system

Abstract

We provide the system. [Solution] Means of acquiring data, Means for performing preprocessing to process the aforementioned data, A means of analyzing data from multiple modalities and extracting features, A means for determining the situation based on the aforementioned characteristics, Means for generating action proposals based on the aforementioned judgment results, A system including means for presenting the aforementioned proposal to a user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] At modern investigation sites, a huge amount of data is collected from various information sources such as videos, audios, and text data. However, efficiently integrating these and extracting important information in real time places a great burden on investigators. In addition, delays and errors in situation judgment may cause criminals to escape or result in misinvestigations. The present invention aims to solve these problems and speed up investigations and improve accuracy.

Means for Solving the Problems

[0005] The present invention provides a system comprising means for acquiring data, means for preprocessing data, means for analyzing data from multiple modalities and extracting features, means for determining the situation based on the features, means for generating action suggestions based on the determination results, and means for presenting the suggestions to the user. The aim is to enable investigators to integrate and analyze a wide variety of data collected at the scene in real time, thereby supporting rapid and accurate decision-making.

[0006] "Means of acquiring data" refers to the function of collecting various types of information, such as video, audio, and text data, using sensors and input devices.

[0007] "Methods for preprocessing" refers to the process of converting data into an appropriate format for analysis, and performing noise reduction and formatting.

[0008] "Methods for analyzing data from multiple modalities and extracting features" refers to technologies that integrate and process data from different modalities and identify characteristic information from each set of data.

[0009] "Means of assessing the situation" refers to the process of analyzing the on-site situation and related information based on the extracted characteristics, and then making a judgment.

[0010] "Means for generating action suggestions" refers to a function that derives and suggests specific actions that the user should take based on the decision results.

[0011] "Means of presenting suggestions to users" refers to an interface that displays generated action suggestions to users in an intuitive format to facilitate understanding. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system of this invention utilizes multimodal AI technology to support rapid and accurate situational assessment at crime scenes. The system consists of a terminal, a server, and a user interface.

[0034] The terminal plays a role in acquiring data at the crime scene. Specifically, it collects video data using a camera and audio data using a microphone. It also collects text information entered by investigators at the scene, integrating necessary information according to the situation. This data is encrypted and securely transmitted to the server.

[0035] The server plays a central role in the integrated analysis of data from multiple modalities. Preprocessing of received data includes noise reduction and format conversion, preparing the data for analysis. In video analysis, it recognizes objects and people, and in audio analysis, it transcribes speech through speech recognition. Furthermore, natural language processing extracts keywords and emotions from the text data. Based on these analysis results, the system assesses the situation, organizes relevant information, and provides guidance for communicating it to investigators.

[0036] Users can receive action suggestions sent from the server and instantly obtain information through the interface. These suggestions include specific action examples, characteristics of the target individual, and safe travel routes. Based on this information, users can decide on their next course of action and take swift action.

[0037] As a concrete example, consider a situation where multiple suspicious individuals have been sighted at a crime scene.

[0038] The device records a wide range of surroundings with its camera and collects audio with its microphone.

[0039] The server identifies individuals with specific clothing or postures through video analysis and extracts potentially alarming statements from audio analysis.

[0040] Based on the analysis results, the server generates likely action suggestions, such as "Pursue the suspicious person in the direction of the south exit."

[0041] The user, the investigator, will then quickly take action and carry out the next steps accordingly.

[0042] Thus, by utilizing the system of the present invention, it is possible to streamline decision-making at the scene of an investigation and improve the efficiency of police activities.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device collects video and audio data at the investigation scene. It captures surrounding video with its camera and records audio with its microphone. Text data entered by investigators is also captured by the device. The collected data is encrypted and sent to a server.

[0046] Step 2:

[0047] The server receives data sent from the terminal. The received data undergoes preprocessing such as noise reduction and format conversion. This results in video data being divided into frames, audio data being converted to text, and text data being formatted.

[0048] Step 3:

[0049] The server analyzes pre-processed data. It detects and recognizes objects and people from video data. It uses speech recognition technology to transcribe speech from audio data. Natural language processing is used to extract important information and emotions from the text data.

[0050] Step 4:

[0051] The server assesses the situation on-site based on the analysis results. It compares the analyzed information with past databases to identify abnormal patterns and suspects.

[0052] Step 5:

[0053] The server generates action suggestions based on the situation. These suggestions include information on possible routes and priority targets for investigation. These suggestions are intended to support the investigators' decision-making.

[0054] Step 6:

[0055] The terminal receives suggestions from the server and displays them to the investigator through the user interface. The investigator then takes swift action based on these suggestions and carries out the next investigative steps.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In investigative settings, it is crucial to quickly and accurately assess the situation and provide appropriate instructions. However, traditional methods struggle to integrate and analyze multiple data formats, resulting in a lack of mechanisms to support accurate decision-making. To address this challenge, there is a need for a system that performs advanced data analysis and efficiently delivers information.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes a device for encrypting data for transmission, a device for preprocessing data, and a device for analyzing data in multiple formats and extracting features. This enables secure data transmission, improved data quality through noise reduction, and advanced situational judgment using multiple data formats.

[0061] "Data collection equipment" refers to hardware used to acquire video, audio, and text information at the scene of an investigation.

[0062] An "encryption device" refers to a system that uses encryption technology to transform acquired data in order to protect it from unauthorized access.

[0063] A "pre-processing device" is a device that performs processing to remove noise from received data and convert it into a unified data format.

[0064] A "device for analyzing data in multiple formats" refers to a system that simultaneously analyzes data in different formats, such as video and audio, to extract useful information.

[0065] A "feature extraction device" is a device that executes algorithms to identify important elements and information from the data being analyzed.

[0066] A "device for judging a situation" refers to a device that analyzes the current situation based on extracted characteristics and derives an appropriate judgment.

[0067] A "device for generating action suggestions" refers to a system that, based on the assessed situation, specifically presents the next actions or measures that should be taken.

[0068] "Providing devices" refers to devices that visually or audibly communicate generated action suggestions to the user.

[0069] This invention is a system for streamlining information gathering and situational assessment at crime scenes. The system mainly consists of terminals, servers, and users, each playing a specific role.

[0070] The terminal is a device used to collect data at the scene of an investigation. It uses a camera and microphone to capture video and audio. Manual text input by the user is also possible. The collected data is encrypted and securely transmitted to a server. Encryption is crucial to maintain the confidentiality and integrity of the data.

[0071] The server plays a central role in data analysis. Received data is first preprocessed, undergoing noise reduction and format conversion. Video data is analyzed using object detection algorithms (e.g., general image analysis software), and audio data is converted to text using a speech recognition API. Keywords and emotions are extracted from the data using natural language processing techniques (e.g., text analysis libraries). Furthermore, a generative AI model is used to generate action suggestions based on the analysis results.

[0072] Users receive suggested actions through an interface. The interface visually presents information and supports decision-making. These suggestions include appropriate countermeasures, points to note, and recommended routes. Based on this, users can make quick decisions and conduct efficient field activities.

[0073] As a concrete example, consider a police investigation scene where numerous suspicious individuals have been sighted. The terminal records the surrounding environment with its camera and captures audio with its microphone. The server identifies individuals with distinctive clothing or mannerisms through video analysis and detects potentially alarming statements through audio analysis. Based on the analysis results, a suggestion is generated: "Pursue the suspicious individuals in the direction of the south exit." The investigators follow this suggestion and begin responding quickly.

[0074] As an example of a prompt to the generating AI model, the text "Generate action suggestions to assist in dealing with suspicious individuals at a crime scene" is used. This prompt allows the AI ​​model to derive the optimal course of action.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The terminal is responsible for collecting data at the crime scene. It handles video and audio data acquired from cameras and microphones as input. In addition, it accepts text information manually entered by investigators. The acquired data is encrypted using an encryption algorithm and sent to the server in a secure format as output.

[0078] Step 2:

[0079] The server receives encrypted data from the terminal. Inputs include video data, audio data, and text data. The server denoises this data and converts the video data to a standard format. For audio data, it performs format conversion and cleanup to create an analyzable data format as output.

[0080] Step 3:

[0081] The server analyzes the pre-processed data. For video data, an object detection algorithm is applied to identify people and objects. Audio data is converted to text using speech recognition software. This allows the multimodal data received as input to be output as information possessing specific characteristics of people and objects.

[0082] Step 4:

[0083] The server uses a generative AI model based on the analysis results to generate action suggestions. Based on the analyzed video, audio, and text data as input, the generative AI model receives an appropriate prompt (e.g., "Generate the best course of action in this situation") and creates specific action suggestions as output.

[0084] Step 5:

[0085] The user receives action suggestions from the server through an interface. Based on the action suggestions provided as input, the user confirms the specific instructions and then takes action based on them as output. The suggestions include travel routes and precautions, and the user quickly takes the next step according to them.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] When conducting security monitoring and safety management on-site, it is essential to quickly detect suspicious individuals and dangerous situations and respond immediately. However, current systems do not process information in real time, leading to delays in situation assessment and subsequent responses. This delay is a major obstacle to ensuring safety on-site, and the development of technologies that enable effective and rapid responses is highly desirable.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for extracting features from video and audio in real time, means for generating warnings and instructions based on situational judgment, and means for immediately transmitting this information to the user. This enables rapid and accurate security response on-site, ensuring safety.

[0091] "Data" refers to information such as video, audio, and text acquired from the monitored location.

[0092] A "device" is a set of hardware or software components designed to perform a specific function.

[0093] "Information format" refers to the various forms that data can take, including, for example, video, audio, and text.

[0094] "Features" are important patterns and indicators derived from analyzed data, and are used for situational assessment.

[0095] "Situation assessment" is the process of evaluating the current on-site situation based on acquired characteristics and deciding on appropriate responses.

[0096] An "action proposal" is a set of guidelines outlining specific actions to be taken on-site, based on the results of a situation assessment.

[0097] "Users" refer to those who receive information through the system, and in this context, primarily security staff.

[0098] A "warning" is an alert sent to the user to inform them of a potential danger detected by the system.

[0099] The system that implements this application supports rapid decision-making by leveraging advanced data analysis technologies to enhance security. The system mainly consists of terminals, servers, and users.

[0100] The terminal is responsible for acquiring data in the field. Specifically, it uses a camera to capture video of the surroundings and a microphone to collect audio. This data is securely transmitted to the server using end-to-end encryption technology.

[0101] The server plays a central role in comprehensively analyzing the received data. Video data is identified using an object detection API to identify people and specific objects, and audio data is converted to text using a speech recognition API. Furthermore, relevant keywords are extracted from the text by utilizing a natural language processing library. Based on these analysis results, a generative AI model assesses the situation and generates necessary action guidelines.

[0102] Security staff, as users, can utilize the information transmitted from the server and take swift and appropriate action through the interface. For example, if suspicious activity is detected in a specific area, they can immediately direct their attention to that area.

[0103] As a concrete example, consider a situation in a commercial facility where a specific individual is exhibiting abnormal behavior. The terminal records the individual's movements on video and detects surrounding sounds. The server analyzes this data and, if abnormal behavior is detected, generates a suspicious behavior alert and sends it to the user to promote safety measures.

[0104] An example of a prompt is provided: "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." Based on this prompt, the generating AI model performs appropriate analysis and provides useful results.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The terminal acquires video data using on-site cameras and collects audio data using microphones. This data is converted into a digital format as input. The converted data is then transmitted to the server in a secure manner via end-to-end encryption technology.

[0108] Step 2:

[0109] The server performs noise reduction and format conversion on the received video data. This process extracts video data that can be analyzed. Next, an object detection API is used to perform image analysis and identify people and objects in the video. Information about the identified people and objects is obtained as output.

