest appropriate corrections by referring to the user's past revision history. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input past revision history data into AI, and the AI ​​can suggest corrections.

[0089] The system can estimate the user's emotions and adjust the training content based on those emotions. For example, the system can estimate the user's emotions using facial recognition or voice analysis. For instance, the system can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. Alternatively, the system can record the user's voice with a microphone and estimate their emotions using voice analysis technology. For example, if the user is tense, the system can provide relaxing training content. If the user is relaxed, the system can provide detailed training content. Furthermore, if the user is in a hurry, the system can provide concise and to-the-point training content. This allows for effective training by adjusting the training content according to the user's emotions. Some or all of the above processes in the system may be performed using AI, or without AI. For example, the system can input user facial expression data and voice data into an AI, which then estimates the emotions and adjusts the training content.

[0090] The system can provide customized support based on the characteristics of each police organization during system deployment. For example, the system can store the size, personnel, and regional characteristics of each police organization in a database and provide customized support based on that data. For example, the system can provide an optimal support plan according to the size and personnel of each police organization. The system can also provide customized support based on the regional characteristics of each police organization. Furthermore, the system can provide optimal support based on the past operational history of each police organization. For example, the system can analyze past operational history and provide optimal support for each police organization. This enables effective system deployment by providing customized support based on the characteristics of each police organization. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system inputs characteristic data of each police organization into AI, and the AI ​​provides customized support.

[0091] The system can select the optimal deployment method by referring to past deployment history during system deployment. For example, the system can store past deployment history in a database and select the optimal deployment method based on that data. For example, the system can formulate an optimal deployment plan based on past successful deployment methods. The system can also extract problems from past deployment history and select a deployment method that incorporates improvement measures. Furthermore, the system can analyze past deployment history and propose the optimal deployment method for each police organization. For example, the system proposes the optimal deployment method for each police organization based on past deployment history. In this way, the system can provide the optimal deployment method by referring to past deployment history. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system can input past deployment history data into AI, and the AI ​​will select the optimal deployment method.

[0092] The system can estimate the user's emotions and determine the priority of support based on those emotions. For example, the system can estimate the user's emotions using facial recognition or voice analysis. For instance, the system can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. Alternatively, the system can record the user's voice with a microphone and estimate their emotions using voice analysis technology. For example, if the user is tense, the system can provide quick support to reassure them. If the user is relaxed, the system can provide detailed support to deepen their understanding. Furthermore, if the user is in a hurry, the system can provide concise and to-the-point support. This allows for effective support by prioritizing support according to the user's emotions. Some or all of the above processes in the system may be performed using AI, or not. For example, the system can input user facial expression data or voice data into an AI, which then estimates the emotions and determines the priority of support.

[0093] The system can select the optimal deployment method when deploying the system, taking into account the geographical location information of each police organization. For example, the system can store the geographical location information of each police organization in a database and select the optimal deployment method based on that data. For example, the system can formulate an optimal deployment schedule based on the geographical location of each police organization. The system can also assign the most suitable support team to each police organization based on the geographical location information. Furthermore, the system can provide the most suitable training plan to each police organization, taking into account the geographical location information. For example, the system can provide the most suitable training plan to each police organization based on the geographical location information. In this way, the system can provide the optimal deployment method by taking into account the geographical location information of each police organization. Some or all of the above processes in the system may be performed using AI, for example, or not using AI. For example, the system can input geographical location data into AI, and the AI ​​can select the optimal deployment method.

[0094] The system can propose training content by referring to the past training history of each police organization during system deployment. For example, the system can store the past training history of each police organization in a database and propose training content based on that data. For example, the system can propose the optimal training content based on the past training history of each police organization. The system can also extract and propose effective training methods from past training history. Furthermore, the system can analyze the training history of each police organization and provide customized training content as needed. For example, the system can propose the optimal training content for each police organization based on past training history. This allows the system to provide optimal training content by referring to the past training history of each police organization. Some or all of the above processes in the system may be performed using AI, or not. For example, the system inputs past training history data into AI, and the AI ​​proposes training content. === Hard Collateral 1-1 === Each of the multiple elements described above, including the input unit, generation unit, and verification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit can input the outline of a case and evidence using the receiving device 38 of the smart device 14. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates a document that highlights the key points of laws and precedents using generation AI. The verification unit is implemented in the control unit 46A of the smart device 14 and can verify the generated document and make corrections as necessary. === Hard Collateral 1-2 === Each of the multiple elements described above, including the input unit, generation unit, and verification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit can input the outline of a case and evidence by voice using the microphone 238 of the smart glasses 214. The generation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and generates a document that highlights the key points of laws and precedents using generation AI. The verification unit is implemented, for example, in the control unit 46A of the smart glasses 214, and can verify the generated document and make corrections as necessary. === Hard Collateral 1-3 === Each of the multiple elements described above, including the input unit, generation unit, and verification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit can input the outline of the case and evidence by voice using the microphone 238 of the headset terminal 314. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates a document that highlights the key points of the law and precedents using generation AI. The verification unit is implemented in the control unit 46A of the headset terminal 314 and can verify the generated document and make corrections as necessary. === Hard Collateral 1-4 === Each of the multiple elements described above, including the input unit, generation unit, and verification unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the input unit can input the outline of the case and evidence by voice using the microphone 238 of the robot 414. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a document that highlights the key points of the law and precedents using generation AI. The verification unit is implemented, for example, by the control unit 46A of the robot 414, and can verify the generated document and make corrections as necessary.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The input unit can analyze user input in real time and automatically suggest relevant laws and precedents based on that input. For example, if a user inputs information about a specific crime, the input unit will instantly display relevant legal articles and past precedents. The input unit can also suggest appropriate methods of presenting evidence depending on the type of evidence entered by the user. Furthermore, the input unit can automatically supplement relevant additional information based on the information entered by the user. This allows users to efficiently input necessary information and quickly refer to relevant laws and precedents.

