system

Abstract

The system according to the embodiment aims to resolve cases quickly and prevent false accusations based on emergency calls to 110. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit monitors emergency calls and immediately begins collecting information. The analysis unit analyzes the information collected by the collection unit and grasps the outline of the incident. The generation unit formulates a case scenario hypothesis based on the information analyzed by the analysis unit. The provision unit provides the information generated by the generation unit to investigators.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to quickly resolve cases based on emergency calls, and there was a risk of false accusations.

[0005] The system according to the embodiment aims to resolve cases quickly and prevent false accusations based on emergency calls to 110. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit monitors emergency calls and immediately begins collecting information. The analysis unit analyzes the information collected by the collection unit and grasps the outline of the incident. The generation unit formulates an incident scenario hypothesis based on the information analyzed by the analysis unit. The provision unit provides the information generated by the generation unit to investigators. [Effects of the Invention]

[0007] The system according to the embodiment can resolve cases quickly and prevent false accusations based on emergency calls to 110. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention aims to resolve cases quickly and prevent false accusations. This system monitors emergency calls and immediately begins collecting information. It then analyzes the collected information to grasp the outline of the case. It then formulates a case scenario hypothesis based on the analyzed information and provides the resulting information to investigators. For example, the system analyzes the content of the call and extracts information such as the location, time, and parties involved in the incident. It then imports and analyzes investigative information, such as physical evidence of the incident or accident, eyewitness testimony, expert opinions, audio and video recordings, and confessions. It cross-checks the evidential data based on the analysis results and formulates a case scenario hypothesis. It then generates a reenactment video based on the hypothesis and provides information to accelerate the investigation. This enables the system to resolve cases quickly and prevent false accusations. For example, the system can compare eyewitness testimony with video recordings and, if there is a discrepancy, suggest the possibility of a false accusation. The system can also recommend missing evidence to prevent oversights in the investigation. This enables rapid and accurate investigations based on reliable verification information.

[0029] The case solving system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit monitors emergency calls and immediately begins collecting information. The collection unit, for example, analyzes the call content in real time to grasp an outline of the case. The collection unit can also extract information from the call content, such as the location, time, and parties involved in the incident. The analysis unit analyzes the information collected by the collection unit to grasp an outline of the case. The analysis unit analyzes investigative information, such as physical evidence of the incident or accident, eyewitness testimony, expert opinions, audio and video recordings, and confessions. The analysis unit can also compare eyewitness testimony with video recordings to confirm whether they match. The generation unit formulates a case scenario hypothesis based on the information analyzed by the analysis unit. The generation unit, for example, hypothesizes a scenario from the occurrence to the resolution of the incident based on physical evidence and testimony. The generation unit can also generate a reenactment video based on the hypothesis. The provision unit provides the information generated by the generation unit to investigators. The providing unit, for example, provides reenactment footage to investigators to help them grasp the overall picture of the case. The providing unit can also provide highly reliable verification information such as recommendations for missing evidence, correlations between people, and case hypotheses. This allows the case solving system according to the embodiment to achieve early resolution of cases and prevent false accusations.

[0030] The analysis unit can analyze investigative information such as physical evidence, eyewitness testimony, expert opinions, audio and video recordings, and confessions of crimes and accidents. The analysis unit analyzes, for example, physical evidence of crimes and accidents. Physical evidence includes, but is not limited to, fingerprints, DNA, and murder weapons. The analysis unit can also analyze eyewitness testimony. Eyewitness testimony is collected through recordings of testimony or written testimony. The analysis unit can also analyze expert opinions. Expert opinions include the opinions of forensic pathologists and psychologists' analyses. The analysis unit can also analyze audio and video recordings. Audio and video recordings include surveillance camera footage and telephone recordings. The analysis unit can also analyze confessions. Confessions include suspect confessions and depositions. This allows the analysis unit to improve the accuracy of its analysis of investigative information.

[0031] The generation unit may include a cross-check unit that cross-checks the evidence data based on the analysis results. The cross-check unit, for example, compares eyewitness testimony with video recordings to see if they match. The cross-check unit may also compare physical evidence with testimony to detect inconsistencies. Furthermore, the cross-check unit may compare audio recordings with video recordings to see if they match. In this way, the cross-check unit can improve the reliability of the evidence data.

[0032] The generation unit may include a hypothesis unit that formulates a case scenario hypothesis based on the analysis results. The hypothesis unit hypothesizes a scenario from the occurrence of the case to its resolution based on, for example, physical evidence and testimony. The hypothesis unit may also formulate multiple scenarios and select the most reliable one. Furthermore, the hypothesis unit may refer to past case data to improve the accuracy of the scenario hypothesis. This allows the hypothesis unit to improve the accuracy of the case scenario hypothesis.

[0033] The generation unit may include a reenactment unit that generates a reenactment video based on the results of the hypothesis unit. The reenactment unit generates the reenactment video using, for example, 3D modeling. The reenactment unit may also generate the reenactment video using animation. Furthermore, the reenactment unit may refer to past incident data to improve the accuracy of the reenactment video. This allows the reenactment unit to make it easier to grasp the overall picture of the incident.

[0034] The providing unit may include an analysis unit that provides information to accelerate the investigation based on the results of the reconstruction unit. The analysis unit may, for example, provide missing evidence recommendations. The analysis unit may also provide person correlations. Furthermore, the analysis unit may provide case hypotheses. This allows the analysis unit to accelerate the investigation.

[0035] The consideration unit can provide highly reliable verification information for missing evidence recommendations, person correlations, and case hypotheses. For example, the consideration unit can provide a list of additional evidence to be collected. The consideration unit can also provide a correlation diagram of the people involved. Furthermore, the consideration unit can provide multiple case hypotheses and select the most reliable one. This allows the consideration unit to provide highly reliable verification information, enabling the case to be resolved quickly.

[0036] The collection unit can analyze the urgency of the report content and select the optimal collection method. For example, if the urgency is high, the collection unit prioritizes audio analysis and immediately extracts important information. In addition, if the urgency is medium, the collection unit can analyze video recordings to grasp the situation at the scene. Furthermore, if the urgency is low, the collection unit can collect eyewitness testimony and expert opinions to obtain detailed information. This enables the collection unit to collect optimal information according to the urgency. The urgency analysis is performed, for example, based on keywords in the report content and the situation of the caller. Keyword analysis extracts words with high urgency contained in the report content and evaluates the urgency. The situation of the caller is evaluated by analyzing the caller's tone of voice, background sounds, etc., and evaluates the urgency. This allows the collection unit to select the optimal collection method according to the urgency.

[0037] The collection unit can determine the priority of the information to be collected based on the content of the report. For example, the collection unit collects information regarding the location of the incident as a first priority. The collection unit can also collect information regarding the time of the incident as a second priority. Furthermore, the collection unit can collect information regarding people involved in the incident last. This allows the collection unit to prioritize collecting important information. The priority of the information is determined based on, for example, the importance and urgency of the information. The importance of the information gives priority to information that is directly related to solving the incident. The urgency gives priority to information that is needed immediately after the incident occurs. This allows the collection unit to determine the priority of the information to be collected based on the content of the report.

[0038] The collection unit can perform speech analysis of the call content and extract important keywords to collect information. The collection unit, for example, extracts emergency keywords such as "help" and "fire" from the call content. The collection unit can also extract keywords related to the incident, such as "culprit" and "escape" from the call content. The collection unit can also extract basic information such as "location" and "time" from the call content. This allows the collection unit to extract important keywords and collect information quickly. The speech analysis is performed using, for example, a speech recognition algorithm or keyword extraction technology. The speech recognition algorithm converts the call content into text and extracts keywords. The keyword extraction technology extracts important words from the text and uses them to collect information. This allows the collection unit to perform speech analysis of the call content and extract important keywords to collect information.

[0039] The collection unit can prioritize collecting relevant information by taking into account the location information of the caller. For example, the collection unit prioritizes collecting information close to the crime scene from the location information of the caller. The collection unit can also prioritize collecting information about escape routes from the location information of the caller. Furthermore, the collection unit can prioritize collecting eyewitness testimony from the location information of the caller. This allows the collection unit to collect appropriate information based on the location information of the caller. Location information is acquired, for example, using GPS data or a location information service. GPS data is used to identify the current location of the caller and collect relevant information. Location information services acquire the location information of the caller in real time and use it to collect information. This allows the collection unit to prioritize collecting relevant information by taking into account the location information of the caller.

[0040] The collection unit can automatically translate the language of the report content and collect information in multiple languages. For example, if the report content is in a foreign language, the collection unit can automatically translate it to collect information. Furthermore, if the report content is provided in multiple languages, the collection unit can automatically translate each language to collect information. Furthermore, if the report content includes dialects or slang, the collection unit can translate it into standard Japanese to collect information. This enables the collection unit to collect information in multiple languages. The automatic translation is performed using, for example, a machine translation algorithm or technology to improve translation accuracy. The machine translation algorithm automatically translates the report content to help with information collection. The technology to improve translation accuracy supports multiple languages ​​and provides accurate translations. This enables the collection unit to automatically translate the language of the report content and collect information in multiple languages.

[0041] The collection unit can analyze background sounds in the report content and collect information for understanding the situation of the incident. For example, the collection unit analyzes emergency sounds, such as gunshots and screams, from the background sounds in the report content. The collection unit can also analyze moving sounds, such as car engine sounds and footsteps, from the background sounds in the report content. Furthermore, the collection unit can analyze environmental sounds (such as wind and rain) from the background sounds in the report content and identify the location of the incident. This allows the collection unit to understand the situation of the incident by analyzing the background sounds. The analysis of background sounds is performed using techniques such as voice filtering and sound source identification. Voice filtering extracts and analyzes background sounds from the report content. Sound source identification is used to identify the type of background sound and understand the situation of the incident. This allows the collection unit to analyze the background sounds in the report content and collect information for understanding the situation of the incident.

[0042] The analysis unit can evaluate the reliability of physical evidence and testimony and reflect this in the analysis results. For example, the analysis unit evaluates the reliability of physical evidence and prioritizes the analysis of highly reliable physical evidence. The analysis unit can also evaluate the reliability of testimony and prioritize the analysis of highly reliable testimony. Furthermore, the analysis unit can evaluate the degree of consistency between physical evidence and testimony and prioritize the analysis of matching information. This allows the analysis unit to obtain highly reliable analysis results. The evaluation of reliability is performed, for example, based on the degree of consistency of evidence and the consistency of testimony. The degree of consistency of evidence evaluates whether multiple pieces of evidence match. The consistency of testimony evaluates whether the testimony is consistent. This allows the analysis unit to evaluate the reliability of physical evidence and testimony and reflect this in the analysis results.

[0043] The analysis unit can improve the accuracy of the analysis by referring to data on similar past cases during analysis. The analysis unit, for example, refers to data on similar past cases to improve the accuracy of the analysis. The analysis unit can also analyze case patterns based on data on similar past cases. Furthermore, the analysis unit can evaluate the reliability of evidence based on data on similar past cases. In this way, the analysis unit can improve the accuracy of the analysis by referring to past data. The data on similar past cases is referenced using, for example, a case database or similarity evaluation criteria. The case database provides detailed data on past cases to aid in the analysis. The similarity evaluation criteria evaluate the similarity between past cases and the current case to aid in the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to data on similar past cases during analysis.

[0044] The analysis unit can integrate multiple information sources during analysis. The analysis unit integrates and analyzes multiple information sources, such as physical evidence, testimony, and video recordings. The analysis unit can also integrate and analyze expert opinions and eyewitness testimony. Furthermore, the analysis unit can also integrate and analyze audio recordings and video recordings. In this way, the analysis unit can improve the accuracy of the analysis by integrating multiple information sources. Information source integration is performed using, for example, database integration and information cross-referencing. Database integration integrates multiple databases to aid in the analysis. Information cross-referencing cross-references multiple information sources to aid in the analysis. In this way, the analysis unit can integrate and analyze multiple information sources during analysis.

[0045] The analysis unit can take the timeline of the incident into consideration when performing the analysis. The analysis unit, for example, evaluates the reliability of evidence based on the timeline of the incident. The analysis unit can also analyze eyewitness testimony based on the timeline of the incident. Furthermore, the analysis unit can analyze physical evidence based on the timeline of the incident. In this way, the analysis unit can improve the accuracy of the analysis by taking the timeline into consideration. The timeline is taken into consideration based on, for example, the time when the incident occurred and the elapsed time. The time when the incident occurred identifies the specific time when the incident occurred and is useful for the analysis. The elapsed time evaluates the passage of time since the incident occurred and is useful for the analysis. In this way, the analysis unit can take the timeline of the incident into consideration when performing the analysis.