[0110] Step 3:

[0111] The server uses a speech recognition API to convert received audio data into text. This process analyzes the audio file and outputs the corresponding text data. Furthermore, it performs noise reduction processing to extract clear text information.

[0112] Step 4:

[0113] The server utilizes a generative AI model to comprehensively assess the current situation based on the analysis results of video and audio. In this process, it extracts relevant keywords using a natural language processing library and analyzes the data. This generates action suggestions that are appropriate to the situation.

[0114] Step 5:

[0115] The server sends generated action suggestions to the user. These suggestions may include, for example, warnings about suspicious individuals or alerts to specific areas. The prompt message is formatted as follows: "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." The action suggestions are then notified to the user.

[0116] Step 6:

[0117] Users review the action suggestions received via their devices and take appropriate actions on-site according to the instructions. Specific actions include checking for proximity to suspicious individuals and strengthening security in specific areas. This feedback loop enables effective real-time security management.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] The system of this invention is an advanced investigative support system that incorporates an emotion engine that recognizes user emotions, in addition to conventional data acquisition and analysis functions. This system consists mainly of a terminal, a server, and a user interface.

[0120] The device collects data needed at the investigation scene from various sources. Specifically, it captures video with its camera and records audio with its microphone. In addition, it collects text information entered by the user at the scene, as well as data on the user's gaze and facial expressions. This data is encrypted and securely transmitted to the server.

[0121] The server comprehensively analyzes the received data. Individual analyses are performed for each modality—video, audio, and text—while the emotion engine processes the data to recognize emotions. Video data analysis recognizes individuals and objects, while audio data analysis transcribes speech and performs emotion recognition. From text-based data, emotions are extracted based on the frequency and context of specific words. Based on these various analysis results, the system makes situational judgments on-site.

[0122] The user receives action suggestions from the server through an interface. These suggestions are presented as specific instructions for action appropriate to the situation. Furthermore, suggestions are tailored to the user's psychological state based on emotion recognition results, thereby reducing stress and facilitating quick decision-making.

[0123] As a concrete example, let me give a situation from a certain investigation scene.

[0124] The terminal collects diverse data under the control of investigators at the scene and immediately transmits all data, including captured video and recorded audio, to the server.

[0125] The server identifies individual emotions from the tone and content of speech through voice analysis, and grasps emotions from changes in facial expressions and posture through video analysis. These common emotional patterns are analyzed by an emotion engine and reflected in the situational judgment.

[0126] The user, acting as the investigator, initiates action based on the judgment obtained and takes the optimal course of action to achieve the proposed objective.

[0127] Thus, by using the present invention system incorporating an emotion engine, the efficiency of investigations can be significantly improved, and detailed responses tailored to the situation can be made possible.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The device acquires multimodal data on-site. It collects surrounding video using a camera and records ambient sounds with a microphone. In addition, it collects text information obtained from user input, as well as user facial expressions and gaze data. The acquired data is encrypted and sent to the server.

[0131] Step 2:

[0132] The server receives data sent from the terminal and performs preprocessing. It performs noise reduction and frame splitting on video data, and filtering and text conversion on audio data. Keyword extraction and formatting are performed on the text data.

[0133] Step 3:

[0134] The server analyzes multiple modalities. In video analysis, facial recognition technology is used to identify individuals and determine emotions from their facial expressions. In audio analysis, emotions are extracted from spoken content, and additional emotional information is obtained through natural language processing. The emotion engine integrates these analysis results to identify the user's overall emotional state.

[0135] Step 4:

[0136] The server integrates the analysis results to make a situation assessment. By comparing them with past emotional data and incident patterns stored in the database, it identifies abnormal emotions and important trends. This assessment is performed in real time, deepening the understanding of the situation on the scene.

[0137] Step 5:

[0138] The server generates action suggestions that incorporate emotion recognition. Considering the user's current psychological state, it provides suggestions to reduce the user's burden and offers situation-appropriate action plans. This information assists investigators in making appropriate decisions.

[0139] Step 6:

[0140] The terminal receives the generated action suggestions and displays them to the user through the user interface. The suggestions are presented in a visually clear manner, allowing investigators to quickly recognize them and initiate appropriate actions.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] With the recent advancements in information technology, there is a growing need to quickly and accurately analyze diverse data at the crime scene and, based on that analysis, provide appropriate action suggestions tailored to the situation. However, current systems struggle to provide action suggestions that comprehensively capture the user's emotions, resulting in a lack of efficiency and flexibility in investigations. In particular, there is a need for emotion analysis that takes nonverbal elements such as eye gaze and facial expressions into consideration, and the provision of immediate action instructions based on the results.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for acquiring data including the user's gaze and facial expressions, means for encrypting and transmitting the data using the SSL / TLS protocol, and means for analyzing the data for each modality of video, audio, and text, and extracting emotions. This makes it possible to comprehensively analyze diverse data obtained at the scene of an investigation and immediately provide the user with accurate and flexible action suggestions based on prompt sentences using a generated AI model.

[0146] "Data including user gaze and facial expressions" refers to non-verbal information that records the user's eye movements and changes in facial expressions, and is an element used to analyze the user's emotions and psychological state.

[0147] The SSL / TLS protocol is an encryption protocol used to ensure security in data communication and protects the confidentiality and integrity of data.

[0148] "Analyzing data for each modality—video, audio, and text—means performing individual analyses on each of the three different data formats—video, audio, and text—and extracting information by leveraging the characteristics of each format.

[0149] "Emotion extraction" is the process of analysis performed to identify the emotional state of a user or subject based on collected data.

[0150] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate solutions or suggestions based on specific input information.

[0151] A "prompt statement" is an input statement that provides specific instructions or context to a generative AI model, and is used to guide the model's output.

[0152] The system of this invention includes a user, a terminal, and a server, and focuses particularly on emotion analysis and behavioral suggestions in investigative settings.

[0153] The terminal is deployed at the investigation site and collects data through various sensors and interfaces. Specifically, it acquires video using a camera and records audio using a microphone. In addition, eye-tracking sensors and facial recognition cameras collect data including the user's gaze and facial expressions. Text data is obtained from information entered by the user and notes recorded at the scene. This data is encrypted via the SSL / TLS protocol and securely transmitted to the server.

[0154] The server analyzes data from various modalities. Video data incorporates computer vision technology for facial recognition and object detection. Audio data is converted to text using a speech recognition engine, and emotions are extracted through tone analysis. Text data uses natural language processing technology to analyze emotions from sentence structure and word frequency. The emotional information extracted through these processes is integrated by an emotion engine and used for comprehensive situational judgment.

[0155] Subsequently, the server utilizes the generated AI model to construct prompt sentences based on the analysis results and generate action suggestions. These prompt sentences play a role in providing specific instructions to the AI ​​model and outputting the optimal action scenario. The generated action suggestions take into account the user's psychological state, promoting stress reduction and rapid decision-making.

[0156] Users receive these action suggestions sent from the server through an interface. The interface explicitly displays the action suggestions, making it easier for users to select the most appropriate response for the situation. For example, if a user, acting as an investigator, is conducting an interview, they might receive suggestions based on analysis such as, "The subject is stressed. Please soften your questioning tone to create a calmer atmosphere," enabling them to respond effectively in a situation-appropriate manner.

[0157] As an example of a prompt, the AI ​​model can be instructed to "Generate action suggestions based on the investigator's psychological state while tracking the progress at the scene in real time. However, ensure that all emotions and situations are handled safely and appropriately," which will generate appropriate action suggestions for the user.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The device collects diverse data from users in real time at the crime scene. Specifically, it acquires video with a camera and records audio using a microphone. In addition, an eye-tracking sensor captures the user's gaze data, and a facial recognition camera detects changes in facial expressions. This input data includes raw video, audio, gaze, and facial expression data. The collected data is encrypted using the SSL / TLS protocol and transmitted to the server via a secure channel.

[0161] Step 2:

[0162] The server receives various data transmitted from the terminal and begins the analysis process. The inputs received are encrypted video, audio, gaze, and facial expression data. The server first analyzes the video data using computer vision technology and outputs metadata for recognized faces and objects. Audio data is converted to text by a speech recognition engine, and the tone of speech is further extracted through acoustic analysis. Gaze and facial expression data are used by an emotion engine to identify the user's emotional state. The output of this process is emotion information and extracted feature data.

[0163] Step 3:

[0164] The server generates action suggestions using a generative AI model based on integrated sentiment information and feature data. The input consists of the analyzed sentiment information and feature data. Prompt sentences are constructed and provided to the AI ​​model, automatically generating situation-appropriate action instructions. The output is a specific action suggestion. For example, it might generate something like, "The survey participants appear restless. We recommend increasing the interval between questions."

[0165] Step 4:

[0166] Users receive action suggestions from the server through a dedicated interface. The input is the generated action suggestions. The interface visually presents these suggestions to the user, allowing the user to decide on a course of action at the investigation scene based on this information. The final output is the action chosen by the user, which improves investigation efficiency and enables rapid decision-making.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] The challenge lies in accurately understanding user emotions from diverse data and presenting appropriate content in real time to make the user experience more engaging and stress-free. Furthermore, recommending content based on individual emotions is difficult, requiring flexible responses tailored to user feelings.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for pre-processing data for processing, means for analyzing data from multiple modalities and extracting feature information, and means for analyzing user sentiment information and presenting appropriate content. This enables immediate recommendation of optimal content according to the user's emotional state.

[0172] A "data acquisition device" is a mechanism that uses various sensors and input devices to collect diverse data such as audio, video, and text.

[0173] A "preprocessing device" is a mechanism that converts collected data into a format that is easy to process, and performs noise reduction and correction.

[0174] A "feature information extraction device" is a mechanism that finds important features from data across multiple modalities and processes them into a format that can be used for analysis.

[0175] A "device for determining the situation" is a mechanism that analyzes the relationships between data based on extracted features and grasps the current state.

[0176] A "device for generating action proposals" is a mechanism for formulating and presenting optimal action guidelines based on the assessed situation.

[0177] A "device that analyzes user emotional information and presents appropriate content" is a mechanism that analyzes changes in a user's emotions and selects and provides the most suitable entertainment and information accordingly.

[0178] The system implementing this invention utilizes an application running on a smartphone or wearable device to provide content recommendations based on the user's emotions.

[0179] The device acquires user data such as gaze, facial expressions, and voice through its camera and microphone. The data is acquired in real time, pre-processed, encrypted, and then sent to the server.

[0180] The server uses software such as TENSORFLOW® and OpenCV to analyze emotions from facial expressions and voice. By removing noise in the preprocessing stage and analyzing the data comprehensively from multiple modalities, it accurately determines the user's emotional state.

[0181] Based on the emotional information obtained, the server uses an AI model to select and present appropriate content in real time. This entire process makes it possible to provide entertainment and information tailored to the user's current mood.

[0182] For example, if the camera captures a user's face while they are using their smartphone and detects that they are relaxing while listening to music, the system can use that information to suggest relaxing music or sounds with a sleep timer function.

[0183] An example of a prompt to input into a generative AI model is, "Please recommend music that is best suited for when the user is relaxing."

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The device uses the smartphone's camera and microphone to capture the user's gaze, facial expressions, and voice data. This data is immediately and temporarily stored on the device. The acquired data serves as basic material for inferring the user's emotional state.

[0187] Step 2:

[0188] The device preprocesses the acquired gaze, facial expression, and audio data. Specifically, it performs noise reduction and synchronizes the video and audio. As a result, data that can be analyzed is generated. The preprocessed data is encrypted and sent to the server.

[0189] Step 3:

[0190] The server uses OpenCV to analyze facial expressions from received data and determines the user's emotions. The input data is video information, and the output is an estimated result of the emotional state. The server uses TensorFlow to analyze audio data and extract supplementary emotional information from factors such as voice tone and speed.