[0097] The generation unit can estimate the user's emotions and adjust the tone of the document based on those emotions. For example, if the user is stressed, the generation unit will soften the tone of the document and use reassuring language. If the user is relaxed, the generation unit can generate a document with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a concise document that gets straight to the point. By adjusting the tone of the document according to the user's emotions, a more appropriate document is generated.

[0098] The verification unit can estimate the user's emotions and adjust the timing of verification based on those emotions. For example, if the user is focused, the verification unit will prioritize displaying important verification items and allow for quick completion. If the user is tired, the verification unit can display simpler verification items first and show a message encouraging them to take a break. Furthermore, if the user is stressed, the verification unit will provide a simple and highly visible verification method. By adjusting the timing of verification according to the user's emotions, more appropriate verification becomes possible.

[0099] The generation unit can customize the content of a document by referring to the user's past input history during document generation. For example, the generation unit can automatically incorporate relevant laws and precedents into the document based on information previously entered by the user. Furthermore, the generation unit can learn specific expressions and writing styles from the user's past input history and generate documents accordingly. In addition, the generation unit can automatically include specialized and technical terms previously used by the user in the document. This allows for the generation of more appropriate documents by referencing the user's past input history.

[0100] The input section can automatically suggest region-specific laws and regulations, taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize displaying laws and regulations relevant to that region. Furthermore, if the user is on the move, the input section can automatically update relevant laws and regulations based on their current location. In addition, if the user is in a specific location, the input section can automatically input information relevant to that location. This allows for the prioritization of highly relevant information by considering the user's geographical location.

[0101] The generation unit can estimate the user's emotions and adjust the document's visual layout based on those emotions. For example, if the user is stressed, the generation unit will simplify the document's layout and adopt a visually less burdensome design. If the user is relaxed, the generation unit can use a complex layout that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can adopt a layout that emphasizes the main points. In this way, a more appropriate document is generated by adjusting the visual layout according to the user's emotions.

[0102] The verification unit can improve the efficiency of verification by referring to the user's past verification history. For example, the verification unit can automatically add relevant verification items based on what the user has previously verified. Furthermore, the verification unit can learn specific verification methods from the user's past verification history and suggest the optimal verification method. In addition, the verification unit can suggest corrections based on what the user has previously modified. This enables efficient verification by referring to the user's past verification history.

[0103] The input unit can analyze the user's social media activity and automatically input relevant information. For example, the input unit can automatically complete input fields based on information the user has shared on social media. It can also automatically input relevant incident information from the user's social media activity. Furthermore, the input unit can automatically set input fields based on the location and date mentioned by the user on social media. This allows for the automatic input of relevant information by analyzing the user's social media activity.

[0104] The generation unit can estimate the user's emotions and adjust the document length based on that estimation. For example, if the user is in a hurry, the generation unit will produce a short, concise document. If the user is relaxed, it can produce a longer document with detailed explanations. Furthermore, if the user is excited, it can produce a document with visually stimulating effects. By adjusting the document length according to the user's emotions, a more appropriate document is generated.

[0105] The verification unit can select the optimal verification method by considering the user's device information. For example, if the user is using a smartphone, the verification unit can provide a verification method that matches the screen size. Furthermore, if the user is using a tablet, the verification unit can provide a verification method optimized for a larger screen. Additionally, if the user is using a smartwatch, the verification unit can provide a concise and highly visible verification method. In this way, the system can provide the optimal verification method by considering the user's device information.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The input section is used to enter the case summary or evidence. The case summary includes the date and time of the incident, location, information about those involved, and the type of evidence. Information can be entered in the input section in various formats, such as text, images, and audio. Step 2: The generation unit generates documents that highlight key points of laws and precedents based on the information entered by the input unit. The generation unit uses generation AI to generate documents based on past precedents and legal knowledge, and generates documents that take into account methods of presenting evidence and the scope of application of the law. It also generates documents that avoid technical jargon and explain in simple language, as well as documents that are easy to understand visually using diagrams and tables. Step 3: The verification unit reviews the document generated by the generation unit and makes corrections as needed. The verification unit performs grammatical checks and verifies the accuracy of the content, manages the revision history, and can track the revision history.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering the case summary or evidence, A generation unit generates a document that highlights the key points of laws and precedents based on the information entered by the aforementioned input unit, The system includes a verification unit that checks and corrects the document generated by the generation unit. A system characterized by the following features.

2. The generating unit is Documents are generated based on past precedents and legal knowledge. The system according to feature 1.

3. The generating unit is Generate documents that take into account the method of presenting evidence or the scope of legal application. The system according to feature 1.

4. The generating unit is Generate documents that explain things in simple language, without using technical jargon. The system according to feature 1.

5. The generating unit is Generate visually easy-to-understand documents using diagrams and tables. The system according to feature 1.

6. The aforementioned verification unit is Review the generated document and make any necessary corrections. The system according to feature 1.

7. The aforementioned verification unit is Manage revision history The system according to feature 1.

8. The system is Deployed to each police organization, providing the necessary training or support for its operation. The system according to feature 1.

Images