[0046] The analysis unit can perform analysis while taking into account attribute information of the parties involved. For example, the analysis unit evaluates the reliability of evidence based on the attribute information of the parties involved. The analysis unit can also analyze eyewitness testimony based on the attribute information of the parties involved. Furthermore, the analysis unit can analyze physical evidence based on the attribute information of the parties involved. In this way, the analysis unit can improve the accuracy of analysis by taking into account the attribute information of the parties involved. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is useful for the analysis. The occupation identifies the occupation of the parties involved and is useful for the analysis. The relationship identifies the relationship between the parties involved and is useful for the analysis. In this way, the analysis unit can perform analysis while taking into account attribute information of the parties involved.

[0047] The generation unit can improve the accuracy of the scenario hypothesis by referring to past incident data during generation. The generation unit, for example, refers to past incident data to improve the accuracy of the scenario hypothesis. The generation unit can also analyze incident patterns based on past incident data. Furthermore, the generation unit can evaluate the reliability of evidence based on past incident data. In this way, the generation unit can improve the accuracy of the scenario hypothesis by referring to past data. The reference to past incident data is performed using, for example, an incident database or a similarity evaluation criterion. The incident database provides detailed data on past incidents and is useful for generating the scenario hypothesis. The similarity evaluation criterion evaluates the similarity between past incidents and the current incident and is useful for generating the scenario hypothesis. In this way, the generation unit can improve the accuracy of the scenario hypothesis by referring to past incident data during generation.

[0048] The generation unit can simultaneously generate and compare multiple hypotheses during generation. The generation unit, for example, simultaneously generates multiple scenario hypotheses and selects the most reliable hypothesis. The generation unit can also compare multiple scenario hypotheses and extract commonalities. The generation unit can also consider multiple scenario hypotheses and select the most appropriate hypothesis. In this way, the generation unit compares multiple hypotheses and selects the most reliable hypothesis. The generation of multiple hypotheses is performed based on, for example, hypothesis components and evaluation criteria. The hypothesis components include a scenario from the occurrence to the resolution of an incident. The evaluation criteria evaluate the reliability and consistency of the hypothesis. In this way, the generation unit can simultaneously generate and compare multiple hypotheses during generation.

[0049] The generation unit can generate a scenario by taking into account the behavior patterns of the participants during generation. The generation unit generates a scenario hypothesis based on, for example, the behavior patterns of the participants. The generation unit can also analyze the behavior patterns of the participants and generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the behavior patterns of the participants. In this way, the generation unit generates a highly reliable scenario by taking into account the behavior patterns of the participants. The behavior patterns are taken into account based on, for example, past behavior history and behavior consistency. The past behavior history identifies the past behavior of the participants and is used to generate a scenario hypothesis. The behavior consistency evaluates whether the behavior of the participants is consistent and is used to generate a scenario hypothesis. In this way, the generation unit can generate a scenario by taking into account the behavior patterns of the participants during generation.

[0050] The generation unit can generate a scenario by taking into account geographic information of the location where the incident occurred. The generation unit, for example, generates a scenario hypothesis based on the geographic information of the location where the incident occurred. The generation unit can also analyze the geographic information of the location where the incident occurred to generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the geographic information of the location where the incident occurred. In this way, the generation unit generates a highly reliable scenario by taking into account geographic information. The consideration of geographic information is based on, for example, GPS data and map information. GPS data identifies the location where the incident occurred and is useful for generating scenario hypotheses. Map information provides detailed geographic information of the location where the incident occurred and is useful for generating scenario hypotheses. In this way, the generation unit can generate a scenario by taking into account geographic information of the location where the incident occurred.

[0051] The generation unit can generate a scenario taking into account the timeline of the incident at the time of generation. The generation unit generates a scenario hypothesis based on, for example, the timeline of the incident. The generation unit can also analyze the timeline of the incident and generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the timeline of the incident. In this way, the generation unit generates a highly reliable scenario by taking the timeline into account. The timeline is taken into account based on, for example, the occurrence time of the incident and the elapsed time. The occurrence time of the incident identifies the specific time when the incident occurred and is useful for generating scenario hypotheses. The elapsed time evaluates the passage of time since the incident occurred and is useful for generating scenario hypotheses. In this way, the generation unit can generate a scenario taking into account the timeline of the incident at the time of generation.

[0052] The generation unit can generate a scenario by taking into account attribute information of stakeholders at the time of generation. The generation unit generates a scenario hypothesis based on, for example, the attribute information of stakeholders. The generation unit can also analyze the attribute information of stakeholders and generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the attribute information of stakeholders. In this way, the generation unit generates a highly reliable scenario by taking into account the attribute information of stakeholders. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the stakeholders and is useful for generating a scenario hypothesis. The occupation identifies the occupation of the stakeholders and is useful for generating a scenario hypothesis. The relationship identifies the relationship between the stakeholders and is useful for generating a scenario hypothesis. In this way, the generation unit can generate a scenario by taking into account attribute information of stakeholders at the time of generation.

[0053] The providing unit can provide information by reflecting past feedback from investigators when providing the information. The providing unit, for example, selects the optimal information provision method based on past feedback from investigators. The providing unit can also adjust the level of detail of the information by reflecting past feedback from investigators. Furthermore, the providing unit can adjust the timing of information provision by referring to past feedback from investigators. In this way, the providing unit can provide optimal information by reflecting past feedback. Feedback is reflected based on, for example, past evaluations and areas for improvement. Past evaluations are based on evaluations of information provided by investigators in the past. Areas for improvement are based on areas for improvement pointed out by investigators in the past. In this way, the providing unit can provide information by reflecting past feedback from investigators when providing the information.

[0054] The provision unit can adjust the level of detail of the information according to the investigator's level of expertise when providing the information. The provision unit adjusts the level of detail of the information according to, for example, the investigator's level of expertise. The provision unit can also select the optimal information provision method taking into account the investigator's level of expertise. Furthermore, the provision unit can determine the priority of information based on the investigator's level of expertise. This allows the provision unit to provide appropriate information according to the investigator's level of expertise. The evaluation of the expertise level is based on criteria such as beginner, intermediate, and expert. A beginner provides basic information. An intermediate provides detailed information. An expert provides specialized information. This allows the provision unit to adjust the level of detail of the information according to the investigator's level of expertise when providing the information.

[0055] The providing unit can determine the priority of information according to the investigator's current task at the time of providing the information. The providing unit determines the priority of information according to, for example, the investigator's current task. The providing unit can also select the optimal information provision method taking into account the investigator's current task. Furthermore, the providing unit can adjust the level of detail of the information based on the investigator's current task. This allows the providing unit to provide appropriate information according to the investigator's current task. The task evaluation is performed based on, for example, the current work content and urgency. The current work content identifies the work the investigator is currently performing and is used to provide information. The urgency evaluates the urgency the investigator is facing and is used to provide information. This allows the providing unit to determine the priority of information according to the investigator's current task at the time of providing the information.

[0056] The providing unit can select the optimal display method by taking into consideration the device information of the investigator when providing the information. For example, if the investigator is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the investigator is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the investigator is using a desktop, the providing unit can also provide a display method that includes detailed information. This allows the providing unit to provide an appropriate display method that matches the investigator's device. The device information is taken into consideration based on, for example, the device type, screen size, OS, etc. The device type identifies a smartphone, tablet, desktop, etc. and is used to determine the display method. The screen size provides a display method that matches the screen size of the device. The OS provides a display method that is compatible with the device's OS. This allows the providing unit to select the optimal display method by taking into consideration the device information of the investigator when providing the information.

[0057] The providing unit can make the information multilingual in accordance with the investigator's language setting when providing the information. The providing unit, for example, automatically translates the information based on the language setting of the investigator's device. The providing unit can also provide a language switching function when the investigator uses multiple languages. Furthermore, if the investigator selects a specific language, the providing unit can provide the information in that language. This enables the providing unit to provide information in multiple languages. The language setting is taken into consideration based on, for example, the user's language setting and the accuracy of the translation. The user's language setting identifies the language setting of the device and is used to provide the information. The accuracy of the translation supports multiple languages ​​and provides accurate translations. This allows the providing unit to make the information multilingual in accordance with the investigator's language setting when providing the information.

[0058] The providing unit can provide relevant information by taking into account the geographical location information of the investigator when providing the information. For example, the providing unit provides information about the crime scene based on the geographical location information of the investigator. The providing unit can also provide information about escape routes based on the geographical location information of the investigator. Furthermore, the providing unit can provide information about eyewitness testimony based on the geographical location information of the investigator. This allows the providing unit to provide appropriate information based on the geographical location information. The geographical location information is taken into account based on, for example, GPS data and map information. GPS data identifies the current location of the investigator and provides relevant information. Map information provides detailed geographical information of the current location of the investigator and is useful for providing information. This allows the providing unit to provide relevant information by taking into account the geographical location information of the investigator when providing the information.

[0059] The cross-check unit can improve accuracy by referring to data on similar past cases during a cross-check. The cross-check unit, for example, refers to data on similar past cases to improve the accuracy of the cross-check. The cross-check unit can also analyze case patterns based on data on similar past cases. Furthermore, the cross-check unit can evaluate the reliability of evidence based on data on similar past cases. In this way, the cross-check unit improves the accuracy of the cross-check by referring to past data. The data on similar past cases is referred to using, for example, a case database or similarity evaluation criteria. The case database provides detailed data on past cases to be useful in the cross-check. The similarity evaluation criteria evaluate the similarity between past cases and the current case to be useful in the cross-check. In this way, the cross-check unit can improve accuracy by referring to data on similar past cases during a cross-check.

[0060] The crosschecking unit can integrate and check multiple information sources when crosschecking. The crosschecking unit integrates and crosschecks multiple information sources, such as physical evidence, testimony, and video recordings. The crosschecking unit can also integrate and crosscheck expert opinions and eyewitness testimony. Furthermore, the crosschecking unit can integrate and crosscheck audio recordings and video recordings. In this way, the crosschecking unit can improve the accuracy of crosschecks by integrating multiple information sources. Information integration is performed using, for example, database integration and information cross-referencing. Database integration integrates multiple databases to be useful for crosschecks. Information cross-referencing mutually references multiple information sources to be useful for crosschecks. In this way, the crosschecking unit can integrate and check multiple information sources when crosschecking.

[0061] The cross-checking unit can evaluate the reliability of evidence during a cross-check and reflect the evaluation in the results. The cross-checking unit, for example, evaluates the reliability of physical evidence and prioritizes cross-checking highly reliable physical evidence. The cross-checking unit can also evaluate the reliability of testimony and prioritize cross-checking highly reliable testimony. Furthermore, the cross-checking unit can evaluate the degree of consistency between physical evidence and testimony and prioritize cross-checking matching information. This allows the cross-checking unit to obtain highly reliable cross-check results. The evaluation of reliability is performed, for example, based on the degree of consistency of evidence and the consistency of testimony. The degree of consistency of evidence is evaluated by evaluating whether multiple pieces of evidence match. The consistency of testimony is evaluated by evaluating whether the testimony is consistent. This allows the cross-checking unit to evaluate the reliability of evidence during a cross-check and reflect the evaluation in the results.

[0062] The crosscheck unit can take into consideration geographic information of the location of the incident when performing a crosscheck. The crosscheck unit, for example, evaluates the reliability of evidence based on geographic information of the location of the incident. The crosscheck unit can also crosscheck eyewitness testimony based on geographic information of the location of the incident. Furthermore, the crosscheck unit can crosscheck physical evidence based on geographic information of the location of the incident. In this way, the crosscheck unit improves the accuracy of the crosscheck by taking into consideration geographic information. The geographic information is taken into consideration based on, for example, GPS data and map information. GPS data identifies the location of the incident and is useful for the crosscheck. Map information provides detailed geographic information of the location of the incident and is useful for the crosscheck. In this way, the crosscheck unit can take into consideration geographic information of the location of the incident when performing a crosscheck.

[0063] The crosscheck unit can take the timeline of an incident into consideration when performing a crosscheck. The crosscheck unit, for example, evaluates the reliability of evidence based on the timeline of an incident. The crosscheck unit can also crosscheck eyewitness testimony based on the timeline of an incident. The crosscheck unit can also crosscheck physical evidence based on the timeline of an incident. In this way, the crosscheck unit can improve the accuracy of crosschecks by taking the timeline into consideration. The timeline is taken into consideration based on, for example, the time when an incident occurred and the elapsed time. The time when an incident occurred identifies the specific time when the incident occurred and is useful for crosschecks. The elapsed time evaluates the passage of time since the incident occurred and is useful for crosschecks. In this way, the crosscheck unit can take the timeline of an incident into consideration when performing a crosscheck.