[0191] Step 4:

[0192] The server uses an emotion engine to comprehensively analyze emotion estimation results from multiple modalities. It receives emotion estimation results from facial expressions and emotion information from speech as input, and outputs an integrated emotion judgment. This allows for a precise determination of the user's current emotional state.

[0193] Step 5:

[0194] The server uses a generative AI model based on the sentiment assessment results to generate prompt messages and select the most appropriate content for the user. The output is specific content such as music or video. This selected content is immediately sent to the user's device.

[0195] Step 6:

[0196] Users receive selected content from the server on their devices and use it. This process allows users to experience entertainment that best suits their emotional state in real time.

[0197] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0203] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0204] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0205] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0206] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0207] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0208] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0209] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0210] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0211] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0212] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0213] The system of this invention utilizes multimodal AI technology to support rapid and accurate situational assessment at crime scenes. The system consists of a terminal, a server, and a user interface.

[0214] The terminal plays a role in acquiring data at the crime scene. Specifically, it collects video data using a camera and audio data using a microphone. It also collects text information entered by investigators at the scene, integrating necessary information according to the situation. This data is encrypted and securely transmitted to the server.

[0215] The server plays a central role in the integrated analysis of data from multiple modalities. Preprocessing of received data includes noise reduction and format conversion, preparing the data for analysis. In video analysis, it recognizes objects and people, and in audio analysis, it transcribes speech through speech recognition. Furthermore, natural language processing extracts keywords and emotions from the text data. Based on these analysis results, the system assesses the situation, organizes relevant information, and provides guidance for communicating it to investigators.

[0216] Users can receive action suggestions sent from the server and instantly obtain information through the interface. These suggestions include specific action examples, characteristics of the target individual, and safe travel routes. Based on this information, users can decide on their next course of action and take swift action.

[0217] As a concrete example, consider a situation where multiple suspicious individuals have been sighted at a crime scene.

[0218] The device records a wide range of surroundings with its camera and collects audio with its microphone.

[0219] The server identifies individuals with specific clothing or postures through video analysis and extracts potentially alarming statements from audio analysis.

[0220] Based on the analysis results, the server generates likely action suggestions, such as "Pursue the suspicious person in the direction of the south exit."

[0221] The user, the investigator, will then quickly take action and carry out the next steps accordingly.

[0222] Thus, by utilizing the system of the present invention, it is possible to streamline decision-making at the scene of an investigation and improve the efficiency of police activities.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The device collects video and audio data at the investigation scene. It captures surrounding video with its camera and records audio with its microphone. Text data entered by investigators is also captured by the device. The collected data is encrypted and sent to a server.

[0226] Step 2:

[0227] The server receives data sent from the terminal. The received data undergoes preprocessing such as noise reduction and format conversion. This results in video data being divided into frames, audio data being converted to text, and text data being formatted.

[0228] Step 3:

[0229] The server analyzes pre-processed data. It detects and recognizes objects and people from video data. It uses speech recognition technology to transcribe speech from audio data. Natural language processing is used to extract important information and emotions from the text data.

[0230] Step 4:

[0231] The server assesses the situation on-site based on the analysis results. It compares the analyzed information with past databases to identify abnormal patterns and suspects.

[0232] Step 5:

[0233] The server generates action suggestions based on the situation. These suggestions include information on possible routes and priority targets for investigation. These suggestions are intended to support the investigators' decision-making.

[0234] Step 6:

[0235] The terminal receives suggestions from the server and displays them to the investigator through the user interface. The investigator then takes swift action based on these suggestions and carries out the next investigative steps.

[0236] (Example 1)

[0237] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0238] In investigative settings, it is crucial to quickly and accurately assess the situation and provide appropriate instructions. However, traditional methods struggle to integrate and analyze multiple data formats, resulting in a lack of mechanisms to support accurate decision-making. To address this challenge, there is a need for a system that performs advanced data analysis and efficiently delivers information.

[0239] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0240] In this invention, the server includes a device for encrypting data for transmission, a device for preprocessing data, and a device for analyzing data in multiple formats and extracting features. This enables secure data transmission, improved data quality through noise reduction, and advanced situational judgment using multiple data formats.

[0241] "Data collection equipment" refers to hardware used to acquire video, audio, and text information at the scene of an investigation.

[0242] An "encryption device" refers to a system that uses encryption technology to transform acquired data in order to protect it from unauthorized access.

[0243] A "pre-processing device" is a device that performs processing to remove noise from received data and convert it into a unified data format.

[0244] A "device for analyzing data in multiple formats" refers to a system that simultaneously analyzes data in different formats, such as video and audio, to extract useful information.

[0245] A "feature extraction device" is a device that executes algorithms to identify important elements and information from the data being analyzed.

[0246] A "device for judging a situation" refers to a device that analyzes the current situation based on extracted characteristics and derives an appropriate judgment.

[0247] A "device for generating action suggestions" refers to a system that, based on the assessed situation, specifically presents the next actions or measures that should be taken.

[0248] "Providing devices" refers to devices that visually or audibly communicate generated action suggestions to the user.

[0249] This invention is a system for streamlining information gathering and situational assessment at crime scenes. The system mainly consists of terminals, servers, and users, each playing a specific role.

[0250] The terminal is a device used to collect data at the scene of an investigation. It uses a camera and microphone to capture video and audio. Manual text input by the user is also possible. The collected data is encrypted and securely transmitted to a server. Encryption is crucial to maintain the confidentiality and integrity of the data.

[0251] The server plays a central role in data analysis. Received data is first preprocessed, undergoing noise reduction and format conversion. Video data is analyzed using object detection algorithms (e.g., general image analysis software), and audio data is converted to text using a speech recognition API. Keywords and emotions are extracted from the data using natural language processing techniques (e.g., text analysis libraries). Furthermore, a generative AI model is used to generate action suggestions based on the analysis results.

[0252] Users receive suggested actions through an interface. The interface visually presents information and supports decision-making. These suggestions include appropriate countermeasures, points to note, and recommended routes. Based on this, users can make quick decisions and conduct efficient field activities.

[0253] As a concrete example, consider a police investigation scene where numerous suspicious individuals have been sighted. The terminal records the surrounding environment with its camera and captures audio with its microphone. The server identifies individuals with distinctive clothing or mannerisms through video analysis and detects potentially alarming statements through audio analysis. Based on the analysis results, a suggestion is generated: "Pursue the suspicious individuals in the direction of the south exit." The investigators follow this suggestion and begin responding quickly.

[0254] As an example of a prompt to the generating AI model, the text "Generate action suggestions to assist in dealing with suspicious individuals at a crime scene" is used. This prompt allows the AI ​​model to derive the optimal course of action.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The terminal is responsible for collecting data at the crime scene. It handles video and audio data acquired from cameras and microphones as input. In addition, it accepts text information manually entered by investigators. The acquired data is encrypted using an encryption algorithm and sent to the server in a secure format as output.

[0258] Step 2:

[0259] The server receives encrypted data from the terminal. Inputs include video data, audio data, and text data. The server denoises this data and converts the video data to a standard format. For audio data, it performs format conversion and cleanup to create an analyzable data format as output.

[0260] Step 3:

[0261] The server analyzes the pre-processed data. For video data, an object detection algorithm is applied to identify people and objects. Audio data is converted to text using speech recognition software. This allows the multimodal data received as input to be output as information possessing specific characteristics of people and objects.

[0262] Step 4:

[0263] The server uses a generative AI model based on the analysis results to generate action suggestions. Based on the analyzed video, audio, and text data as input, the generative AI model receives an appropriate prompt (e.g., "Generate the best course of action in this situation") and creates specific action suggestions as output.

[0264] Step 5:

[0265] The user receives action suggestions from the server through an interface. Based on the action suggestions provided as input, the user confirms the specific instructions and then takes action based on them as output. The suggestions include travel routes and precautions, and the user quickly takes the next step according to them.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] When conducting security monitoring and safety management on-site, it is essential to quickly detect suspicious individuals and dangerous situations and respond immediately. However, current systems do not process information in real time, leading to delays in situation assessment and subsequent responses. This delay is a major obstacle to ensuring safety on-site, and the development of technologies that enable effective and rapid responses is highly desirable.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes means for extracting features from video and audio in real time, means for generating warnings and instructions based on situational judgment, and means for immediately transmitting this information to the user. This enables rapid and accurate security response on-site, ensuring safety.

[0271] "Data" refers to information such as video, audio, and text acquired from the monitored location.

[0272] A "device" is a set of hardware or software components designed to perform a specific function.

[0273] "Information format" refers to the various forms that data can take, including, for example, video, audio, and text.

[0274] "Features" are important patterns and indicators derived from analyzed data, and are used for situational assessment.

[0275] "Situation assessment" is the process of evaluating the current on-site situation based on acquired characteristics and deciding on appropriate responses.

[0276] An "action proposal" is a set of guidelines outlining specific actions to be taken on-site, based on the results of a situation assessment.

[0277] "Users" refer to those who receive information through the system, and in this context, primarily security staff.

[0278] A "warning" is an alert sent to the user to inform them of a potential danger detected by the system.

[0279] The system that implements this application supports rapid decision-making by leveraging advanced data analysis technologies to enhance security. The system mainly consists of terminals, servers, and users.

[0280] The terminal is responsible for acquiring data in the field. Specifically, it uses a camera to capture video of the surroundings and a microphone to collect audio. This data is securely transmitted to the server using end-to-end encryption technology.

[0281] The server plays a central role in comprehensively analyzing the received data. Video data is identified using an object detection API to identify people and specific objects, and audio data is converted to text using a speech recognition API. Furthermore, relevant keywords are extracted from the text by utilizing a natural language processing library. Based on these analysis results, a generative AI model assesses the situation and generates necessary action guidelines.

[0282] Security staff, as users, can utilize the information transmitted from the server and take swift and appropriate action through the interface. For example, if suspicious activity is detected in a specific area, they can immediately direct their attention to that area.

[0283] As a concrete example, consider a situation in a commercial facility where a specific individual is exhibiting abnormal behavior. The terminal records the individual's movements on video and detects surrounding sounds. The server analyzes this data and, if abnormal behavior is detected, generates a suspicious behavior alert and sends it to the user to promote safety measures.

[0284] As an example of a prompt sentence, "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." is prepared. Based on this prompt, the generative AI model performs appropriate analysis and provides useful results.

[0285] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The terminal uses the on-site camera to acquire video data and uses the microphone to collect audio data. These data are converted into digital format as input. The converted data is transmitted to the server in a security-protected form via end-to-end encryption technology.

[0288] Step 2:

[0289] The server performs noise removal and format conversion on the received video data. Through this process, analyzable video data is extracted. Next, image analysis is performed using the object detection API to identify people and objects in the video. Information on the identified people and objects is obtained as output.

[0290] Step 3:

[0291] The server uses the speech recognition API to convert the received audio data into text. In this operation, the audio file is analyzed and the corresponding text data is output. Furthermore, through the process of removing noise, clear text information is extracted.

[0292] Step 4:

[0293] The server utilizes the generative AI model to comprehensively judge the current situation based on the analysis results of video and audio. In this process, relevant keywords are extracted using the natural language processing library and the data is analyzed. Thereby, action proposals appropriate to the situation are generated.

[0294] Step 5:

[0295] The server sends generated action suggestions to the user. These suggestions may include, for example, warnings about suspicious individuals or alerts to specific areas. The prompt message is formatted as follows: "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." The action suggestions are then notified to the user.

[0296] Step 6:

[0297] Users review the action suggestions received via their devices and take appropriate actions on-site according to the instructions. Specific actions include checking for proximity to suspicious individuals and strengthening security in specific areas. This feedback loop enables effective real-time security management.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] The system of this invention is an advanced investigative support system that incorporates an emotion engine that recognizes user emotions, in addition to conventional data acquisition and analysis functions. This system consists mainly of a terminal, a server, and a user interface.

[0300] The device collects data needed at the investigation scene from various sources. Specifically, it captures video with its camera and records audio with its microphone. In addition, it collects text information entered by the user at the scene, as well as data on the user's gaze and facial expressions. This data is encrypted and securely transmitted to the server.

[0301] The server analyzes the received data integratively. While performing individual analyses for each modality of video, audio, and text, the emotion engine performs processing to recognize emotions from these data. In video data analysis, it recognizes the target person or object, and in audio data analysis, it converts the speech content into text and recognizes emotions. From text-based data, it extracts emotions from the usage frequency of specific words and the context. Based on the various analysis results thus obtained, it determines the situation on the scene.

[0302] The user receives the action proposals provided by the server through the interface. The proposals are presented as instructions for specific actions according to the situation. Also, according to the emotion recognition results, proposals considering the user's psychological state are made, which promotes stress reduction and rapid decision-making.

[0303] As a specific example, the situation at a certain investigation site is cited.

[0304] The terminal collects various data under the operation of the investigator at the scene and immediately transmits all data including the captured video and recorded audio to the server.

[0305] The server identifies individual emotions from the tone and content of the speech through audio analysis, and grasps emotions from changes in expressions and postures in video analysis. These common emotion patterns are analyzed by the emotion engine and reflected as a situation judgment.

[0306] The investigator, who is the user, starts an action based on the obtained judgment result and performs the optimal action to achieve the proposed goal.

[0307] In this way, by using the system of the present invention incorporating an emotion engine, the efficiency of the investigation site is greatly improved, and detailed responses according to the situation become possible.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] The device acquires multimodal data on-site. It collects surrounding video using a camera and records ambient sounds with a microphone. In addition, it collects text information obtained from user input, as well as user facial expressions and gaze data. The acquired data is encrypted and sent to the server.

[0311] Step 2:

[0312] The server receives data sent from the terminal and performs preprocessing. It performs noise reduction and frame splitting on video data, and filtering and text conversion on audio data. Keyword extraction and formatting are performed on the text data.

[0313] Step 3:

[0314] The server analyzes multiple modalities. In video analysis, facial recognition technology is used to identify individuals and determine emotions from their facial expressions. In audio analysis, emotions are extracted from spoken content, and additional emotional information is obtained through natural language processing. The emotion engine integrates these analysis results to identify the user's overall emotional state.

[0315] Step 4:

[0316] The server integrates the analysis results to make a situation assessment. By comparing them with past emotional data and incident patterns stored in the database, it identifies abnormal emotions and important trends. This assessment is performed in real time, deepening the understanding of the situation on the scene.

[0317] Step 5:

[0318] The server generates action suggestions that incorporate emotion recognition. Considering the user's current psychological state, it provides suggestions to reduce the user's burden and offers situation-appropriate action plans. This information assists investigators in making appropriate decisions.

[0319] Step 6:

[0320] The terminal receives the generated action suggestions and displays them to the user through the user interface. The suggestions are presented in a visually clear manner, allowing investigators to quickly recognize them and initiate appropriate actions.

[0321] (Example 2)

[0322] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0323] With the recent advancements in information technology, there is a growing need to quickly and accurately analyze diverse data at the crime scene and, based on that analysis, provide appropriate action suggestions tailored to the situation. However, current systems struggle to provide action suggestions that comprehensively capture the user's emotions, resulting in a lack of efficiency and flexibility in investigations. In particular, there is a need for emotion analysis that takes nonverbal elements such as eye gaze and facial expressions into consideration, and the provision of immediate action instructions based on the results.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes means for acquiring data including the user's gaze and facial expressions, means for encrypting and transmitting the data using the SSL / TLS protocol, and means for analyzing the data for each modality of video, audio, and text, and extracting emotions. This makes it possible to comprehensively analyze diverse data obtained at the scene of an investigation and immediately provide the user with accurate and flexible action suggestions based on prompt sentences using a generated AI model.

[0326] "Data including user gaze and facial expressions" refers to non-verbal information that records the user's eye movements and changes in facial expressions, and is an element used to analyze the user's emotions and psychological state.

[0327] The SSL / TLS protocol is an encryption protocol used to ensure security in data communication and protects the confidentiality and integrity of data.

[0328] "Analyzing data for each modality—video, audio, and text—means performing individual analyses on each of the three different data formats—video, audio, and text—and extracting information by leveraging the characteristics of each format.

[0329] "Emotion extraction" is the process of analysis performed to identify the emotional state of a user or subject based on collected data.

[0330] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate solutions or suggestions based on specific input information.

[0331] A "prompt statement" is an input statement that provides specific instructions or context to a generative AI model, and is used to guide the model's output.

[0332] The system of this invention includes a user, a terminal, and a server, and focuses particularly on emotion analysis and behavioral suggestions in investigative settings.

[0333] The terminal is deployed at the investigation site and collects data through various sensors and interfaces. Specifically, it acquires video using a camera and records audio using a microphone. In addition, eye-tracking sensors and facial recognition cameras collect data including the user's gaze and facial expressions. Text data is obtained from information entered by the user and notes recorded at the scene. This data is encrypted via the SSL / TLS protocol and securely transmitted to the server.

[0334] The server analyzes data from various modalities. Video data incorporates computer vision technology for facial recognition and object detection. Audio data is converted to text using a speech recognition engine, and emotions are extracted through tone analysis. Text data uses natural language processing technology to analyze emotions from sentence structure and word frequency. The emotional information extracted through these processes is integrated by an emotion engine and used for comprehensive situational judgment.

[0335] Subsequently, the server utilizes the generated AI model to construct prompt sentences based on the analysis results and generate action suggestions. These prompt sentences play a role in providing specific instructions to the AI ​​model and outputting the optimal action scenario. The generated action suggestions take into account the user's psychological state, promoting stress reduction and rapid decision-making.

[0336] Users receive these action suggestions sent from the server through an interface. The interface explicitly displays the action suggestions, making it easier for users to select the most appropriate response for the situation. For example, if a user, acting as an investigator, is conducting an interview, they might receive suggestions based on analysis such as, "The subject is stressed. Please soften your questioning tone to create a calmer atmosphere," enabling them to respond effectively in a situation-appropriate manner.

[0337] As an example of a prompt, the AI ​​model can be instructed to "Generate action suggestions based on the investigator's psychological state while tracking the progress at the scene in real time. However, ensure that all emotions and situations are handled safely and appropriately," which will generate appropriate action suggestions for the user.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The device collects diverse data from users in real time at the crime scene. Specifically, it acquires video with a camera and records audio using a microphone. In addition, an eye-tracking sensor captures the user's gaze data, and a facial recognition camera detects changes in facial expressions. This input data includes raw video, audio, gaze, and facial expression data. The collected data is encrypted using the SSL / TLS protocol and transmitted to the server via a secure channel.

[0341] Step 2:

[0342] The server receives various data transmitted from the terminal and begins the analysis process. The inputs received are encrypted video, audio, gaze, and facial expression data. The server first analyzes the video data using computer vision technology and outputs metadata for recognized faces and objects. Audio data is converted to text by a speech recognition engine, and the tone of speech is further extracted through acoustic analysis. Gaze and facial expression data are used by an emotion engine to identify the user's emotional state. The output of this process is emotion information and extracted feature data.

[0343] Step 3:

[0344] The server generates action suggestions using a generative AI model based on integrated sentiment information and feature data. The input consists of the analyzed sentiment information and feature data. Prompt sentences are constructed and provided to the AI ​​model, automatically generating situation-appropriate action instructions. The output is a specific action suggestion. For example, it might generate something like, "The survey participants appear restless. We recommend increasing the interval between questions."

[0345] Step 4:

[0346] Users receive action suggestions from the server through a dedicated interface. The input is the generated action suggestions. The interface visually presents these suggestions to the user, allowing the user to decide on a course of action at the investigation scene based on this information. The final output is the action chosen by the user, which improves investigation efficiency and enables rapid decision-making.

[0347] (Application Example 2)

[0348] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0349] The challenge lies in accurately understanding user emotions from diverse data and presenting appropriate content in real time to make the user experience more engaging and stress-free. Furthermore, recommending content based on individual emotions is difficult, requiring flexible responses tailored to user feelings.

[0350] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0351] In this invention, the server includes means for pre-processing data for processing, means for analyzing data from multiple modalities and extracting feature information, and means for analyzing user sentiment information and presenting appropriate content. This enables immediate recommendation of optimal content according to the user's emotional state.

[0352] A "data acquisition device" is a mechanism that uses various sensors and input devices to collect diverse data such as audio, video, and text.

[0353] A "preprocessing device" is a mechanism that converts collected data into a format that is easy to process, and performs noise reduction and correction.

[0354] A "feature information extraction device" is a mechanism that finds important features from data across multiple modalities and processes them into a format that can be used for analysis.

[0355] A "device for determining the situation" is a mechanism that analyzes the relationships between data based on extracted features and grasps the current state.

[0356] A "device for generating action proposals" is a mechanism for formulating and presenting optimal action guidelines based on the assessed situation.

[0357] A "device that analyzes user emotional information and presents appropriate content" is a mechanism that analyzes changes in a user's emotions and selects and provides the most suitable entertainment and information accordingly.

[0358] The system implementing this invention utilizes an application running on a smartphone or wearable device to provide content recommendations based on the user's emotions.

[0359] The device acquires user data such as gaze, facial expressions, and voice through its camera and microphone. The data is acquired in real time, pre-processed, encrypted, and then sent to the server.

[0360] The server uses software such as TensorFlow and OpenCV to analyze emotions from facial expressions and voice. By removing noise in the preprocessing stage and analyzing the data comprehensively from multiple modalities, it accurately determines the user's emotional state.

[0361] Based on the emotional information obtained, the server uses an AI model to select and present appropriate content in real time. This entire process makes it possible to provide entertainment and information tailored to the user's current mood.

[0362] For example, if the camera captures a user's face while they are using their smartphone and detects that they are relaxing while listening to music, the system can use that information to suggest relaxing music or sounds with a sleep timer function.

[0363] An example of a prompt to input into a generative AI model is, "Please recommend music that is best suited for when the user is relaxing."

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The device uses the smartphone's camera and microphone to capture the user's gaze, facial expressions, and voice data. This data is immediately and temporarily stored on the device. The acquired data serves as basic material for inferring the user's emotional state.

[0367] Step 2:

[0368] The device preprocesses the acquired gaze, facial expression, and audio data. Specifically, it performs noise reduction and synchronizes the video and audio. As a result, data that can be analyzed is generated. The preprocessed data is encrypted and sent to the server.

[0369] Step 3:

[0370] The server uses OpenCV to analyze facial expressions from received data and determines the user's emotions. The input data is video information, and the output is an estimated result of the emotional state. The server uses TensorFlow to analyze audio data and extract supplementary emotional information from factors such as voice tone and speed.

[0371] Step 4:

[0372] The server uses an emotion engine to comprehensively analyze emotion estimation results from multiple modalities. It receives emotion estimation results from facial expressions and emotion information from speech as input, and outputs an integrated emotion judgment. This allows for a precise determination of the user's current emotional state.

[0373] Step 5:

[0374] The server uses a generative AI model based on the sentiment assessment results to generate prompt messages and select the most appropriate content for the user. The output is specific content such as music or video. This selected content is immediately sent to the user's device.

[0375] Step 6:

[0376] Users receive selected content from the server on their devices and use it. This process allows users to experience entertainment that best suits their emotional state in real time.

[0377] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0384] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0386] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0388] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0389] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] The system of this invention utilizes multimodal AI technology to support rapid and accurate situational assessment at crime scenes. The system consists of a terminal, a server, and a user interface.