[0064] The crosscheck unit can perform a check taking into consideration the attribute information of the parties involved when performing a crosscheck. The crosscheck unit, for example, evaluates the reliability of evidence based on the attribute information of the parties involved. The crosscheck unit can also crosscheck eyewitness testimony based on the attribute information of the parties involved. The crosscheck unit can also crosscheck physical evidence based on the attribute information of the parties involved. This allows the crosscheck unit to improve the accuracy of the crosscheck by taking into consideration the attribute information of the parties involved. The attribute information is taken into consideration based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is useful for the crosscheck. The occupation identifies the occupation of the parties involved and is useful for the crosscheck. The relationship identifies the relationship between the parties involved and is useful for the crosscheck. This allows the crosscheck unit to perform a check taking into consideration the attribute information of the parties involved when performing a crosscheck.

[0065] The hypothesis unit can improve the accuracy of a hypothesis by referring to past case data when formulating a hypothesis. The hypothesis unit, for example, refers to past case data to improve the accuracy of a hypothesis. The hypothesis unit can also analyze case patterns based on past case data. Furthermore, the hypothesis unit can evaluate the reliability of evidence based on past case data. In this way, the hypothesis unit improves the accuracy of a hypothesis by referring to past data. The reference to past case data is performed, for example, using a case database or a similarity evaluation criterion. The case database provides detailed data on past cases and is useful for formulating a hypothesis. The similarity evaluation criterion evaluates the similarity between past cases and the current case and is useful for formulating a hypothesis. In this way, the hypothesis unit can improve the accuracy of a hypothesis by referring to past case data when formulating a hypothesis.

[0066] The hypothesis unit can simultaneously formulate multiple hypotheses and compare and consider them when formulating a hypothesis. The hypothesis unit, for example, can simultaneously formulate multiple hypotheses and select the most reliable one. The hypothesis unit can also compare multiple hypotheses and extract commonalities. The hypothesis unit can also consider multiple hypotheses and select the most appropriate one. In this way, the hypothesis unit compares multiple hypotheses and selects the most reliable one. The formulation of multiple hypotheses is performed based on, for example, hypothesis components and evaluation criteria. The hypothesis components include a scenario from the occurrence of an incident to its resolution. The evaluation criteria evaluate the reliability and consistency of the hypothesis. In this way, the hypothesis unit can simultaneously formulate multiple hypotheses and compare and consider them when formulating a hypothesis.

[0067] The hypothesis unit can formulate a hypothesis by taking into account the behavioral patterns of the parties involved when formulating a hypothesis. The hypothesis unit formulates a hypothesis, for example, based on the behavioral patterns of the parties involved. The hypothesis unit can also analyze the behavioral patterns of the parties involved and formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account the behavioral patterns of the parties involved. In this way, the hypothesis unit formulates a highly reliable hypothesis by taking into account the behavioral patterns of the parties involved. The behavioral patterns are considered based on, for example, past behavioral history and behavioral consistency. The past behavioral history identifies the past behavior of the parties involved and is useful for formulating a hypothesis. The behavioral consistency evaluates whether the behavior of the parties involved is consistent and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate a hypothesis by taking into account the behavioral patterns of the parties involved when formulating a hypothesis.

[0068] When formulating a hypothesis, the hypothesis unit can formulate the hypothesis by taking into account geographic information of the location where the incident occurred. The hypothesis unit, for example, formulates a hypothesis based on geographic information of the location where the incident occurred. The hypothesis unit can also analyze the geographic information of the location where the incident occurred to formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account geographic information of the location where the incident occurred. In this way, the hypothesis unit can formulate a highly reliable hypothesis by taking into account geographic information. The geographic information is taken into account based on, for example, GPS data and map information. GPS data identifies the location where the incident occurred and is useful in formulating a hypothesis. Map information provides detailed geographic information of the location where the incident occurred and is useful in formulating a hypothesis. In this way, the hypothesis unit can formulate the hypothesis by taking into account geographic information of the location where the incident occurred.

[0069] The hypothesis unit can formulate a hypothesis by taking into account the timeline of the incident when formulating a hypothesis. The hypothesis unit formulates a hypothesis based on, for example, the timeline of the incident. The hypothesis unit can also analyze the timeline of the incident and formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account the timeline of the incident. In this way, the hypothesis unit can formulate a highly reliable hypothesis by taking the timeline into account. The timeline is considered based on, for example, the time when the incident occurred and the elapsed time. The time when the incident occurred identifies the specific time when the incident occurred and is useful for formulating a hypothesis. The elapsed time evaluates the passage of time since the incident occurred and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate a hypothesis by taking into account the timeline of the incident when formulating a hypothesis.

[0070] The hypothesis unit can formulate a hypothesis by taking into account attribute information of the parties involved when formulating a hypothesis. The hypothesis unit formulates a hypothesis based on, for example, the attribute information of the parties involved. The hypothesis unit can also analyze the attribute information of the parties involved and formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account the attribute information of the parties involved. In this way, the hypothesis unit formulates a highly reliable hypothesis by taking into account the attribute information of the parties involved. The attribute information is considered based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is useful for formulating a hypothesis. The occupation identifies the occupation of the parties involved and is useful for formulating a hypothesis. The relationship identifies the relationship between the parties involved and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate a hypothesis by taking into account attribute information of the parties involved when formulating a hypothesis.

[0071] The reenactment unit can improve accuracy by referring to past incident data when generating the reenactment video. For example, the reenactment unit can improve the accuracy of the reenactment video by referring to past incident data. The reenactment unit can also analyze incident patterns based on past incident data. Furthermore, the reenactment unit can evaluate the reliability of evidence based on past incident data. In this way, the reenactment unit can improve the accuracy of the reenactment video by referring to past data. The reference to past incident data is performed using, for example, an incident database or a similarity evaluation criterion. The incident database provides detailed data on past incidents and is useful for generating the reenactment video. The similarity evaluation criterion evaluates the similarity between past incidents and the current incident and is useful for generating the reenactment video. In this way, the reenactment unit can improve accuracy by referring to past incident data when generating the reenactment video.

[0072] The reenactment unit can simultaneously generate and compare multiple scenarios when generating the reenactment video. The reenactment unit, for example, simultaneously generates multiple scenarios and selects the most reliable scenario. The reenactment unit can also compare multiple scenarios and extract commonalities. The reenactment unit can also consider multiple scenarios and select the most appropriate scenario. In this way, the reenactment unit compares multiple scenarios and selects the most reliable scenario. The generation of multiple scenarios is performed based on, for example, scenario components and evaluation criteria. The scenario components include scenarios from the occurrence of an incident to its resolution. The evaluation criteria evaluate the reliability and consistency of the scenario. In this way, the reenactment unit can simultaneously generate and compare multiple scenarios when generating the reenactment video.

[0073] The reproducing unit can generate the re-enacted video by taking into account the behavioral patterns of the people involved. The re-enacted video is generated based on, for example, the behavioral patterns of the people involved. The re-enacted video can also be generated by analyzing the behavioral patterns of the people involved and generating the most reliable re-enacted video. The re-enacted video can also be generated by taking into account the behavioral patterns of the people involved and generating the most appropriate re-enacted video. In this way, the re-enacted video is generated by taking into account the behavioral patterns of the people involved. The behavioral patterns are taken into account based on, for example, past behavioral history and behavioral consistency. The past behavioral history is used to identify the past behavior of the people involved and is useful for generating the re-enacted video. The behavioral consistency is used to evaluate whether the behavior of the people involved is consistent and is useful for generating the re-enacted video. In this way, the re-enacted video can be generated by taking into account the behavioral patterns of the people involved.

[0074] The reenactment unit can generate the reenactment video by taking into account geographic information of the location where the incident occurred. For example, the reenactment unit generates the reenactment video based on geographic information of the location where the incident occurred. The reenactment unit can also analyze the geographic information of the location where the incident occurred to generate the most reliable reenactment video. Furthermore, the reenactment unit can generate the most appropriate reenactment video by taking into account the geographic information of the location where the incident occurred. In this way, the reenactment unit can generate a highly reliable reenactment video by taking into account geographic information. The geographic information can be taken into account based on, for example, GPS data or map information. GPS data identifies the location where the incident occurred and is useful for generating the reenactment video. Map information provides detailed geographic information of the location where the incident occurred and is useful for generating the reenactment video. In this way, the reenactment unit can generate the video by taking into account geographic information of the location where the incident occurred.

[0075] The reenactment unit can generate the reenactment video taking into account the timeline of the incident when generating the reenactment video. The reenactment unit generates the reenactment video based on, for example, the timeline of the incident. The reenactment unit can also analyze the timeline of the incident and generate the most reliable reenactment video. The reenactment unit can also generate the most appropriate reenactment video by taking the timeline of the incident into account. In this way, the reenactment unit can generate a highly reliable reenactment video by taking the timeline into account. The timeline is taken into account based on, for example, the time when the incident occurred and the elapsed time. The time when the incident occurred identifies the specific time when the incident occurred and is useful for generating the reenactment video. The elapsed time evaluates the passage of time since the incident occurred and is useful for generating the reenactment video. In this way, the reenactment unit can generate the video taking into account the timeline of the incident when generating the reenactment video.

[0076] The reproduction unit can generate a reproduced video by taking into account attribute information of the parties involved. For example, the reproduction unit generates a reproduced video based on the attribute information of the parties involved. The reproduction unit can also analyze the attribute information of the parties involved and generate the most reliable reproduced video. The reproduction unit can also generate the most appropriate reproduced video by taking into account the attribute information of the parties involved. In this way, the reproduction unit generates a highly reliable reproduced video by taking into account the attribute information of the parties involved. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is used to generate the reproduced video. The occupation identifies the occupation of the parties involved and is used to generate the reproduced video. The relationship identifies the relationship between the parties involved and is used to generate the reproduced video. In this way, the reproduction unit can generate a video by taking into account attribute information of the parties involved.

[0077] The consideration unit can improve the accuracy of the consideration by referring to past case data during consideration. The consideration unit, for example, refers to past case data to improve the accuracy of the consideration. The consideration unit can also analyze case patterns based on past case data. Furthermore, the consideration unit can evaluate the reliability of evidence based on past case data. In this way, the consideration unit can improve the accuracy of the consideration by referring to past data. The reference to past case data is performed, for example, using a case database or a similarity evaluation criterion. The case database provides detailed data on past cases to be useful in the consideration. The similarity evaluation criterion evaluates the similarity between past cases and the current case to be useful in the consideration. In this way, the consideration unit can improve the accuracy of the consideration by referring to past case data during consideration.

[0078] The consideration unit can consider and compare multiple hypotheses simultaneously during consideration. The consideration unit, for example, considers multiple hypotheses simultaneously and selects the most reliable hypothesis. The consideration unit can also compare multiple hypotheses and extract commonalities. The consideration unit can also consider multiple hypotheses and select the most appropriate hypothesis. In this way, the consideration unit selects the most reliable hypothesis by comparing multiple hypotheses. The consideration of multiple hypotheses is performed based on, for example, the components of a hypothesis and evaluation criteria. The components of a hypothesis include a scenario from the occurrence of an incident to its resolution. The evaluation criteria evaluate the reliability and consistency of a hypothesis. In this way, the consideration unit can consider and compare multiple hypotheses simultaneously during consideration.

[0079] The consideration unit can make considerations taking into account the behavioral patterns of the person involved during consideration. The consideration unit makes considerations, for example, based on the behavioral patterns of the person involved. The consideration unit can also analyze the behavioral patterns of the person involved and make the most reliable consideration. Furthermore, the consideration unit can make the most appropriate consideration by taking into account the behavioral patterns of the person involved. In this way, the consideration unit can make reliable considerations by taking into account the behavioral patterns of the person involved. Consideration of behavioral patterns is done, for example, based on past behavioral history and behavioral consistency. The past behavioral history identifies the past behavior of the person involved and is used for consideration. The behavioral consistency evaluates whether the behavior of the person involved is consistent and is used for consideration. In this way, the consideration unit can make considerations taking into account the behavioral patterns of the person involved during consideration.

[0080] The consideration unit can make considerations taking into account geographic information of the location where the incident occurred during consideration. The consideration unit, for example, makes considerations based on geographic information of the location where the incident occurred. The consideration unit can also analyze the geographic information of the location where the incident occurred and make the most reliable consideration. The consideration unit can also make the most appropriate consideration by taking into account the geographic information of the location where the incident occurred. In this way, the consideration unit can make a reliable consideration by taking into account geographic information. Consideration of geographic information is based on, for example, GPS data and map information. GPS data identifies the location where the incident occurred and is useful for the consideration. Map information provides detailed geographic information of the location where the incident occurred and is useful for the consideration. In this way, the consideration unit can make considerations taking into account geographic information of the location where the incident occurred during consideration.