[0394] The terminal plays a role in acquiring data at the crime scene. Specifically, it collects video data using a camera and audio data using a microphone. It also collects text information entered by investigators at the scene, integrating necessary information according to the situation. This data is encrypted and securely transmitted to the server.

[0395] The server plays a central role in the integrated analysis of data from multiple modalities. Preprocessing of received data includes noise reduction and format conversion, preparing the data for analysis. In video analysis, it recognizes objects and people, and in audio analysis, it transcribes speech through speech recognition. Furthermore, natural language processing extracts keywords and emotions from the text data. Based on these analysis results, the system assesses the situation, organizes relevant information, and provides guidance for communicating it to investigators.

[0396] Users can receive action suggestions sent from the server and instantly obtain information through the interface. These suggestions include specific action examples, characteristics of the target individual, and safe travel routes. Based on this information, users can decide on their next course of action and take swift action.

[0397] As a concrete example, consider a situation where multiple suspicious individuals have been sighted at a crime scene.

[0398] The device records a wide range of surroundings with its camera and collects audio with its microphone.

[0399] The server identifies individuals with specific clothing or postures through video analysis and extracts potentially alarming statements from audio analysis.

[0400] Based on the analysis results, the server generates likely action suggestions, such as "Pursue the suspicious person in the direction of the south exit."

[0401] The user, the investigator, will then quickly take action and carry out the next steps accordingly.

[0402] Thus, by utilizing the system of the present invention, it is possible to streamline decision-making at the scene of an investigation and improve the efficiency of police activities.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] The device collects video and audio data at the investigation scene. It captures surrounding video with its camera and records audio with its microphone. Text data entered by investigators is also captured by the device. The collected data is encrypted and sent to a server.

[0406] Step 2:

[0407] The server receives data sent from the terminal. The received data undergoes preprocessing such as noise reduction and format conversion. This results in video data being divided into frames, audio data being converted to text, and text data being formatted.

[0408] Step 3:

[0409] The server analyzes pre-processed data. It detects and recognizes objects and people from video data. It uses speech recognition technology to transcribe speech from audio data. Natural language processing is used to extract important information and emotions from the text data.

[0410] Step 4:

[0411] The server assesses the situation on-site based on the analysis results. It compares the analyzed information with past databases to identify abnormal patterns and suspects.

[0412] Step 5:

[0413] The server generates action suggestions based on the situation. These suggestions include information on possible routes and priority targets for investigation. These suggestions are intended to support the investigators' decision-making.

[0414] Step 6:

[0415] The terminal receives suggestions from the server and displays them to the investigator through the user interface. The investigator then takes swift action based on these suggestions and carries out the next investigative steps.

[0416] (Example 1)

[0417] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0418] In investigative settings, it is crucial to quickly and accurately assess the situation and provide appropriate instructions. However, traditional methods struggle to integrate and analyze multiple data formats, resulting in a lack of mechanisms to support accurate decision-making. To address this challenge, there is a need for a system that performs advanced data analysis and efficiently delivers information.

[0419] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0420] In this invention, the server includes a device for encrypting data for transmission, a device for preprocessing data, and a device for analyzing data in multiple formats and extracting features. This enables secure data transmission, improved data quality through noise reduction, and advanced situational judgment using multiple data formats.

[0421] "Data collection equipment" refers to hardware used to acquire video, audio, and text information at the scene of an investigation.

[0422] An "encryption device" refers to a system that uses encryption technology to transform acquired data in order to protect it from unauthorized access.

[0423] A "pre-processing device" is a device that performs processing to remove noise from received data and convert it into a unified data format.

[0424] A "device for analyzing data in multiple formats" refers to a system that simultaneously analyzes data in different formats, such as video and audio, to extract useful information.

[0425] A "feature extraction device" is a device that executes algorithms to identify important elements and information from the data being analyzed.

[0426] A "device for judging a situation" refers to a device that analyzes the current situation based on extracted characteristics and derives an appropriate judgment.

[0427] A "device for generating action suggestions" refers to a system that, based on the assessed situation, specifically presents the next actions or measures that should be taken.

[0428] "Providing devices" refers to devices that visually or audibly communicate generated action suggestions to the user.

[0429] This invention is a system for streamlining information gathering and situational assessment at crime scenes. The system mainly consists of terminals, servers, and users, each playing a specific role.

[0430] The terminal is a device used to collect data at the scene of an investigation. It uses a camera and microphone to capture video and audio. Manual text input by the user is also possible. The collected data is encrypted and securely transmitted to a server. Encryption is crucial to maintain the confidentiality and integrity of the data.

[0431] The server plays a central role in data analysis. Received data is first preprocessed, undergoing noise reduction and format conversion. Video data is analyzed using object detection algorithms (e.g., general image analysis software), and audio data is converted to text using a speech recognition API. Keywords and emotions are extracted from the data using natural language processing techniques (e.g., text analysis libraries). Furthermore, a generative AI model is used to generate action suggestions based on the analysis results.

[0432] Users receive suggested actions through an interface. The interface visually presents information and supports decision-making. These suggestions include appropriate countermeasures, points to note, and recommended routes. Based on this, users can make quick decisions and conduct efficient field activities.

[0433] As a concrete example, consider a police investigation scene where numerous suspicious individuals have been sighted. The terminal records the surrounding environment with its camera and captures audio with its microphone. The server identifies individuals with distinctive clothing or mannerisms through video analysis and detects potentially alarming statements through audio analysis. Based on the analysis results, a suggestion is generated: "Pursue the suspicious individuals in the direction of the south exit." The investigators follow this suggestion and begin responding quickly.

[0434] As an example of a prompt to the generating AI model, the text "Generate action suggestions to assist in dealing with suspicious individuals at a crime scene" is used. This prompt allows the AI ​​model to derive the optimal course of action.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The terminal is responsible for collecting data at the crime scene. It handles video and audio data acquired from cameras and microphones as input. In addition, it accepts text information manually entered by investigators. The acquired data is encrypted using an encryption algorithm and sent to the server in a secure format as output.

[0438] Step 2:

[0439] The server receives encrypted data from the terminal. Inputs include video data, audio data, and text data. The server denoises this data and converts the video data to a standard format. For audio data, it performs format conversion and cleanup to create an analyzable data format as output.

[0440] Step 3:

[0441] The server analyzes the pre-processed data. For video data, an object detection algorithm is applied to identify people and objects. Audio data is converted to text using speech recognition software. This allows the multimodal data received as input to be output as information possessing specific characteristics of people and objects.

[0442] Step 4:

[0443] The server uses a generative AI model based on the analysis results to generate action suggestions. Based on the analyzed video, audio, and text data as input, the generative AI model receives an appropriate prompt (e.g., "Generate the best course of action in this situation") and creates specific action suggestions as output.

[0444] Step 5:

[0445] The user receives action suggestions from the server through an interface. Based on the action suggestions provided as input, the user confirms the specific instructions and then takes action based on them as output. The suggestions include travel routes and precautions, and the user quickly takes the next step according to them.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0448] When conducting security monitoring and safety management on-site, it is essential to quickly detect suspicious individuals and dangerous situations and respond immediately. However, current systems do not process information in real time, leading to delays in situation assessment and subsequent responses. This delay is a major obstacle to ensuring safety on-site, and the development of technologies that enable effective and rapid responses is highly desirable.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes means for extracting features from video and audio in real time, means for generating warnings and instructions based on situational judgment, and means for immediately transmitting this information to the user. This enables rapid and accurate security response on-site, ensuring safety.

[0451] "Data" refers to information such as video, audio, and text acquired from the monitored location.

[0452] A "device" is a set of hardware or software components designed to perform a specific function.

[0453] "Information format" refers to the various forms that data can take, including, for example, video, audio, and text.

[0454] "Features" are important patterns and indicators derived from analyzed data, and are used for situational assessment.

[0455] "Situation assessment" is the process of evaluating the current on-site situation based on acquired characteristics and deciding on appropriate responses.

[0456] An "action proposal" is a set of guidelines outlining specific actions to be taken on-site, based on the results of a situation assessment.

[0457] "Users" refer to those who receive information through the system, and in this context, primarily security staff.

[0458] A "warning" is an alert sent to the user to inform them of a potential danger detected by the system.

[0459] The system that implements this application supports rapid decision-making by leveraging advanced data analysis technologies to enhance security. The system mainly consists of terminals, servers, and users.

[0460] The terminal is responsible for acquiring data in the field. Specifically, it uses a camera to capture video of the surroundings and a microphone to collect audio. This data is securely transmitted to the server using end-to-end encryption technology.

[0461] The server plays a central role in comprehensively analyzing the received data. Video data is identified using an object detection API to identify people and specific objects, and audio data is converted to text using a speech recognition API. Furthermore, relevant keywords are extracted from the text by utilizing a natural language processing library. Based on these analysis results, a generative AI model assesses the situation and generates necessary action guidelines.

[0462] Security staff, as users, can utilize the information transmitted from the server and take swift and appropriate action through the interface. For example, if suspicious activity is detected in a specific area, they can immediately direct their attention to that area.

[0463] As a concrete example, consider a situation in a commercial facility where a specific individual is exhibiting abnormal behavior. The terminal records the individual's movements on video and detects surrounding sounds. The server analyzes this data and, if abnormal behavior is detected, generates a suspicious behavior alert and sends it to the user to promote safety measures.

[0464] An example of a prompt is provided: "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." Based on this prompt, the generating AI model performs appropriate analysis and provides useful results.

[0465] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0466] Step 1:

[0467] The terminal acquires video data using on-site cameras and collects audio data using microphones. This data is converted into a digital format as input. The converted data is then transmitted to the server in a secure manner via end-to-end encryption technology.

[0468] Step 2:

[0469] The server performs noise reduction and format conversion on the received video data. This process extracts video data that can be analyzed. Next, an object detection API is used to perform image analysis and identify people and objects in the video. Information about the identified people and objects is obtained as output.

[0470] Step 3:

[0471] The server uses a speech recognition API to convert received audio data into text. This process analyzes the audio file and outputs the corresponding text data. Furthermore, it performs noise reduction processing to extract clear text information.

[0472] Step 4:

[0473] The server utilizes a generative AI model to comprehensively assess the current situation based on the analysis results of video and audio. In this process, it extracts relevant keywords using a natural language processing library and analyzes the data. This generates action suggestions that are appropriate to the situation.

[0474] Step 5:

[0475] The server sends generated action suggestions to the user. These suggestions may include, for example, warnings about suspicious individuals or alerts to specific areas. The prompt message is formatted as follows: "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." The action suggestions are then notified to the user.

[0476] Step 6:

[0477] Users review the action suggestions received via their devices and take appropriate actions on-site according to the instructions. Specific actions include checking for proximity to suspicious individuals and strengthening security in specific areas. This feedback loop enables effective real-time security management.

[0478] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0479] The system of this invention is an advanced investigative support system that incorporates an emotion engine that recognizes user emotions, in addition to conventional data acquisition and analysis functions. This system consists mainly of a terminal, a server, and a user interface.

[0480] The device collects data needed at the investigation scene from various sources. Specifically, it captures video with its camera and records audio with its microphone. In addition, it collects text information entered by the user at the scene, as well as data on the user's gaze and facial expressions. This data is encrypted and securely transmitted to the server.

[0481] The server comprehensively analyzes the received data. Individual analyses are performed for each modality—video, audio, and text—while the emotion engine processes the data to recognize emotions. Video data analysis recognizes individuals and objects, while audio data analysis transcribes speech and performs emotion recognition. From text-based data, emotions are extracted based on the frequency and context of specific words. Based on these various analysis results, the system makes situational judgments on-site.

[0482] The user receives action suggestions from the server through an interface. These suggestions are presented as specific instructions for action appropriate to the situation. Furthermore, suggestions are tailored to the user's psychological state based on emotion recognition results, thereby reducing stress and facilitating quick decision-making.

[0483] As a concrete example, let me give a situation from a certain investigation scene.

[0484] The terminal collects diverse data under the control of investigators at the scene and immediately transmits all data, including captured video and recorded audio, to the server.

[0485] The server identifies individual emotions from the tone and content of speech through voice analysis, and grasps emotions from changes in facial expressions and posture through video analysis. These common emotional patterns are analyzed by an emotion engine and reflected in the situational judgment.

[0486] The user, acting as the investigator, initiates action based on the judgment obtained and takes the optimal course of action to achieve the proposed objective.

[0487] Thus, by using the present invention system incorporating an emotion engine, the efficiency of investigations can be significantly improved, and detailed responses tailored to the situation can be made possible.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The device acquires multimodal data on-site. It collects surrounding video using a camera and records ambient sounds with a microphone. In addition, it collects text information obtained from user input, as well as user facial expressions and gaze data. The acquired data is encrypted and sent to the server.

[0491] Step 2:

[0492] The server receives data sent from the terminal and performs preprocessing. It performs noise reduction and frame splitting on video data, and filtering and text conversion on audio data. Keyword extraction and formatting are performed on the text data.

[0493] Step 3:

[0494] The server analyzes multiple modalities. In video analysis, facial recognition technology is used to identify individuals and determine emotions from their facial expressions. In audio analysis, emotions are extracted from spoken content, and additional emotional information is obtained through natural language processing. The emotion engine integrates these analysis results to identify the user's overall emotional state.

[0495] Step 4:

[0496] The server integrates the analysis results to make a situation assessment. By comparing them with past emotional data and incident patterns stored in the database, it identifies abnormal emotions and important trends. This assessment is performed in real time, deepening the understanding of the situation on the scene.

[0497] Step 5:

[0498] The server generates action suggestions that incorporate emotion recognition. Considering the user's current psychological state, it provides suggestions to reduce the user's burden and offers situation-appropriate action plans. This information assists investigators in making appropriate decisions.

[0499] Step 6:

[0500] The terminal receives the generated action suggestions and displays them to the user through the user interface. The suggestions are presented in a visually clear manner, allowing investigators to quickly recognize them and initiate appropriate actions.

[0501] (Example 2)

[0502] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0503] With the recent advancements in information technology, there is a growing need to quickly and accurately analyze diverse data at the crime scene and, based on that analysis, provide appropriate action suggestions tailored to the situation. However, current systems struggle to provide action suggestions that comprehensively capture the user's emotions, resulting in a lack of efficiency and flexibility in investigations. In particular, there is a need for emotion analysis that takes nonverbal elements such as eye gaze and facial expressions into consideration, and the provision of immediate action instructions based on the results.

[0504] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0505] In this invention, the server includes means for acquiring data including the user's gaze and facial expressions, means for encrypting and transmitting the data using the SSL / TLS protocol, and means for analyzing the data for each modality of video, audio, and text, and extracting emotions. This makes it possible to comprehensively analyze diverse data obtained at the scene of an investigation and immediately provide the user with accurate and flexible action suggestions based on prompt sentences using a generated AI model.

[0506] "Data including user gaze and facial expressions" refers to non-verbal information that records the user's eye movements and changes in facial expressions, and is an element used to analyze the user's emotions and psychological state.

[0507] The SSL / TLS protocol is an encryption protocol used to ensure security in data communication and protects the confidentiality and integrity of data.

[0508] "Analyzing data for each modality—video, audio, and text—means performing individual analyses on each of the three different data formats—video, audio, and text—and extracting information by leveraging the characteristics of each format.

[0509] "Emotion extraction" is the process of analysis performed to identify the emotional state of a user or subject based on collected data.

[0510] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate solutions or suggestions based on specific input information.

[0511] A "prompt statement" is an input statement that provides specific instructions or context to a generative AI model, and is used to guide the model's output.

[0512] The system of this invention includes a user, a terminal, and a server, and focuses particularly on emotion analysis and behavioral suggestions in investigative settings.

[0513] The terminal is deployed at the investigation site and collects data through various sensors and interfaces. Specifically, it acquires video using a camera and records audio using a microphone. In addition, eye-tracking sensors and facial recognition cameras collect data including the user's gaze and facial expressions. Text data is obtained from information entered by the user and notes recorded at the scene. This data is encrypted via the SSL / TLS protocol and securely transmitted to the server.

[0514] The server analyzes data from various modalities. Video data incorporates computer vision technology for facial recognition and object detection. Audio data is converted to text using a speech recognition engine, and emotions are extracted through tone analysis. Text data uses natural language processing technology to analyze emotions from sentence structure and word frequency. The emotional information extracted through these processes is integrated by an emotion engine and used for comprehensive situational judgment.

[0515] Subsequently, the server utilizes the generated AI model to construct prompt sentences based on the analysis results and generate action suggestions. These prompt sentences play a role in providing specific instructions to the AI ​​model and outputting the optimal action scenario. The generated action suggestions take into account the user's psychological state, promoting stress reduction and rapid decision-making.

[0516] Users receive these action suggestions sent from the server through an interface. The interface explicitly displays the action suggestions, making it easier for users to select the most appropriate response for the situation. For example, if a user, acting as an investigator, is conducting an interview, they might receive suggestions based on analysis such as, "The subject is stressed. Please soften your questioning tone to create a calmer atmosphere," enabling them to respond effectively in a situation-appropriate manner.

[0517] As an example of a prompt, the AI ​​model can be instructed to "Generate action suggestions based on the investigator's psychological state while tracking the progress at the scene in real time. However, ensure that all emotions and situations are handled safely and appropriately," which will generate appropriate action suggestions for the user.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The device collects diverse data from users in real time at the crime scene. Specifically, it acquires video with a camera and records audio using a microphone. In addition, an eye-tracking sensor captures the user's gaze data, and a facial recognition camera detects changes in facial expressions. This input data includes raw video, audio, gaze, and facial expression data. The collected data is encrypted using the SSL / TLS protocol and transmitted to the server via a secure channel.

[0521] Step 2:

[0522] The server receives various data transmitted from the terminal and begins the analysis process. The inputs received are encrypted video, audio, gaze, and facial expression data. The server first analyzes the video data using computer vision technology and outputs metadata for recognized faces and objects. Audio data is converted to text by a speech recognition engine, and the tone of speech is further extracted through acoustic analysis. Gaze and facial expression data are used by an emotion engine to identify the user's emotional state. The output of this process is emotion information and extracted feature data.

[0523] Step 3:

[0524] The server generates action suggestions using a generative AI model based on integrated sentiment information and feature data. The input consists of the analyzed sentiment information and feature data. Prompt sentences are constructed and provided to the AI ​​model, automatically generating situation-appropriate action instructions. The output is a specific action suggestion. For example, it might generate something like, "The survey participants appear restless. We recommend increasing the interval between questions."

[0525] Step 4:

[0526] Users receive action suggestions from the server through a dedicated interface. The input is the generated action suggestions. The interface visually presents these suggestions to the user, allowing the user to decide on a course of action at the investigation scene based on this information. The final output is the action chosen by the user, which improves investigation efficiency and enables rapid decision-making.

[0527] (Application Example 2)

[0528] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0529] The challenge lies in accurately understanding user emotions from diverse data and presenting appropriate content in real time to make the user experience more engaging and stress-free. Furthermore, recommending content based on individual emotions is difficult, requiring flexible responses tailored to user feelings.

[0530] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0531] In this invention, the server includes means for pre-processing data for processing, means for analyzing data from multiple modalities and extracting feature information, and means for analyzing user sentiment information and presenting appropriate content. This enables immediate recommendation of optimal content according to the user's emotional state.

[0532] A "data acquisition device" is a mechanism that uses various sensors and input devices to collect diverse data such as audio, video, and text.

[0533] A "preprocessing device" is a mechanism that converts collected data into a format that is easy to process, and performs noise reduction and correction.

[0534] A "feature information extraction device" is a mechanism that finds important features from data across multiple modalities and processes them into a format that can be used for analysis.

[0535] A "device for determining the situation" is a mechanism that analyzes the relationships between data based on extracted features and grasps the current state.

[0536] A "device for generating action proposals" is a mechanism for formulating and presenting optimal action guidelines based on the assessed situation.

[0537] A "device that analyzes user emotional information and presents appropriate content" is a mechanism that analyzes changes in a user's emotions and selects and provides the most suitable entertainment and information accordingly.

[0538] The system implementing this invention utilizes an application running on a smartphone or wearable device to provide content recommendations based on the user's emotions.

[0539] The device acquires user data such as gaze, facial expressions, and voice through its camera and microphone. The data is acquired in real time, pre-processed, encrypted, and then sent to the server.

[0540] The server uses software such as TensorFlow and OpenCV to analyze emotions from facial expressions and voice. By removing noise in the preprocessing stage and analyzing the data comprehensively from multiple modalities, it accurately determines the user's emotional state.

[0541] Based on the emotional information obtained, the server uses an AI model to select and present appropriate content in real time. This entire process makes it possible to provide entertainment and information tailored to the user's current mood.

[0542] For example, if the camera captures a user's face while they are using their smartphone and detects that they are relaxing while listening to music, the system can use that information to suggest relaxing music or sounds with a sleep timer function.

[0543] An example of a prompt to input into a generative AI model is, "Please recommend music that is best suited for when the user is relaxing."

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The device uses the smartphone's camera and microphone to capture the user's gaze, facial expressions, and voice data. This data is immediately and temporarily stored on the device. The acquired data serves as basic material for inferring the user's emotional state.

[0547] Step 2:

[0548] The device preprocesses the acquired gaze, facial expression, and audio data. Specifically, it performs noise reduction and synchronizes the video and audio. As a result, data that can be analyzed is generated. The preprocessed data is encrypted and sent to the server.

[0549] Step 3:

[0550] The server uses OpenCV to analyze facial expressions from received data and determines the user's emotions. The input data is video information, and the output is an estimated result of the emotional state. The server uses TensorFlow to analyze audio data and extract supplementary emotional information from factors such as voice tone and speed.

[0551] Step 4:

[0552] The server uses an emotion engine to comprehensively analyze emotion estimation results from multiple modalities. It receives emotion estimation results from facial expressions and emotion information from speech as input, and outputs an integrated emotion judgment. This allows for a precise determination of the user's current emotional state.

[0553] Step 5:

[0554] The server uses a generative AI model based on the sentiment assessment results to generate prompt messages and select the most appropriate content for the user. The output is specific content such as music or video. This selected content is immediately sent to the user's device.

[0555] Step 6:

[0556] Users receive selected content from the server on their devices and use it. This process allows users to experience entertainment that best suits their emotional state in real time.

[0557] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0558] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0559] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0563] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0564] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0565] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0566] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0567] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0568] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0569] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0570] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0571] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0572] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0573] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0574] The system of this invention utilizes multimodal AI technology to support rapid and accurate situational assessment at crime scenes. The system consists of a terminal, a server, and a user interface.

[0575] The terminal plays a role in acquiring data at the crime scene. Specifically, it collects video data using a camera and audio data using a microphone. It also collects text information entered by investigators at the scene, integrating necessary information according to the situation. This data is encrypted and securely transmitted to the server.

[0576] The server plays a central role in the integrated analysis of data from multiple modalities. Preprocessing of received data includes noise reduction and format conversion, preparing the data for analysis. In video analysis, it recognizes objects and people, and in audio analysis, it transcribes speech through speech recognition. Furthermore, natural language processing extracts keywords and emotions from the text data. Based on these analysis results, the system assesses the situation, organizes relevant information, and provides guidance for communicating it to investigators.