[0081] The consideration unit can conduct its consideration taking into account the timeline of the incident when conducting its consideration. The consideration unit conducts its consideration based on, for example, the timeline of the incident. The consideration unit can also analyze the timeline of the incident and conduct the most reliable consideration. Furthermore, the consideration unit can conduct the most appropriate consideration by taking the timeline of the incident into account. In this way, the consideration unit can conduct a reliable consideration by taking the timeline into account. The timeline is considered based on, for example, the time when the incident occurred and the time that has elapsed. The time when the incident occurred identifies the specific time when the incident occurred and is useful for the consideration. The time that has elapsed evaluates the passage of time since the incident occurred and is useful for the consideration. In this way, the consideration unit can conduct its consideration taking into account the timeline of the incident when conducting its consideration.

[0082] The consideration unit can make considerations taking into account the attribute information of the related parties when making considerations. The consideration unit makes considerations, for example, based on the attribute information of the related parties. The consideration unit can also analyze the attribute information of the related parties and make the most reliable considerations. Furthermore, the consideration unit can make the most appropriate considerations by taking into account the attribute information of the related parties. In this way, the consideration unit can make a highly reliable consideration by taking into account the attribute information of the related parties. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the related parties and is useful for the consideration. The occupation identifies the occupation of the related parties and is useful for the consideration. The relationship identifies the relationship between the related parties and is useful for the consideration. In this way, the consideration unit can make considerations taking into account the attribute information of the related parties when making considerations.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The analysis unit can analyze the geographic information of the location of the incident based on the collected information and identify the location of the incident. For example, the analysis unit can use GPS data and map information to identify the location of the incident. The analysis unit can also identify the location of the incident based on eyewitness testimony and video recordings. Furthermore, the analysis unit can also identify the location of the incident based on audio recordings and background sounds. In this way, the analysis unit can improve the accuracy of the investigation by identifying the location of the incident. Analysis of geographic information is performed based on GPS data and map information. GPS data identifies the location of the incident and is useful for analysis. Map information provides detailed geographic information of the location of the incident and is useful for analysis. In this way, the analysis unit can analyze the geographic information of the location of the incident based on the collected information and identify the location of the incident.

[0085] The generation unit can formulate a scenario hypothesis based on the analysis results and taking into account the timeline of the incident. For example, the generation unit formulates a scenario hypothesis based on the occurrence time and elapsed time of the incident. The generation unit can also analyze the timeline of the incident and formulate the most reliable scenario hypothesis. The generation unit can also consider the timeline of the incident and formulate the most appropriate scenario hypothesis. In this way, the generation unit formulates a highly reliable scenario hypothesis by taking the timeline into account. The consideration of the timeline is based on the occurrence time and elapsed time of the incident. The occurrence time of the incident identifies the specific time when the incident occurred and is useful for formulating a scenario hypothesis. The elapsed time evaluates the passage of time since the incident occurred and is useful for formulating a scenario hypothesis. In this way, the generation unit can formulate a scenario hypothesis based on the analysis results and taking into account the timeline of the incident.

[0086] The collection unit can analyze the urgency of the report content and select the optimal collection method. For example, if the urgency is high, audio analysis can be prioritized to immediately extract important information. If the urgency is medium, video recordings can be analyzed to grasp the situation at the scene. Furthermore, if the urgency is low, eyewitness testimony and expert opinions can be collected to obtain detailed information. This allows the collection unit to collect optimal information according to the urgency. Urgency analysis is performed based on keywords in the report content and the situation of the caller. Keyword analysis extracts words with high urgency contained in the report content and evaluates the urgency. The situation of the caller is evaluated by analyzing the caller's tone of voice and background sounds, etc. This allows the collection unit to analyze the urgency of the report content and select the optimal collection method.

[0087] The generation unit can improve the accuracy of the scenario hypotheses by referring to past incident data during generation. For example, the generation unit can improve the accuracy of the scenario hypotheses by referring to past incident data. The generation unit can also analyze incident patterns based on past incident data. Furthermore, the generation unit can evaluate the reliability of evidence based on past incident data. In this way, the generation unit can improve the accuracy of the scenario hypotheses by referring to past data. The reference to past incident data is performed using an incident database and similarity evaluation criteria. The incident database provides detailed data on past incidents and is useful for generating scenario hypotheses. The similarity evaluation criteria evaluate the similarity between past incidents and the current incident and is useful for generating scenario hypotheses. In this way, the generation unit can improve the accuracy of the scenario hypotheses by referring to past incident data during generation.

[0088] The provision department can provide information by reflecting the investigator's past feedback when providing the information. For example, the provision department selects the optimal information provision method based on the investigator's past feedback. The provision department can also adjust the level of detail of the information by reflecting the investigator's past feedback. Furthermore, the provision department can adjust the timing of information provision by referring to the investigator's past feedback. In this way, the provision department can provide optimal information by reflecting past feedback. Feedback is reflected based on past evaluations and areas for improvement. Past evaluations are based on the investigator's evaluation of information provided in the past. Areas for improvement are based on areas for improvement pointed out by the investigator in the past. In this way, the provision department can provide information by reflecting the investigator's past feedback when providing the information.

[0089] When formulating a hypothesis, the hypothesis unit can formulate the hypothesis by taking into account the behavioral patterns of the person involved. For example, the hypothesis unit formulates a hypothesis based on the behavioral patterns of the person involved. The hypothesis unit can also analyze the behavioral patterns of the person involved and formulate the most reliable hypothesis. Furthermore, the hypothesis unit can formulate the most appropriate hypothesis by taking into account the behavioral patterns of the person involved. In this way, the hypothesis unit formulates a highly reliable hypothesis by taking into account the behavioral patterns of the person involved. The behavioral patterns are considered based on past behavioral history and behavioral consistency. The past behavioral history identifies the past behavior of the person involved and is useful for formulating a hypothesis. The behavioral consistency evaluates whether the behavior of the person involved is consistent and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate the hypothesis by taking into account the behavioral patterns of the person involved.

[0090] The processing flow of the first embodiment will be briefly explained below.

[0091] Step 1: The collection department monitors emergency calls and immediately begins collecting information. The department analyzes the call content in real time to understand the outline of the incident. It can also extract information from the call content, such as the location, time, and people involved. Step 2: The Analysis Department analyzes the information collected by the Collection Department to understand the outline of the case. The Analysis Department analyzes investigative information such as physical evidence of the incident or accident, eyewitness testimony, expert opinions, audio and video recordings, confessions, etc. It can also compare eyewitness testimony with video recordings to see if there is a match. Step 3: The generation unit formulates a hypothesis for the incident scenario based on the information analyzed by the analysis unit. The generation unit hypothesizes a scenario from the occurrence of the incident to its resolution based on physical evidence and testimony. It can also generate reenactment footage based on the hypothesis. Step 4: The information provided by the generation unit is provided to investigators. The provision unit provides reenactment footage to investigators, making it easier for them to grasp the overall picture of the case. It can also provide reliable verification information such as recommendations for missing evidence, correlations between people, and case hypotheses.

[0092] (Example 2) A system according to an embodiment of the present invention aims to resolve cases quickly and prevent false accusations. This system monitors emergency calls and immediately begins collecting information. It then analyzes the collected information to grasp the outline of the case. It then formulates a case scenario hypothesis based on the analyzed information and provides the resulting information to investigators. For example, the system analyzes the content of the call and extracts information such as the location, time, and parties involved in the incident. It then imports and analyzes investigative information, such as physical evidence of the incident or accident, eyewitness testimony, expert opinions, audio and video recordings, and confessions. It cross-checks the evidential data based on the analysis results and formulates a case scenario hypothesis. It then generates a reenactment video based on the hypothesis and provides information to accelerate the investigation. This enables the system to resolve cases quickly and prevent false accusations. For example, the system can compare eyewitness testimony with video recordings and, if there is a discrepancy, suggest the possibility of a false accusation. The system can also recommend missing evidence to prevent oversights in the investigation. This enables rapid and accurate investigations based on reliable verification information.

[0093] The case solving system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit monitors emergency calls and immediately begins collecting information. The collection unit, for example, analyzes the call content in real time to grasp an outline of the case. The collection unit can also extract information from the call content, such as the location, time, and parties involved in the incident. The analysis unit analyzes the information collected by the collection unit to grasp an outline of the case. The analysis unit analyzes investigative information, such as physical evidence of the incident or accident, eyewitness testimony, expert opinions, audio and video recordings, and confessions. The analysis unit can also compare eyewitness testimony with video recordings to confirm whether they match. The generation unit formulates a case scenario hypothesis based on the information analyzed by the analysis unit. The generation unit, for example, hypothesizes a scenario from the occurrence to the resolution of the incident based on physical evidence and testimony. The generation unit can also generate a reenactment video based on the hypothesis. The provision unit provides the information generated by the generation unit to investigators. The providing unit, for example, provides reenactment footage to investigators to help them grasp the overall picture of the case. The providing unit can also provide highly reliable verification information such as recommendations for missing evidence, correlations between people, and case hypotheses. This allows the case solving system according to the embodiment to achieve early resolution of cases and prevent false accusations.

[0094] The analysis unit can analyze investigative information such as physical evidence, eyewitness testimony, expert opinions, audio and video recordings, and confessions of crimes and accidents. The analysis unit analyzes, for example, physical evidence of crimes and accidents. Physical evidence includes, but is not limited to, fingerprints, DNA, and murder weapons. The analysis unit can also analyze eyewitness testimony. Eyewitness testimony is collected through recordings of testimony or written testimony. The analysis unit can also analyze expert opinions. Expert opinions include the opinions of forensic pathologists and psychologists' analyses. The analysis unit can also analyze audio and video recordings. Audio and video recordings include surveillance camera footage and telephone recordings. The analysis unit can also analyze confessions. Confessions include suspect confessions and depositions. This allows the analysis unit to improve the accuracy of its analysis of investigative information.

[0095] The generation unit may include a cross-check unit that cross-checks the evidence data based on the analysis results. The cross-check unit, for example, compares eyewitness testimony with video recordings to see if they match. The cross-check unit may also compare physical evidence with testimony to detect inconsistencies. Furthermore, the cross-check unit may compare audio recordings with video recordings to see if they match. In this way, the cross-check unit can improve the reliability of the evidence data.

[0096] The generation unit may include a hypothesis unit that formulates a case scenario hypothesis based on the analysis results. The hypothesis unit hypothesizes a scenario from the occurrence of the case to its resolution based on, for example, physical evidence and testimony. The hypothesis unit may also formulate multiple scenarios and select the most reliable one. Furthermore, the hypothesis unit may refer to past case data to improve the accuracy of the scenario hypothesis. This allows the hypothesis unit to improve the accuracy of the case scenario hypothesis.

[0097] The generation unit may include a reenactment unit that generates a reenactment video based on the results of the hypothesis unit. The reenactment unit generates the reenactment video using, for example, 3D modeling. The reenactment unit may also generate the reenactment video using animation. Furthermore, the reenactment unit may refer to past incident data to improve the accuracy of the reenactment video. This allows the reenactment unit to make it easier to grasp the overall picture of the incident.

[0098] The providing unit may include an analysis unit that provides information to accelerate the investigation based on the results of the reconstruction unit. The analysis unit may, for example, provide missing evidence recommendations. The analysis unit may also provide person correlations. Furthermore, the analysis unit may provide case hypotheses. This allows the analysis unit to accelerate the investigation.

[0099] The consideration unit can provide highly reliable verification information for missing evidence recommendations, person correlations, and case hypotheses. For example, the consideration unit can provide a list of additional evidence to be collected. The consideration unit can also provide a correlation diagram of the people involved. Furthermore, the consideration unit can provide multiple case hypotheses and select the most reliable one. This allows the consideration unit to provide highly reliable verification information, enabling the case to be resolved quickly.

[0100] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is nervous, the collection unit can immediately start collecting information and take a quick response. Also, if the user is calm, the collection unit can take a little time to collect detailed information. Furthermore, if the user is confused, the collection unit can first collect basic information and then collect detailed information. This allows the collection unit to collect information at an appropriate timing depending on the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the collection unit to adjust the timing of information collection based on the user's emotions.