[0577] Users can receive action suggestions sent from the server and instantly obtain information through the interface. These suggestions include specific action examples, characteristics of the target individual, and safe travel routes. Based on this information, users can decide on their next course of action and take swift action.

[0578] As a concrete example, consider a situation where multiple suspicious individuals have been sighted at a crime scene.

[0579] The device records a wide range of surroundings with its camera and collects audio with its microphone.

[0580] The server identifies individuals with specific clothing or postures through video analysis and extracts potentially alarming statements from audio analysis.

[0581] Based on the analysis results, the server generates likely action suggestions, such as "Pursue the suspicious person in the direction of the south exit."

[0582] The user, the investigator, will then quickly take action and carry out the next steps accordingly.

[0583] Thus, by utilizing the system of the present invention, it is possible to streamline decision-making at the scene of an investigation and improve the efficiency of police activities.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The device collects video and audio data at the investigation scene. It captures surrounding video with its camera and records audio with its microphone. Text data entered by investigators is also captured by the device. The collected data is encrypted and sent to a server.

[0587] Step 2:

[0588] The server receives data sent from the terminal. The received data undergoes preprocessing such as noise reduction and format conversion. This results in video data being divided into frames, audio data being converted to text, and text data being formatted.

[0589] Step 3:

[0590] The server analyzes pre-processed data. It detects and recognizes objects and people from video data. It uses speech recognition technology to transcribe speech from audio data. Natural language processing is used to extract important information and emotions from the text data.

[0591] Step 4:

[0592] The server assesses the situation on-site based on the analysis results. It compares the analyzed information with past databases to identify abnormal patterns and suspects.

[0593] Step 5:

[0594] The server generates action suggestions based on the situation. These suggestions include information on possible routes and priority targets for investigation. These suggestions are intended to support the investigators' decision-making.

[0595] Step 6:

[0596] The terminal receives suggestions from the server and displays them to the investigator through the user interface. The investigator then takes swift action based on these suggestions and carries out the next investigative steps.

[0597] (Example 1)

[0598] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] In investigative settings, it is crucial to quickly and accurately assess the situation and provide appropriate instructions. However, traditional methods struggle to integrate and analyze multiple data formats, resulting in a lack of mechanisms to support accurate decision-making. To address this challenge, there is a need for a system that performs advanced data analysis and efficiently delivers information.

[0600] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0601] In this invention, the server includes a device for encrypting data for transmission, a device for preprocessing data, and a device for analyzing data in multiple formats and extracting features. This enables secure data transmission, improved data quality through noise reduction, and advanced situational judgment using multiple data formats.

[0602] "Data collection equipment" refers to hardware used to acquire video, audio, and text information at the scene of an investigation.

[0603] An "encryption device" refers to a system that uses encryption technology to transform acquired data in order to protect it from unauthorized access.

[0604] A "pre-processing device" is a device that performs processing to remove noise from received data and convert it into a unified data format.

[0605] A "device for analyzing data in multiple formats" refers to a system that simultaneously analyzes data in different formats, such as video and audio, to extract useful information.

[0606] A "feature extraction device" is a device that executes algorithms to identify important elements and information from the data being analyzed.

[0607] A "device for judging a situation" refers to a device that analyzes the current situation based on extracted characteristics and derives an appropriate judgment.

[0608] A "device for generating action suggestions" refers to a system that, based on the assessed situation, specifically presents the next actions or measures that should be taken.

[0609] "Providing devices" refers to devices that visually or audibly communicate generated action suggestions to the user.

[0610] This invention is a system for streamlining information gathering and situational assessment at crime scenes. The system mainly consists of terminals, servers, and users, each playing a specific role.

[0611] The terminal is a device used to collect data at the scene of an investigation. It uses a camera and microphone to capture video and audio. Manual text input by the user is also possible. The collected data is encrypted and securely transmitted to a server. Encryption is crucial to maintain the confidentiality and integrity of the data.

[0612] The server plays a central role in data analysis. Received data is first preprocessed, undergoing noise reduction and format conversion. Video data is analyzed using object detection algorithms (e.g., general image analysis software), and audio data is converted to text using a speech recognition API. Keywords and emotions are extracted from the data using natural language processing techniques (e.g., text analysis libraries). Furthermore, a generative AI model is used to generate action suggestions based on the analysis results.

[0613] Users receive suggested actions through an interface. The interface visually presents information and supports decision-making. These suggestions include appropriate countermeasures, points to note, and recommended routes. Based on this, users can make quick decisions and conduct efficient field activities.

[0614] As a concrete example, consider a police investigation scene where numerous suspicious individuals have been sighted. The terminal records the surrounding environment with its camera and captures audio with its microphone. The server identifies individuals with distinctive clothing or mannerisms through video analysis and detects potentially alarming statements through audio analysis. Based on the analysis results, a suggestion is generated: "Pursue the suspicious individuals in the direction of the south exit." The investigators follow this suggestion and begin responding quickly.

[0615] As an example of a prompt to the generating AI model, the text "Generate action suggestions to assist in dealing with suspicious individuals at a crime scene" is used. This prompt allows the AI ​​model to derive the optimal course of action.

[0616] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0617] Step 1:

[0618] The terminal is responsible for collecting data at the crime scene. It handles video and audio data acquired from cameras and microphones as input. In addition, it accepts text information manually entered by investigators. The acquired data is encrypted using an encryption algorithm and sent to the server in a secure format as output.

[0619] Step 2:

[0620] The server receives encrypted data from the terminal. Inputs include video data, audio data, and text data. The server denoises this data and converts the video data to a standard format. For audio data, it performs format conversion and cleanup to create an analyzable data format as output.

[0621] Step 3:

[0622] The server analyzes the pre-processed data. For video data, an object detection algorithm is applied to identify people and objects. Audio data is converted to text using speech recognition software. This allows the multimodal data received as input to be output as information possessing specific characteristics of people and objects.

[0623] Step 4:

[0624] The server uses a generative AI model based on the analysis results to generate action suggestions. Based on the analyzed video, audio, and text data as input, the generative AI model receives an appropriate prompt (e.g., "Generate the best course of action in this situation") and creates specific action suggestions as output.

[0625] Step 5:

[0626] The user receives action suggestions from the server through an interface. Based on the action suggestions provided as input, the user confirms the specific instructions and then takes action based on them as output. The suggestions include travel routes and precautions, and the user quickly takes the next step according to them.

[0627] (Application Example 1)

[0628] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] When conducting security monitoring and safety management on-site, it is essential to quickly detect suspicious individuals and dangerous situations and respond immediately. However, current systems do not process information in real time, leading to delays in situation assessment and subsequent responses. This delay is a major obstacle to ensuring safety on-site, and the development of technologies that enable effective and rapid responses is highly desirable.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0631] In this invention, the server includes means for extracting features from video and audio in real time, means for generating warnings and instructions based on situational judgment, and means for immediately transmitting this information to the user. This enables rapid and accurate security response on-site, ensuring safety.

[0632] "Data" refers to information such as video, audio, and text acquired from the monitored location.

[0633] A "device" is a set of hardware or software components designed to perform a specific function.

[0634] "Information format" refers to the various forms that data can take, including, for example, video, audio, and text.

[0635] "Features" are important patterns and indicators derived from analyzed data, and are used for situational assessment.

[0636] "Situation assessment" is the process of evaluating the current on-site situation based on acquired characteristics and deciding on appropriate responses.

[0637] An "action proposal" is a set of guidelines outlining specific actions to be taken on-site, based on the results of a situation assessment.

[0638] "Users" refer to those who receive information through the system, and in this context, primarily security staff.

[0639] A "warning" is an alert sent to the user to inform them of a potential danger detected by the system.

[0640] The system that implements this application supports rapid decision-making by leveraging advanced data analysis technologies to enhance security. The system mainly consists of terminals, servers, and users.

[0641] The terminal is responsible for acquiring data in the field. Specifically, it uses a camera to capture video of the surroundings and a microphone to collect audio. This data is securely transmitted to the server using end-to-end encryption technology.

[0642] The server plays a central role in comprehensively analyzing the received data. Video data is identified using an object detection API to identify people and specific objects, and audio data is converted to text using a speech recognition API. Furthermore, relevant keywords are extracted from the text by utilizing a natural language processing library. Based on these analysis results, a generative AI model assesses the situation and generates necessary action guidelines.

[0643] Security staff, as users, can utilize the information transmitted from the server and take swift and appropriate action through the interface. For example, if suspicious activity is detected in a specific area, they can immediately direct their attention to that area.

[0644] As a concrete example, consider a situation in a commercial facility where a specific individual is exhibiting abnormal behavior. The terminal records the individual's movements on video and detects surrounding sounds. The server analyzes this data and, if abnormal behavior is detected, generates a suspicious behavior alert and sends it to the user to promote safety measures.

[0645] An example of a prompt is provided: "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." Based on this prompt, the generating AI model performs appropriate analysis and provides useful results.

[0646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0647] Step 1:

[0648] The terminal acquires video data using on-site cameras and collects audio data using microphones. This data is converted into a digital format as input. The converted data is then transmitted to the server in a secure manner via end-to-end encryption technology.

[0649] Step 2:

[0650] The server performs noise reduction and format conversion on the received video data. This process extracts video data that can be analyzed. Next, an object detection API is used to perform image analysis and identify people and objects in the video. Information about the identified people and objects is obtained as output.

[0651] Step 3:

[0652] The server uses a speech recognition API to convert received audio data into text. This process analyzes the audio file and outputs the corresponding text data. Furthermore, it performs noise reduction processing to extract clear text information.

[0653] Step 4:

[0654] The server utilizes a generative AI model to comprehensively assess the current situation based on the analysis results of video and audio. In this process, it extracts relevant keywords using a natural language processing library and analyzes the data. This generates action suggestions that are appropriate to the situation.

[0655] Step 5:

[0656] The server sends generated action suggestions to the user. These suggestions may include, for example, warnings about suspicious individuals or alerts to specific areas. The prompt message is formatted as follows: "Detect anomalies from video and audio data near the entrance and formulate appropriate response actions." The action suggestions are then notified to the user.

[0657] Step 6:

[0658] Users review the action suggestions received via their devices and take appropriate actions on-site according to the instructions. Specific actions include checking for proximity to suspicious individuals and strengthening security in specific areas. This feedback loop enables effective real-time security management.

[0659] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0660] The system of this invention is an advanced investigative support system that incorporates an emotion engine that recognizes user emotions, in addition to conventional data acquisition and analysis functions. This system consists mainly of a terminal, a server, and a user interface.

[0661] The device collects data needed at the investigation scene from various sources. Specifically, it captures video with its camera and records audio with its microphone. In addition, it collects text information entered by the user at the scene, as well as data on the user's gaze and facial expressions. This data is encrypted and securely transmitted to the server.

[0662] The server comprehensively analyzes the received data. Individual analyses are performed for each modality—video, audio, and text—while the emotion engine processes the data to recognize emotions. Video data analysis recognizes individuals and objects, while audio data analysis transcribes speech and performs emotion recognition. From text-based data, emotions are extracted based on the frequency and context of specific words. Based on these various analysis results, the system makes situational judgments on-site.

[0663] The user receives action suggestions from the server through an interface. These suggestions are presented as specific instructions for action appropriate to the situation. Furthermore, suggestions are tailored to the user's psychological state based on emotion recognition results, thereby reducing stress and facilitating quick decision-making.

[0664] As a concrete example, let me give a situation from a certain investigation scene.

[0665] The terminal collects diverse data under the control of investigators at the scene and immediately transmits all data, including captured video and recorded audio, to the server.

[0666] The server identifies individual emotions from the tone and content of speech through voice analysis, and grasps emotions from changes in facial expressions and posture through video analysis. These common emotional patterns are analyzed by an emotion engine and reflected in the situational judgment.