[0101] The collection unit can analyze the urgency of the report content and select the optimal collection method. For example, if the urgency is high, the collection unit prioritizes audio analysis and immediately extracts important information. In addition, if the urgency is medium, the collection unit can analyze video recordings to grasp the situation at the scene. Furthermore, if the urgency is low, the collection unit can collect eyewitness testimony and expert opinions to obtain detailed information. This enables the collection unit to collect optimal information according to the urgency. The urgency analysis is performed, for example, based on keywords in the report content and the situation of the caller. Keyword analysis extracts words with high urgency contained in the report content and evaluates the urgency. The situation of the caller is evaluated by analyzing the caller's tone of voice, background sounds, etc., and evaluates the urgency. This allows the collection unit to select the optimal collection method according to the urgency.

[0102] The collection unit can determine the priority of the information to be collected based on the content of the report. For example, the collection unit collects information regarding the location of the incident as a first priority. The collection unit can also collect information regarding the time of the incident as a second priority. Furthermore, the collection unit can collect information regarding people involved in the incident last. This allows the collection unit to prioritize collecting important information. The priority of the information is determined based on, for example, the importance and urgency of the information. The importance of the information gives priority to information that is directly related to solving the incident. The urgency gives priority to information that is needed immediately after the incident occurs. This allows the collection unit to determine the priority of the information to be collected based on the content of the report.

[0103] The collection unit can perform speech analysis of the call content and extract important keywords to collect information. The collection unit, for example, extracts emergency keywords such as "help" and "fire" from the call content. The collection unit can also extract keywords related to the incident, such as "culprit" and "escape" from the call content. The collection unit can also extract basic information such as "location" and "time" from the call content. This allows the collection unit to extract important keywords and collect information quickly. The speech analysis is performed using, for example, a speech recognition algorithm or keyword extraction technology. The speech recognition algorithm converts the call content into text and extracts keywords. The keyword extraction technology extracts important words from the text and uses them to collect information. This allows the collection unit to perform speech analysis of the call content and extract important keywords to collect information.

[0104] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is nervous, the collection unit can collect information about the location of the incident as a first priority. Furthermore, if the user is calm, the collection unit can collect information about the time of the incident as a second priority. Furthermore, if the user is confused, the collection unit can collect information about people involved in the incident last. This allows the collection unit to collect appropriate information according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the collection unit to determine the priority of information to be collected based on the user's emotions.

[0105] The collection unit can prioritize collecting relevant information by taking into account the location information of the caller. For example, the collection unit prioritizes collecting information close to the crime scene from the location information of the caller. The collection unit can also prioritize collecting information about escape routes from the location information of the caller. Furthermore, the collection unit can prioritize collecting eyewitness testimony from the location information of the caller. This allows the collection unit to collect appropriate information based on the location information of the caller. Location information is acquired, for example, using GPS data or a location information service. GPS data is used to identify the current location of the caller and collect relevant information. Location information services acquire the location information of the caller in real time and use it to collect information. This allows the collection unit to prioritize collecting relevant information by taking into account the location information of the caller.

[0106] The collection unit can automatically translate the language of the report content and collect information in multiple languages. For example, if the report content is in a foreign language, the collection unit can automatically translate it to collect information. Furthermore, if the report content is provided in multiple languages, the collection unit can automatically translate each language to collect information. Furthermore, if the report content includes dialects or slang, the collection unit can translate it into standard Japanese to collect information. This enables the collection unit to collect information in multiple languages. The automatic translation is performed using, for example, a machine translation algorithm or technology to improve translation accuracy. The machine translation algorithm automatically translates the report content to help with information collection. The technology to improve translation accuracy supports multiple languages ​​and provides accurate translations. This enables the collection unit to automatically translate the language of the report content and collect information in multiple languages.

[0107] The collection unit can analyze background sounds in the report content and collect information for understanding the situation of the incident. For example, the collection unit analyzes emergency sounds, such as gunshots and screams, from the background sounds in the report content. The collection unit can also analyze moving sounds, such as car engine sounds and footsteps, from the background sounds in the report content. Furthermore, the collection unit can analyze environmental sounds (such as wind and rain) from the background sounds in the report content and identify the location of the incident. This allows the collection unit to understand the situation of the incident by analyzing the background sounds. The analysis of background sounds is performed using techniques such as voice filtering and sound source identification. Voice filtering extracts and analyzes background sounds from the report content. Sound source identification is used to identify the type of background sound and understand the situation of the incident. This allows the collection unit to analyze the background sounds in the report content and collect information for understanding the situation of the incident.

[0108] The analysis unit can estimate the user's emotions and adjust the priority of analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit prioritizes analysis of information with a high level of urgency. Furthermore, if the user is calm, the analysis unit can prioritize analysis of detailed information. Furthermore, if the user is confused, the analysis unit can prioritize analysis of basic information. This allows the analysis unit to perform appropriate analysis according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the analysis unit to adjust the priority of analysis based on the user's emotions.

[0109] The analysis unit can evaluate the reliability of physical evidence and testimony and reflect this in the analysis results. For example, the analysis unit evaluates the reliability of physical evidence and prioritizes the analysis of highly reliable physical evidence. The analysis unit can also evaluate the reliability of testimony and prioritize the analysis of highly reliable testimony. Furthermore, the analysis unit can evaluate the degree of consistency between physical evidence and testimony and prioritize the analysis of matching information. This allows the analysis unit to obtain highly reliable analysis results. The evaluation of reliability is performed, for example, based on the degree of consistency of evidence and the consistency of testimony. The degree of consistency of evidence evaluates whether multiple pieces of evidence match. The consistency of testimony evaluates whether the testimony is consistent. This allows the analysis unit to evaluate the reliability of physical evidence and testimony and reflect this in the analysis results.

[0110] The analysis unit can improve the accuracy of the analysis by referring to data on similar past cases during analysis. The analysis unit, for example, refers to data on similar past cases to improve the accuracy of the analysis. The analysis unit can also analyze case patterns based on data on similar past cases. Furthermore, the analysis unit can evaluate the reliability of evidence based on data on similar past cases. In this way, the analysis unit can improve the accuracy of the analysis by referring to past data. The data on similar past cases is referenced using, for example, a case database or similarity evaluation criteria. The case database provides detailed data on past cases to aid in the analysis. The similarity evaluation criteria evaluate the similarity between past cases and the current case to aid in the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to data on similar past cases during analysis.

[0111] The analysis unit can integrate multiple information sources during analysis. The analysis unit integrates and analyzes multiple information sources, such as physical evidence, testimony, and video recordings. The analysis unit can also integrate and analyze expert opinions and eyewitness testimony. Furthermore, the analysis unit can also integrate and analyze audio recordings and video recordings. In this way, the analysis unit can improve the accuracy of the analysis by integrating multiple information sources. Information source integration is performed using, for example, database integration and information cross-referencing. Database integration integrates multiple databases to aid in the analysis. Information cross-referencing cross-references multiple information sources to aid in the analysis. In this way, the analysis unit can integrate and analyze multiple information sources during analysis.

[0112] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is calm, the analysis unit can also provide a display method including detailed information. Furthermore, if the user is confused, the analysis unit can also provide a display method that focuses on the main points. This allows the analysis unit to provide an appropriate display method according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions.

[0113] The analysis unit can take the timeline of the incident into consideration when performing the analysis. The analysis unit, for example, evaluates the reliability of evidence based on the timeline of the incident. The analysis unit can also analyze eyewitness testimony based on the timeline of the incident. Furthermore, the analysis unit can analyze physical evidence based on the timeline of the incident. In this way, the analysis unit can improve the accuracy of the analysis by taking the timeline into consideration. The timeline is taken into consideration based on, for example, the time when the incident occurred and the elapsed time. The time when the incident occurred identifies the specific time when the incident occurred and is useful for the analysis. The elapsed time evaluates the passage of time since the incident occurred and is useful for the analysis. In this way, the analysis unit can take the timeline of the incident into consideration when performing the analysis.

[0114] The analysis unit can perform analysis while taking into account attribute information of the parties involved. For example, the analysis unit evaluates the reliability of evidence based on the attribute information of the parties involved. The analysis unit can also analyze eyewitness testimony based on the attribute information of the parties involved. Furthermore, the analysis unit can analyze physical evidence based on the attribute information of the parties involved. In this way, the analysis unit can improve the accuracy of analysis by taking into account the attribute information of the parties involved. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is useful for the analysis. The occupation identifies the occupation of the parties involved and is useful for the analysis. The relationship identifies the relationship between the parties involved and is useful for the analysis. In this way, the analysis unit can perform analysis while taking into account attribute information of the parties involved.

[0115] The generation unit can estimate the user's emotions and adjust the method for generating scenario hypotheses based on the estimated user's emotions. For example, if the user is nervous, the generation unit generates a simple and highly visible scenario hypothesis. Furthermore, if the user is calm, the generation unit can generate a scenario hypothesis that includes detailed information. Furthermore, if the user is confused, the generation unit can generate a scenario hypothesis that covers the main points. This allows the generation unit to generate an appropriate scenario hypothesis according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the generation unit to adjust the method for generating scenario hypotheses based on the user's emotions.

[0116] The generation unit can improve the accuracy of the scenario hypothesis by referring to past incident data during generation. The generation unit, for example, refers to past incident data to improve the accuracy of the scenario hypothesis. The generation unit can also analyze incident patterns based on past incident data. Furthermore, the generation unit can evaluate the reliability of evidence based on past incident data. In this way, the generation unit can improve the accuracy of the scenario hypothesis by referring to past data. The reference to past incident data is performed using, for example, an incident database or a similarity evaluation criterion. The incident database provides detailed data on past incidents and is useful for generating the scenario hypothesis. The similarity evaluation criterion evaluates the similarity between past incidents and the current incident and is useful for generating the scenario hypothesis. In this way, the generation unit can improve the accuracy of the scenario hypothesis by referring to past incident data during generation.

[0117] The generation unit can simultaneously generate and compare multiple hypotheses during generation. The generation unit, for example, simultaneously generates multiple scenario hypotheses and selects the most reliable hypothesis. The generation unit can also compare multiple scenario hypotheses and extract commonalities. The generation unit can also consider multiple scenario hypotheses and select the most appropriate hypothesis. In this way, the generation unit compares multiple hypotheses and selects the most reliable hypothesis. The generation of multiple hypotheses is performed based on, for example, hypothesis components and evaluation criteria. The hypothesis components include a scenario from the occurrence to the resolution of an incident. The evaluation criteria evaluate the reliability and consistency of the hypothesis. In this way, the generation unit can simultaneously generate and compare multiple hypotheses during generation.

[0118] The generation unit can generate a scenario by taking into account the behavior patterns of the participants during generation. The generation unit generates a scenario hypothesis based on, for example, the behavior patterns of the participants. The generation unit can also analyze the behavior patterns of the participants and generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the behavior patterns of the participants. In this way, the generation unit generates a highly reliable scenario by taking into account the behavior patterns of the participants. The behavior patterns are taken into account based on, for example, past behavior history and behavior consistency. The past behavior history identifies the past behavior of the participants and is used to generate a scenario hypothesis. The behavior consistency evaluates whether the behavior of the participants is consistent and is used to generate a scenario hypothesis. In this way, the generation unit can generate a scenario by taking into account the behavior patterns of the participants during generation.

[0119] The generation unit can estimate the user's emotions and adjust the display method of the scenario hypotheses based on the estimated user emotions. For example, when the user is nervous, the generation unit provides a simple, highly visible display method. Furthermore, when the user is calm, the generation unit can provide a display method including detailed information. Furthermore, when the user is confused, the generation unit can provide a display method that focuses on the main points. In this way, the generation unit can provide an appropriate display method according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. In this way, the generation unit can adjust the display method of the scenario hypotheses based on the user's emotions.

[0120] The generation unit can generate a scenario by taking into account geographic information of the location where the incident occurred. The generation unit, for example, generates a scenario hypothesis based on the geographic information of the location where the incident occurred. The generation unit can also analyze the geographic information of the location where the incident occurred to generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the geographic information of the location where the incident occurred. In this way, the generation unit generates a highly reliable scenario by taking into account geographic information. The consideration of geographic information is based on, for example, GPS data and map information. GPS data identifies the location where the incident occurred and is useful for generating scenario hypotheses. Map information provides detailed geographic information of the location where the incident occurred and is useful for generating scenario hypotheses. In this way, the generation unit can generate a scenario by taking into account geographic information of the location where the incident occurred.

[0121] The generation unit can generate a scenario taking into account the timeline of the incident at the time of generation. The generation unit generates a scenario hypothesis based on, for example, the timeline of the incident. The generation unit can also analyze the timeline of the incident and generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the timeline of the incident. In this way, the generation unit generates a highly reliable scenario by taking the timeline into account. The timeline is taken into account based on, for example, the occurrence time of the incident and the elapsed time. The occurrence time of the incident identifies the specific time when the incident occurred and is useful for generating scenario hypotheses. The elapsed time evaluates the passage of time since the incident occurred and is useful for generating scenario hypotheses. In this way, the generation unit can generate a scenario taking into account the timeline of the incident at the time of generation.

[0122] The generation unit can generate a scenario by taking into account attribute information of stakeholders at the time of generation. The generation unit generates a scenario hypothesis based on, for example, the attribute information of stakeholders. The generation unit can also analyze the attribute information of stakeholders and generate the most reliable scenario hypothesis. The generation unit can also generate the most appropriate scenario hypothesis by taking into account the attribute information of stakeholders. In this way, the generation unit generates a highly reliable scenario by taking into account the attribute information of stakeholders. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the stakeholders and is useful for generating a scenario hypothesis. The occupation identifies the occupation of the stakeholders and is useful for generating a scenario hypothesis. The relationship identifies the relationship between the stakeholders and is useful for generating a scenario hypothesis. In this way, the generation unit can generate a scenario by taking into account attribute information of stakeholders at the time of generation.

[0123] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, when the user is nervous, the providing unit can provide a simple, highly visible information providing method. Furthermore, when the user is calm, the providing unit can also provide an information providing method that includes detailed information. Furthermore, when the user is confused, the providing unit can also provide an information providing method that focuses on the main points. This allows the providing unit to provide appropriate information according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the providing unit to adjust the method of providing information based on the user's emotions.

[0124] The providing unit can provide information by reflecting past feedback from investigators when providing the information. The providing unit, for example, selects the optimal information provision method based on past feedback from investigators. The providing unit can also adjust the level of detail of the information by reflecting past feedback from investigators. Furthermore, the providing unit can adjust the timing of information provision by referring to past feedback from investigators. In this way, the providing unit can provide optimal information by reflecting past feedback. Feedback is reflected based on, for example, past evaluations and areas for improvement. Past evaluations are based on evaluations of information provided by investigators in the past. Areas for improvement are based on areas for improvement pointed out by investigators in the past. In this way, the providing unit can provide information by reflecting past feedback from investigators when providing the information.

[0125] The provision unit can adjust the level of detail of the information according to the investigator's level of expertise when providing the information. The provision unit adjusts the level of detail of the information according to, for example, the investigator's level of expertise. The provision unit can also select the optimal information provision method taking into account the investigator's level of expertise. Furthermore, the provision unit can determine the priority of information based on the investigator's level of expertise. This allows the provision unit to provide appropriate information according to the investigator's level of expertise. The evaluation of the expertise level is based on criteria such as beginner, intermediate, and expert. A beginner provides basic information. An intermediate provides detailed information. An expert provides specialized information. This allows the provision unit to adjust the level of detail of the information according to the investigator's level of expertise when providing the information.

[0126] The providing unit can determine the priority of information according to the investigator's current task at the time of providing the information. The providing unit determines the priority of information according to, for example, the investigator's current task. The providing unit can also select the optimal information provision method taking into account the investigator's current task. Furthermore, the providing unit can adjust the level of detail of the information based on the investigator's current task. This allows the providing unit to provide appropriate information according to the investigator's current task. The task evaluation is performed based on, for example, the current work content and urgency. The current work content identifies the work the investigator is currently performing and is used to provide information. The urgency evaluates the urgency the investigator is facing and is used to provide information. This allows the providing unit to determine the priority of information according to the investigator's current task at the time of providing the information.

[0127] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide information immediately. Furthermore, if the user is calm, the providing unit can take a little time to provide detailed information. Furthermore, if the user is confused, the providing unit can provide basic information and then provide detailed information. This allows the providing unit to provide information at an appropriate timing according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the providing unit to adjust the timing of providing information based on the user's emotions.

[0128] The providing unit can select the optimal display method by taking into consideration the device information of the investigator when providing the information. For example, if the investigator is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the investigator is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the investigator is using a desktop, the providing unit can also provide a display method that includes detailed information. This allows the providing unit to provide an appropriate display method that matches the investigator's device. The device information is taken into consideration based on, for example, the device type, screen size, OS, etc. The device type identifies a smartphone, tablet, desktop, etc. and is used to determine the display method. The screen size provides a display method that matches the screen size of the device. The OS provides a display method that is compatible with the device's OS. This allows the providing unit to select the optimal display method by taking into consideration the device information of the investigator when providing the information.

[0129] The providing unit can make the information multilingual in accordance with the investigator's language setting when providing the information. The providing unit, for example, automatically translates the information based on the language setting of the investigator's device. The providing unit can also provide a language switching function when the investigator uses multiple languages. Furthermore, if the investigator selects a specific language, the providing unit can provide the information in that language. This enables the providing unit to provide information in multiple languages. The language setting is taken into consideration based on, for example, the user's language setting and the accuracy of the translation. The user's language setting identifies the language setting of the device and is used to provide the information. The accuracy of the translation supports multiple languages ​​and provides accurate translations. This allows the providing unit to make the information multilingual in accordance with the investigator's language setting when providing the information.

[0130] The providing unit can provide relevant information by taking into account the geographical location information of the investigator when providing the information. For example, the providing unit provides information about the crime scene based on the geographical location information of the investigator. The providing unit can also provide information about escape routes based on the geographical location information of the investigator. Furthermore, the providing unit can provide information about eyewitness testimony based on the geographical location information of the investigator. This allows the providing unit to provide appropriate information based on the geographical location information. The geographical location information is taken into account based on, for example, GPS data and map information. GPS data identifies the current location of the investigator and provides relevant information. Map information provides detailed geographical information of the current location of the investigator and is useful for providing information. This allows the providing unit to provide relevant information by taking into account the geographical location information of the investigator when providing the information.

[0131] The crosscheck unit can estimate the user's emotions and adjust the priority of the crosscheck based on the estimated user's emotions. For example, when the user is nervous, the crosscheck unit prioritizes crosschecking of information with a high level of urgency. Furthermore, when the user is calm, the crosscheck unit can prioritize crosschecking of detailed information. Furthermore, when the user is confused, the crosscheck unit can prioritize crosschecking of basic information. This allows the crosscheck unit to perform an appropriate crosscheck according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the crosscheck unit to adjust the priority of the crosscheck based on the user's emotions.

[0132] The cross-check unit can improve accuracy by referring to data on similar past cases during a cross-check. The cross-check unit, for example, refers to data on similar past cases to improve the accuracy of the cross-check. The cross-check unit can also analyze case patterns based on data on similar past cases. Furthermore, the cross-check unit can evaluate the reliability of evidence based on data on similar past cases. In this way, the cross-check unit improves the accuracy of the cross-check by referring to past data. The data on similar past cases is referred to using, for example, a case database or similarity evaluation criteria. The case database provides detailed data on past cases to be useful in the cross-check. The similarity evaluation criteria evaluate the similarity between past cases and the current case to be useful in the cross-check. In this way, the cross-check unit can improve accuracy by referring to data on similar past cases during a cross-check.

[0133] The crosschecking unit can integrate and check multiple information sources when crosschecking. The crosschecking unit integrates and crosschecks multiple information sources, such as physical evidence, testimony, and video recordings. The crosschecking unit can also integrate and crosscheck expert opinions and eyewitness testimony. Furthermore, the crosschecking unit can integrate and crosscheck audio recordings and video recordings. In this way, the crosschecking unit can improve the accuracy of crosschecks by integrating multiple information sources. Information integration is performed using, for example, database integration and information cross-referencing. Database integration integrates multiple databases to be useful for crosschecks. Information cross-referencing mutually references multiple information sources to be useful for crosschecks. In this way, the crosschecking unit can integrate and check multiple information sources when crosschecking.

[0134] The cross-checking unit can evaluate the reliability of evidence during a cross-check and reflect the evaluation in the results. The cross-checking unit, for example, evaluates the reliability of physical evidence and prioritizes cross-checking highly reliable physical evidence. The cross-checking unit can also evaluate the reliability of testimony and prioritize cross-checking highly reliable testimony. Furthermore, the cross-checking unit can evaluate the degree of consistency between physical evidence and testimony and prioritize cross-checking matching information. This allows the cross-checking unit to obtain highly reliable cross-check results. The evaluation of reliability is performed, for example, based on the degree of consistency of evidence and the consistency of testimony. The degree of consistency of evidence is evaluated by evaluating whether multiple pieces of evidence match. The consistency of testimony is evaluated by evaluating whether the testimony is consistent. This allows the cross-checking unit to evaluate the reliability of evidence during a cross-check and reflect the evaluation in the results.

[0135] The cross-check unit can estimate the user's emotions and adjust the order in which the cross-check results are displayed based on the estimated user's emotions. For example, if the user is nervous, the cross-check unit can display the results in a simple, highly visible order. If the user is calm, the cross-check unit can also display the results in an order that includes detailed information. Furthermore, if the user is confused, the cross-check unit can display the results in an order that highlights the main points. In this way, the cross-check unit displays the cross-check results in an appropriate order according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. In this way, the cross-check unit can adjust the order in which the cross-check results are displayed based on the user's emotions.

[0136] The crosscheck unit can take into consideration geographic information of the location of the incident when performing a crosscheck. The crosscheck unit, for example, evaluates the reliability of evidence based on geographic information of the location of the incident. The crosscheck unit can also crosscheck eyewitness testimony based on geographic information of the location of the incident. Furthermore, the crosscheck unit can crosscheck physical evidence based on geographic information of the location of the incident. In this way, the crosscheck unit improves the accuracy of the crosscheck by taking into consideration geographic information. The geographic information is taken into consideration based on, for example, GPS data and map information. GPS data identifies the location of the incident and is useful for the crosscheck. Map information provides detailed geographic information of the location of the incident and is useful for the crosscheck. In this way, the crosscheck unit can take into consideration geographic information of the location of the incident when performing a crosscheck.

[0137] The crosscheck unit can take the timeline of an incident into consideration when performing a crosscheck. The crosscheck unit, for example, evaluates the reliability of evidence based on the timeline of an incident. The crosscheck unit can also crosscheck eyewitness testimony based on the timeline of an incident. The crosscheck unit can also crosscheck physical evidence based on the timeline of an incident. In this way, the crosscheck unit can improve the accuracy of crosschecks by taking the timeline into consideration. The timeline is taken into consideration based on, for example, the time when an incident occurred and the elapsed time. The time when an incident occurred identifies the specific time when the incident occurred and is useful for crosschecks. The elapsed time evaluates the passage of time since the incident occurred and is useful for crosschecks. In this way, the crosscheck unit can take the timeline of an incident into consideration when performing a crosscheck.

[0138] The crosscheck unit can perform a check taking into consideration the attribute information of the parties involved when performing a crosscheck. The crosscheck unit, for example, evaluates the reliability of evidence based on the attribute information of the parties involved. The crosscheck unit can also crosscheck eyewitness testimony based on the attribute information of the parties involved. The crosscheck unit can also crosscheck physical evidence based on the attribute information of the parties involved. This allows the crosscheck unit to improve the accuracy of the crosscheck by taking into consideration the attribute information of the parties involved. The attribute information is taken into consideration based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is useful for the crosscheck. The occupation identifies the occupation of the parties involved and is useful for the crosscheck. The relationship identifies the relationship between the parties involved and is useful for the crosscheck. This allows the crosscheck unit to perform a check taking into consideration the attribute information of the parties involved when performing a crosscheck.

[0139] The hypothesis unit can estimate the user's emotions and adjust the method for formulating hypotheses based on the estimated user's emotions. For example, if the user is nervous, the hypothesis unit can formulate a simple and highly visible hypothesis. If the user is calm, the hypothesis unit can also formulate a hypothesis that includes detailed information. Furthermore, if the user is confused, the hypothesis unit can also formulate a hypothesis that covers the main points. This allows the hypothesis unit to formulate an appropriate hypothesis according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the hypothesis unit to adjust the method for formulating hypotheses based on the user's emotions.

[0140] The hypothesis unit can improve the accuracy of a hypothesis by referring to past case data when formulating a hypothesis. The hypothesis unit, for example, refers to past case data to improve the accuracy of a hypothesis. The hypothesis unit can also analyze case patterns based on past case data. Furthermore, the hypothesis unit can evaluate the reliability of evidence based on past case data. In this way, the hypothesis unit improves the accuracy of a hypothesis by referring to past data. The reference to past case data is performed, for example, using a case database or a similarity evaluation criterion. The case database provides detailed data on past cases and is useful for formulating a hypothesis. The similarity evaluation criterion evaluates the similarity between past cases and the current case and is useful for formulating a hypothesis. In this way, the hypothesis unit can improve the accuracy of a hypothesis by referring to past case data when formulating a hypothesis.

[0141] The hypothesis unit can simultaneously formulate multiple hypotheses and compare and consider them when formulating a hypothesis. The hypothesis unit, for example, can simultaneously formulate multiple hypotheses and select the most reliable one. The hypothesis unit can also compare multiple hypotheses and extract commonalities. The hypothesis unit can also consider multiple hypotheses and select the most appropriate one. In this way, the hypothesis unit compares multiple hypotheses and selects the most reliable one. The formulation of multiple hypotheses is performed based on, for example, hypothesis components and evaluation criteria. The hypothesis components include a scenario from the occurrence of an incident to its resolution. The evaluation criteria evaluate the reliability and consistency of the hypothesis. In this way, the hypothesis unit can simultaneously formulate multiple hypotheses and compare and consider them when formulating a hypothesis.

[0142] The hypothesis unit can formulate a hypothesis by taking into account the behavioral patterns of the parties involved when formulating a hypothesis. The hypothesis unit formulates a hypothesis, for example, based on the behavioral patterns of the parties involved. The hypothesis unit can also analyze the behavioral patterns of the parties involved and formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account the behavioral patterns of the parties involved. In this way, the hypothesis unit formulates a highly reliable hypothesis by taking into account the behavioral patterns of the parties involved. The behavioral patterns are considered based on, for example, past behavioral history and behavioral consistency. The past behavioral history identifies the past behavior of the parties involved and is useful for formulating a hypothesis. The behavioral consistency evaluates whether the behavior of the parties involved is consistent and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate a hypothesis by taking into account the behavioral patterns of the parties involved when formulating a hypothesis.

[0143] The hypothesis unit can estimate the user's emotions and adjust the display method of the hypotheses based on the estimated user emotions. For example, if the user is nervous, the hypothesis unit provides a simple, highly visible display method. Furthermore, if the user is calm, the hypothesis unit can provide a display method including detailed information. Furthermore, if the user is confused, the hypothesis unit can provide a display method that focuses on the main points. In this way, the hypothesis unit can provide an appropriate display method according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. In this way, the hypothesis unit can adjust the display method of the hypotheses based on the user's emotions.

[0144] When formulating a hypothesis, the hypothesis unit can formulate the hypothesis by taking into account geographic information of the location where the incident occurred. The hypothesis unit, for example, formulates a hypothesis based on geographic information of the location where the incident occurred. The hypothesis unit can also analyze the geographic information of the location where the incident occurred to formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account geographic information of the location where the incident occurred. In this way, the hypothesis unit can formulate a highly reliable hypothesis by taking into account geographic information. The geographic information is taken into account based on, for example, GPS data and map information. GPS data identifies the location where the incident occurred and is useful in formulating a hypothesis. Map information provides detailed geographic information of the location where the incident occurred and is useful in formulating a hypothesis. In this way, the hypothesis unit can formulate the hypothesis by taking into account geographic information of the location where the incident occurred.

[0145] The hypothesis unit can formulate a hypothesis by taking into account the timeline of the incident when formulating a hypothesis. The hypothesis unit formulates a hypothesis based on, for example, the timeline of the incident. The hypothesis unit can also analyze the timeline of the incident and formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account the timeline of the incident. In this way, the hypothesis unit can formulate a highly reliable hypothesis by taking the timeline into account. The timeline is considered based on, for example, the time when the incident occurred and the elapsed time. The time when the incident occurred identifies the specific time when the incident occurred and is useful for formulating a hypothesis. The elapsed time evaluates the passage of time since the incident occurred and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate a hypothesis by taking into account the timeline of the incident when formulating a hypothesis.

[0146] The hypothesis unit can formulate a hypothesis by taking into account attribute information of the parties involved when formulating a hypothesis. The hypothesis unit formulates a hypothesis based on, for example, the attribute information of the parties involved. The hypothesis unit can also analyze the attribute information of the parties involved and formulate the most reliable hypothesis. The hypothesis unit can also formulate the most appropriate hypothesis by taking into account the attribute information of the parties involved. In this way, the hypothesis unit formulates a highly reliable hypothesis by taking into account the attribute information of the parties involved. The attribute information is considered based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is useful for formulating a hypothesis. The occupation identifies the occupation of the parties involved and is useful for formulating a hypothesis. The relationship identifies the relationship between the parties involved and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate a hypothesis by taking into account attribute information of the parties involved when formulating a hypothesis.

[0147] The reproduction unit can estimate the user's emotions and adjust the method for generating the reproduction video based on the estimated user's emotions. For example, if the user is nervous, the reproduction unit generates a simple, highly visible reproduction video. Furthermore, if the user is calm, the reproduction unit can generate a reproduction video that includes detailed information. Furthermore, if the user is confused, the reproduction unit can generate a reproduction video that focuses on the main points. In this way, the reproduction unit generates an appropriate reproduction video according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. In this way, the reproduction unit can adjust the method for generating the reproduction video based on the user's emotions.

[0148] The reenactment unit can improve accuracy by referring to past incident data when generating the reenactment video. For example, the reenactment unit can improve the accuracy of the reenactment video by referring to past incident data. The reenactment unit can also analyze incident patterns based on past incident data. Furthermore, the reenactment unit can evaluate the reliability of evidence based on past incident data. In this way, the reenactment unit can improve the accuracy of the reenactment video by referring to past data. The reference to past incident data is performed using, for example, an incident database or a similarity evaluation criterion. The incident database provides detailed data on past incidents and is useful for generating the reenactment video. The similarity evaluation criterion evaluates the similarity between past incidents and the current incident and is useful for generating the reenactment video. In this way, the reenactment unit can improve accuracy by referring to past incident data when generating the reenactment video.

[0149] The reenactment unit can simultaneously generate and compare multiple scenarios when generating the reenactment video. The reenactment unit, for example, simultaneously generates multiple scenarios and selects the most reliable scenario. The reenactment unit can also compare multiple scenarios and extract commonalities. The reenactment unit can also consider multiple scenarios and select the most appropriate scenario. In this way, the reenactment unit compares multiple scenarios and selects the most reliable scenario. The generation of multiple scenarios is performed based on, for example, scenario components and evaluation criteria. The scenario components include scenarios from the occurrence of an incident to its resolution. The evaluation criteria evaluate the reliability and consistency of the scenario. In this way, the reenactment unit can simultaneously generate and compare multiple scenarios when generating the reenactment video.

[0150] The reproducing unit can generate the re-enacted video by taking into account the behavioral patterns of the people involved. The re-enacted video is generated based on, for example, the behavioral patterns of the people involved. The re-enacted video can also be generated by analyzing the behavioral patterns of the people involved and generating the most reliable re-enacted video. The re-enacted video can also be generated by taking into account the behavioral patterns of the people involved and generating the most appropriate re-enacted video. In this way, the re-enacted video is generated by taking into account the behavioral patterns of the people involved. The behavioral patterns are taken into account based on, for example, past behavioral history and behavioral consistency. The past behavioral history is used to identify the past behavior of the people involved and is useful for generating the re-enacted video. The behavioral consistency is used to evaluate whether the behavior of the people involved is consistent and is useful for generating the re-enacted video. In this way, the re-enacted video can be generated by taking into account the behavioral patterns of the people involved.

[0151] The reproduction unit can estimate the user's emotions and adjust the display method of the reproduced video based on the estimated user's emotions. For example, if the user is nervous, the reproduction unit provides a simple, highly visible display method. Furthermore, if the user is calm, the reproduction unit can provide a display method including detailed information. Furthermore, if the user is confused, the reproduction unit can provide a display method that focuses on the main points. In this way, the reproduction unit can provide an appropriate display method according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. In this way, the reproduction unit can adjust the display method of the reproduced video based on the user's emotions.

[0152] The reenactment unit can generate the reenactment video by taking into account geographic information of the location where the incident occurred. For example, the reenactment unit generates the reenactment video based on geographic information of the location where the incident occurred. The reenactment unit can also analyze the geographic information of the location where the incident occurred to generate the most reliable reenactment video. Furthermore, the reenactment unit can generate the most appropriate reenactment video by taking into account the geographic information of the location where the incident occurred. In this way, the reenactment unit can generate a highly reliable reenactment video by taking into account geographic information. The geographic information can be taken into account based on, for example, GPS data or map information. GPS data identifies the location where the incident occurred and is useful for generating the reenactment video. Map information provides detailed geographic information of the location where the incident occurred and is useful for generating the reenactment video. In this way, the reenactment unit can generate the video by taking into account geographic information of the location where the incident occurred.

[0153] The reenactment unit can generate the reenactment video taking into account the timeline of the incident when generating the reenactment video. The reenactment unit generates the reenactment video based on, for example, the timeline of the incident. The reenactment unit can also analyze the timeline of the incident and generate the most reliable reenactment video. The reenactment unit can also generate the most appropriate reenactment video by taking the timeline of the incident into account. In this way, the reenactment unit can generate a highly reliable reenactment video by taking the timeline into account. The timeline is taken into account based on, for example, the time when the incident occurred and the elapsed time. The time when the incident occurred identifies the specific time when the incident occurred and is useful for generating the reenactment video. The elapsed time evaluates the passage of time since the incident occurred and is useful for generating the reenactment video. In this way, the reenactment unit can generate the video taking into account the timeline of the incident when generating the reenactment video.

[0154] The reproduction unit can generate a reproduced video by taking into account attribute information of the parties involved. For example, the reproduction unit generates a reproduced video based on the attribute information of the parties involved. The reproduction unit can also analyze the attribute information of the parties involved and generate the most reliable reproduced video. The reproduction unit can also generate the most appropriate reproduced video by taking into account the attribute information of the parties involved. In this way, the reproduction unit generates a highly reliable reproduced video by taking into account the attribute information of the parties involved. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the parties involved and is used to generate the reproduced video. The occupation identifies the occupation of the parties involved and is used to generate the reproduced video. The relationship identifies the relationship between the parties involved and is used to generate the reproduced video. In this way, the reproduction unit can generate a video by taking into account attribute information of the parties involved.

[0155] The consideration unit can estimate the user's emotions and adjust the consideration method based on the estimated user's emotions. For example, if the user is nervous, the consideration unit provides a simple and highly visible consideration method. Furthermore, if the user is calm, the consideration unit can provide a consideration method that includes detailed information. Furthermore, if the user is confused, the consideration unit can provide a consideration method that focuses on the main points. This allows the consideration unit to make appropriate considerations according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the consideration unit to adjust the consideration method based on the user's emotions.

[0156] The consideration unit can improve the accuracy of the consideration by referring to past case data during consideration. The consideration unit, for example, refers to past case data to improve the accuracy of the consideration. The consideration unit can also analyze case patterns based on past case data. Furthermore, the consideration unit can evaluate the reliability of evidence based on past case data. In this way, the consideration unit can improve the accuracy of the consideration by referring to past data. The reference to past case data is performed, for example, using a case database or a similarity evaluation criterion. The case database provides detailed data on past cases to be useful in the consideration. The similarity evaluation criterion evaluates the similarity between past cases and the current case to be useful in the consideration. In this way, the consideration unit can improve the accuracy of the consideration by referring to past case data during consideration.

[0157] The consideration unit can consider and compare multiple hypotheses simultaneously during consideration. The consideration unit, for example, considers multiple hypotheses simultaneously and selects the most reliable hypothesis. The consideration unit can also compare multiple hypotheses and extract commonalities. The consideration unit can also consider multiple hypotheses and select the most appropriate hypothesis. In this way, the consideration unit selects the most reliable hypothesis by comparing multiple hypotheses. The consideration of multiple hypotheses is performed based on, for example, the components of a hypothesis and evaluation criteria. The components of a hypothesis include a scenario from the occurrence of an incident to its resolution. The evaluation criteria evaluate the reliability and consistency of a hypothesis. In this way, the consideration unit can consider and compare multiple hypotheses simultaneously during consideration.

[0158] The consideration unit can make considerations taking into account the behavioral patterns of the person involved during consideration. The consideration unit makes considerations, for example, based on the behavioral patterns of the person involved. The consideration unit can also analyze the behavioral patterns of the person involved and make the most reliable consideration. Furthermore, the consideration unit can make the most appropriate consideration by taking into account the behavioral patterns of the person involved. In this way, the consideration unit can make reliable considerations by taking into account the behavioral patterns of the person involved. Consideration of behavioral patterns is done, for example, based on past behavioral history and behavioral consistency. The past behavioral history identifies the past behavior of the person involved and is used for consideration. The behavioral consistency evaluates whether the behavior of the person involved is consistent and is used for consideration. In this way, the consideration unit can make considerations taking into account the behavioral patterns of the person involved during consideration.

[0159] The consideration unit can estimate the user's emotions and adjust the display method of the considerations based on the estimated user's emotions. For example, if the user is nervous, the consideration unit provides a simple, highly visible display method. Furthermore, if the user is calm, the consideration unit can provide a display method including detailed information. Furthermore, if the user is confused, the consideration unit can provide a display method that focuses on the main points. This allows the consideration unit to provide an appropriate display method according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the consideration unit to adjust the display method of the considerations based on the user's emotions.

[0160] The consideration unit can make considerations taking into account geographic information of the location where the incident occurred during consideration. The consideration unit, for example, makes considerations based on geographic information of the location where the incident occurred. The consideration unit can also analyze the geographic information of the location where the incident occurred and make the most reliable consideration. The consideration unit can also make the most appropriate consideration by taking into account the geographic information of the location where the incident occurred. In this way, the consideration unit can make a reliable consideration by taking into account geographic information. Consideration of geographic information is based on, for example, GPS data and map information. GPS data identifies the location where the incident occurred and is useful for the consideration. Map information provides detailed geographic information of the location where the incident occurred and is useful for the consideration. In this way, the consideration unit can make considerations taking into account geographic information of the location where the incident occurred during consideration.

[0161] The consideration unit can conduct its consideration taking into account the timeline of the incident when conducting its consideration. The consideration unit conducts its consideration based on, for example, the timeline of the incident. The consideration unit can also analyze the timeline of the incident and conduct the most reliable consideration. Furthermore, the consideration unit can conduct the most appropriate consideration by taking the timeline of the incident into account. In this way, the consideration unit can conduct a reliable consideration by taking the timeline into account. The timeline is considered based on, for example, the time when the incident occurred and the time that has elapsed. The time when the incident occurred identifies the specific time when the incident occurred and is useful for the consideration. The time that has elapsed evaluates the passage of time since the incident occurred and is useful for the consideration. In this way, the consideration unit can conduct its consideration taking into account the timeline of the incident when conducting its consideration.

[0162] The consideration unit can make considerations taking into account the attribute information of the related parties when making considerations. The consideration unit makes considerations, for example, based on the attribute information of the related parties. The consideration unit can also analyze the attribute information of the related parties and make the most reliable considerations. Furthermore, the consideration unit can make the most appropriate considerations by taking into account the attribute information of the related parties. In this way, the consideration unit can make a highly reliable consideration by taking into account the attribute information of the related parties. The attribute information is taken into account based on information such as age, occupation, and relationship. The age identifies the age of the related parties and is useful for the consideration. The occupation identifies the occupation of the related parties and is useful for the consideration. The relationship identifies the relationship between the related parties and is useful for the consideration. In this way, the consideration unit can make considerations taking into account the attribute information of the related parties when making considerations. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit monitors the contents of reports using the camera 42 and microphone 38B of the smart device 14 and collects information using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and grasps the outline of the incident. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, formulates an incident scenario hypothesis based on the analyzed information. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated information to investigators. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit monitors the contents of reports using the camera 42 and microphone 238 of the smart glasses 214 and collects information using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and grasps the outline of the incident. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, formulates an incident scenario hypothesis based on the analyzed information. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the generated information to an investigator. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit monitors the contents of reports using the camera 42 and microphone 238 of the headset terminal 314 and collects information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to grasp the outline of the incident. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formulates an incident scenario hypothesis based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides the generated information to an investigator. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit monitors the contents of reports using the camera 42 and microphone 238 of the robot 414 and collects information using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to grasp the outline of the incident. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and formulates an incident scenario hypothesis based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated information to investigators.

[0163] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0164] The collection unit can analyze the caller's tone of voice and background sounds to estimate the caller's emotional state. For example, if the caller is nervous, the collection unit can immediately begin collecting information and respond quickly. If the caller is calm, the collection unit can take a little time to collect detailed information. If the caller is confused, the collection unit can first collect basic information and then collect detailed information. This allows the collection unit to collect information at an appropriate time depending on the caller's emotions. The caller's emotions are estimated using technologies such as voice analysis and background sound analysis. Voice analysis analyzes the tone and speed of the caller's voice to estimate their emotions. Background sound analysis analyzes the background sounds of the call content to understand the caller's situation. This allows the collection unit to adjust the timing of information collection based on the caller's emotions.

[0165] The analysis unit can analyze the geographic information of the location of the incident based on the collected information and identify the location of the incident. For example, the analysis unit can use GPS data and map information to identify the location of the incident. The analysis unit can also identify the location of the incident based on eyewitness testimony and video recordings. Furthermore, the analysis unit can also identify the location of the incident based on audio recordings and background sounds. In this way, the analysis unit can improve the accuracy of the investigation by identifying the location of the incident. Analysis of geographic information is performed based on GPS data and map information. GPS data identifies the location of the incident and is useful for analysis. Map information provides detailed geographic information of the location of the incident and is useful for analysis. In this way, the analysis unit can analyze the geographic information of the location of the incident based on the collected information and identify the location of the incident.

[0166] The generation unit can formulate a scenario hypothesis based on the analysis results and taking into account the timeline of the incident. For example, the generation unit formulates a scenario hypothesis based on the occurrence time and elapsed time of the incident. The generation unit can also analyze the timeline of the incident and formulate the most reliable scenario hypothesis. The generation unit can also consider the timeline of the incident and formulate the most appropriate scenario hypothesis. In this way, the generation unit formulates a highly reliable scenario hypothesis by taking the timeline into account. The consideration of the timeline is based on the occurrence time and elapsed time of the incident. The occurrence time of the incident identifies the specific time when the incident occurred and is useful for formulating a scenario hypothesis. The elapsed time evaluates the passage of time since the incident occurred and is useful for formulating a scenario hypothesis. In this way, the generation unit can formulate a scenario hypothesis based on the analysis results and taking into account the timeline of the incident.

[0167] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible information providing method can be provided. If the user is calm, an information providing method including detailed information can be provided. Furthermore, if the user is confused, an information providing method that focuses on the main points can be provided. This allows the providing unit to provide appropriate information according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the providing unit to adjust the method of providing information based on the user's emotions.

[0168] The collection unit can analyze the urgency of the report content and select the optimal collection method. For example, if the urgency is high, audio analysis can be prioritized to immediately extract important information. If the urgency is medium, video recordings can be analyzed to grasp the situation at the scene. Furthermore, if the urgency is low, eyewitness testimony and expert opinions can be collected to obtain detailed information. This allows the collection unit to collect optimal information according to the urgency. Urgency analysis is performed based on keywords in the report content and the situation of the caller. Keyword analysis extracts words with high urgency contained in the report content and evaluates the urgency. The situation of the caller is evaluated by analyzing the caller's tone of voice and background sounds, etc. This allows the collection unit to analyze the urgency of the report content and select the optimal collection method.

[0169] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is nervous, it can prioritize analyzing information with a high level of urgency. Also, if the user is calm, it can prioritize analyzing detailed information. Furthermore, if the user is confused, it can prioritize analyzing basic information. This allows the analysis unit to perform appropriate analysis according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the analysis unit to adjust the analysis priority based on the user's emotions.

[0170] The generation unit can improve the accuracy of the scenario hypotheses by referring to past incident data during generation. For example, the generation unit can improve the accuracy of the scenario hypotheses by referring to past incident data. The generation unit can also analyze incident patterns based on past incident data. Furthermore, the generation unit can evaluate the reliability of evidence based on past incident data. In this way, the generation unit can improve the accuracy of the scenario hypotheses by referring to past data. The reference to past incident data is performed using an incident database and similarity evaluation criteria. The incident database provides detailed data on past incidents and is useful for generating scenario hypotheses. The similarity evaluation criteria evaluate the similarity between past incidents and the current incident and is useful for generating scenario hypotheses. In this way, the generation unit can improve the accuracy of the scenario hypotheses by referring to past incident data during generation.

[0171] The provision department can provide information by reflecting the investigator's past feedback when providing the information. For example, the provision department selects the optimal information provision method based on the investigator's past feedback. The provision department can also adjust the level of detail of the information by reflecting the investigator's past feedback. Furthermore, the provision department can adjust the timing of information provision by referring to the investigator's past feedback. In this way, the provision department can provide optimal information by reflecting past feedback. Feedback is reflected based on past evaluations and areas for improvement. Past evaluations are based on the investigator's evaluation of information provided in the past. Areas for improvement are based on areas for improvement pointed out by the investigator in the past. In this way, the provision department can provide information by reflecting the investigator's past feedback when providing the information.

[0172] The crosscheck unit can estimate the user's emotions and adjust the priority of crosschecks based on the estimated user's emotions. For example, if the user is nervous, it can prioritize crosschecking information with high urgency. Also, if the user is calm, it can prioritize crosschecking detailed information. Furthermore, if the user is confused, it can prioritize crosschecking basic information. This allows the crosscheck unit to perform appropriate crosschecks according to the user's emotions. The user's emotions are estimated using technologies such as voice analysis and facial expression recognition. Voice analysis analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition analyzes changes in the user's facial expressions to estimate emotions. This allows the crosscheck unit to adjust the priority of crosschecks based on the user's emotions.

[0173] When formulating a hypothesis, the hypothesis unit can formulate the hypothesis by taking into account the behavioral patterns of the person involved. For example, the hypothesis unit formulates a hypothesis based on the behavioral patterns of the person involved. The hypothesis unit can also analyze the behavioral patterns of the person involved and formulate the most reliable hypothesis. Furthermore, the hypothesis unit can formulate the most appropriate hypothesis by taking into account the behavioral patterns of the person involved. In this way, the hypothesis unit formulates a highly reliable hypothesis by taking into account the behavioral patterns of the person involved. The behavioral patterns are considered based on past behavioral history and behavioral consistency. The past behavioral history identifies the past behavior of the person involved and is useful for formulating a hypothesis. The behavioral consistency evaluates whether the behavior of the person involved is consistent and is useful for formulating a hypothesis. In this way, the hypothesis unit can formulate the hypothesis by taking into account the behavioral patterns of the person involved.

[0174] The processing flow of the second embodiment will be briefly explained below.

[0175] Step 1: The collection department monitors emergency calls and immediately begins collecting information. The department analyzes the call content in real time to understand the outline of the incident. It can also extract information from the call content, such as the location, time, and people involved. Step 2: The Analysis Department analyzes the information collected by the Collection Department to understand the outline of the case. The Analysis Department analyzes investigative information such as physical evidence of the incident or accident, eyewitness testimony, expert opinions, audio and video recordings, confessions, etc. It can also compare eyewitness testimony with video recordings to see if there is a match. Step 3: The generation unit formulates a hypothesis for the incident scenario based on the information analyzed by the analysis unit. The generation unit hypothesizes a scenario from the occurrence of the incident to its resolution based on physical evidence and testimony. It can also generate reenactment footage based on the hypothesis. Step 4: The information provided by the generation unit is provided to investigators. The provision unit provides reenactment footage to investigators, making it easier for them to grasp the overall picture of the case. It can also provide reliable verification information such as recommendations for missing evidence, correlations between people, and case hypotheses.

[0176] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0181] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0182] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0183] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0184] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0185] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0186] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0187] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0188] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0189] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0190] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0191] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0192] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0197] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0198] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0199] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0200] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0201] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0202] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0203] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0204] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0205] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0206] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0207] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0208] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0210] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0211] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0212] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0213] 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.

[0214] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0215] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0216] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0218] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0219] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0220] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0221] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0222] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0223] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0224] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0225] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0226] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0227] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0228] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0229] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0230] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0231] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0232] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0233] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0234] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0235] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0236] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0237] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0238] 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.

[0239] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0240] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0241] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0242] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0243] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0244] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0245] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0246] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0247] [Explanation of symbols]

[0248] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The collection department monitors emergency calls and immediately begins collecting information. an analysis unit that analyzes the information collected by the collection unit and grasps an outline of the incident; a generation unit that formulates a case scenario hypothesis based on the information analyzed by the analysis unit; a providing unit that provides the information generated by the generating unit to an investigator. A system characterized by:

2. The analysis unit Analyze investigative information such as physical evidence, eyewitness testimony, expert opinions, audio and video recordings, and confessions of crimes and accidents 2. The system of claim 1.

3. The generation unit Equipped with a cross-check unit that cross-checks evidence data based on analysis results 2. The system of claim 1.

4. The generation unit Equipped with a hypothesis department that formulates incident scenario hypotheses based on the analysis results 2. The system of claim 1.

5. The generation unit a reproduction unit that generates a reproduction image based on the result of the hypothesis unit; 5. The system of claim 4.

6. The providing unit and a review unit that provides information to accelerate the investigation based on the results of the reconstruction unit.

6. The system of claim 5.

7. The consideration unit Providing reliable verification information for missing evidence recommendations, person correlations, and case hypotheses The system of claim 6 .

8. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

Images