[0667] The user, acting as the investigator, initiates action based on the judgment obtained and takes the optimal course of action to achieve the proposed objective.

[0668] Thus, by using the present invention system incorporating an emotion engine, the efficiency of investigations can be significantly improved, and detailed responses tailored to the situation can be made possible.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The device acquires multimodal data on-site. It collects surrounding video using a camera and records ambient sounds with a microphone. In addition, it collects text information obtained from user input, as well as user facial expressions and gaze data. The acquired data is encrypted and sent to the server.

[0672] Step 2:

[0673] The server receives data sent from the terminal and performs preprocessing. It performs noise reduction and frame splitting on video data, and filtering and text conversion on audio data. Keyword extraction and formatting are performed on the text data.

[0674] Step 3:

[0675] The server analyzes multiple modalities. In video analysis, facial recognition technology is used to identify individuals and determine emotions from their facial expressions. In audio analysis, emotions are extracted from spoken content, and additional emotional information is obtained through natural language processing. The emotion engine integrates these analysis results to identify the user's overall emotional state.

[0676] Step 4:

[0677] The server integrates the analysis results to make a situation assessment. By comparing them with past emotional data and incident patterns stored in the database, it identifies abnormal emotions and important trends. This assessment is performed in real time, deepening the understanding of the situation on the scene.

[0678] Step 5:

[0679] The server generates action suggestions that incorporate emotion recognition. Considering the user's current psychological state, it provides suggestions to reduce the user's burden and offers situation-appropriate action plans. This information assists investigators in making appropriate decisions.

[0680] Step 6:

[0681] The terminal receives the generated action suggestions and displays them to the user through the user interface. The suggestions are presented in a visually clear manner, allowing investigators to quickly recognize them and initiate appropriate actions.

[0682] (Example 2)

[0683] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0684] With the recent advancements in information technology, there is a growing need to quickly and accurately analyze diverse data at the crime scene and, based on that analysis, provide appropriate action suggestions tailored to the situation. However, current systems struggle to provide action suggestions that comprehensively capture the user's emotions, resulting in a lack of efficiency and flexibility in investigations. In particular, there is a need for emotion analysis that takes nonverbal elements such as eye gaze and facial expressions into consideration, and the provision of immediate action instructions based on the results.

[0685] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0686] In this invention, the server includes means for acquiring data including the user's gaze and facial expressions, means for encrypting and transmitting the data using the SSL / TLS protocol, and means for analyzing the data for each modality of video, audio, and text, and extracting emotions. This makes it possible to comprehensively analyze diverse data obtained at the scene of an investigation and immediately provide the user with accurate and flexible action suggestions based on prompt sentences using a generated AI model.

[0687] "Data including user gaze and facial expressions" refers to non-verbal information that records the user's eye movements and changes in facial expressions, and is an element used to analyze the user's emotions and psychological state.

[0688] The SSL / TLS protocol is an encryption protocol used to ensure security in data communication and protects the confidentiality and integrity of data.

[0689] "Analyzing data for each modality—video, audio, and text—means performing individual analyses on each of the three different data formats—video, audio, and text—and extracting information by leveraging the characteristics of each format.

[0690] "Emotion extraction" is the process of analysis performed to identify the emotional state of a user or subject based on collected data.

[0691] A "generative AI model" is an artificial intelligence model designed to automatically generate appropriate solutions or suggestions based on specific input information.

[0692] A "prompt statement" is an input statement that provides specific instructions or context to a generative AI model, and is used to guide the model's output.

[0693] The system of this invention includes a user, a terminal, and a server, and focuses particularly on emotion analysis and behavioral suggestions in investigative settings.

[0694] The terminal is deployed at the investigation site and collects data through various sensors and interfaces. Specifically, it acquires video using a camera and records audio using a microphone. In addition, eye-tracking sensors and facial recognition cameras collect data including the user's gaze and facial expressions. Text data is obtained from information entered by the user and notes recorded at the scene. This data is encrypted via the SSL / TLS protocol and securely transmitted to the server.

[0695] The server analyzes data from various modalities. Video data incorporates computer vision technology for facial recognition and object detection. Audio data is converted to text using a speech recognition engine, and emotions are extracted through tone analysis. Text data uses natural language processing technology to analyze emotions from sentence structure and word frequency. The emotional information extracted through these processes is integrated by an emotion engine and used for comprehensive situational judgment.

[0696] Subsequently, the server utilizes the generated AI model to construct prompt sentences based on the analysis results and generate action suggestions. These prompt sentences play a role in providing specific instructions to the AI ​​model and outputting the optimal action scenario. The generated action suggestions take into account the user's psychological state, promoting stress reduction and rapid decision-making.

[0697] Users receive these action suggestions sent from the server through an interface. The interface explicitly displays the action suggestions, making it easier for users to select the most appropriate response for the situation. For example, if a user, acting as an investigator, is conducting an interview, they might receive suggestions based on analysis such as, "The subject is stressed. Please soften your questioning tone to create a calmer atmosphere," enabling them to respond effectively in a situation-appropriate manner.

[0698] As an example of a prompt, the AI ​​model can be instructed to "Generate action suggestions based on the investigator's psychological state while tracking the progress at the scene in real time. However, ensure that all emotions and situations are handled safely and appropriately," which will generate appropriate action suggestions for the user.

[0699] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0700] Step 1:

[0701] The device collects diverse data from users in real time at the crime scene. Specifically, it acquires video with a camera and records audio using a microphone. In addition, an eye-tracking sensor captures the user's gaze data, and a facial recognition camera detects changes in facial expressions. This input data includes raw video, audio, gaze, and facial expression data. The collected data is encrypted using the SSL / TLS protocol and transmitted to the server via a secure channel.

[0702] Step 2:

[0703] The server receives various data transmitted from the terminal and begins the analysis process. The inputs received are encrypted video, audio, gaze, and facial expression data. The server first analyzes the video data using computer vision technology and outputs metadata for recognized faces and objects. Audio data is converted to text by a speech recognition engine, and the tone of speech is further extracted through acoustic analysis. Gaze and facial expression data are used by an emotion engine to identify the user's emotional state. The output of this process is emotion information and extracted feature data.

[0704] Step 3:

[0705] The server generates action suggestions using a generative AI model based on integrated sentiment information and feature data. The input consists of the analyzed sentiment information and feature data. Prompt sentences are constructed and provided to the AI ​​model, automatically generating situation-appropriate action instructions. The output is a specific action suggestion. For example, it might generate something like, "The survey participants appear restless. We recommend increasing the interval between questions."

[0706] Step 4:

[0707] Users receive action suggestions from the server through a dedicated interface. The input is the generated action suggestions. The interface visually presents these suggestions to the user, allowing the user to decide on a course of action at the investigation scene based on this information. The final output is the action chosen by the user, which improves investigation efficiency and enables rapid decision-making.

[0708] (Application Example 2)

[0709] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0710] The challenge lies in accurately understanding user emotions from diverse data and presenting appropriate content in real time to make the user experience more engaging and stress-free. Furthermore, recommending content based on individual emotions is difficult, requiring flexible responses tailored to user feelings.

[0711] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0712] In this invention, the server includes means for pre-processing data for processing, means for analyzing data from multiple modalities and extracting feature information, and means for analyzing user sentiment information and presenting appropriate content. This enables immediate recommendation of optimal content according to the user's emotional state.

[0713] A "data acquisition device" is a mechanism that uses various sensors and input devices to collect diverse data such as audio, video, and text.

[0714] A "preprocessing device" is a mechanism that converts collected data into a format that is easy to process, and performs noise reduction and correction.

[0715] A "feature information extraction device" is a mechanism that finds important features from data across multiple modalities and processes them into a format that can be used for analysis.

[0716] A "device for determining the situation" is a mechanism that analyzes the relationships between data based on extracted features and grasps the current state.

[0717] A "device for generating action proposals" is a mechanism for formulating and presenting optimal action guidelines based on the assessed situation.

[0718] A "device that analyzes user emotional information and presents appropriate content" is a mechanism that analyzes changes in a user's emotions and selects and provides the most suitable entertainment and information accordingly.

[0719] The system implementing this invention utilizes an application running on a smartphone or wearable device to provide content recommendations based on the user's emotions.

[0720] The device acquires user data such as gaze, facial expressions, and voice through its camera and microphone. The data is acquired in real time, pre-processed, encrypted, and then sent to the server.

[0721] The server uses software such as TensorFlow and OpenCV to analyze emotions from facial expressions and voice. By removing noise in the preprocessing stage and analyzing the data comprehensively from multiple modalities, it accurately determines the user's emotional state.

[0722] Based on the emotional information obtained, the server uses an AI model to select and present appropriate content in real time. This entire process makes it possible to provide entertainment and information tailored to the user's current mood.

[0723] For example, if the camera captures a user's face while they are using their smartphone and detects that they are relaxing while listening to music, the system can use that information to suggest relaxing music or sounds with a sleep timer function.

[0724] An example of a prompt to input into a generative AI model is, "Please recommend music that is best suited for when the user is relaxing."

[0725] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0726] Step 1:

[0727] The device uses the smartphone's camera and microphone to capture the user's gaze, facial expressions, and voice data. This data is immediately and temporarily stored on the device. The acquired data serves as basic material for inferring the user's emotional state.

[0728] Step 2:

[0729] The device preprocesses the acquired gaze, facial expression, and audio data. Specifically, it performs noise reduction and synchronizes the video and audio. As a result, data that can be analyzed is generated. The preprocessed data is encrypted and sent to the server.

[0730] Step 3:

[0731] The server uses OpenCV to analyze facial expressions from received data and determines the user's emotions. The input data is video information, and the output is an estimated result of the emotional state. The server uses TensorFlow to analyze audio data and extract supplementary emotional information from factors such as voice tone and speed.

[0732] Step 4:

[0733] The server uses an emotion engine to comprehensively analyze emotion estimation results from multiple modalities. It receives emotion estimation results from facial expressions and emotion information from speech as input, and outputs an integrated emotion judgment. This allows for a precise determination of the user's current emotional state.

[0734] Step 5:

[0735] The server uses a generative AI model based on the sentiment assessment results to generate prompt messages and select the most appropriate content for the user. The output is specific content such as music or video. This selected content is immediately sent to the user's device.

[0736] Step 6:

[0737] Users receive selected content from the server on their devices and use it. This process allows users to experience entertainment that best suits their emotional state in real time.

[0738] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0739] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0740] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0741] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0742] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0743] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0744] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0745] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0746] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0747] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0748] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0749] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0750] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0751] 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.

[0752] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0753] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0754] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0755] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0756] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0757] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0758] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] Means of acquiring data,

[0762] Means for performing preprocessing to process the aforementioned data,

[0763] A means of analyzing data from multiple modalities and extracting features,

[0764] A means for determining the situation based on the aforementioned characteristics,

[0765] Means for generating action proposals based on the aforementioned judgment results,

[0766] A system including means for presenting the aforementioned proposal to a user.

[0767] (Claim 2)

[0768] The system according to claim 1, further comprising means for analyzing audio data and performing speech recognition.

[0769] (Claim 3)

[0770] The system according to claim 1, further comprising means for analyzing video data and recognizing people and objects.

[0771] "Example 1"

[0772] (Claim 1)

[0773] A device for collecting data,

[0774] A device that encrypts the data in order to transmit it,

[0775] A device for performing preprocessing to process the aforementioned data,

[0776] A device that analyzes data in multiple formats and extracts features,

[0777] A device for determining the situation based on the aforementioned characteristics,

[0778] A device that generates action proposals based on the aforementioned judgment results,

[0779] A system including a device for presenting the aforementioned proposal to a user.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising a device for analyzing voice information and performing voice recognition.

[0782] (Claim 3)

[0783] The system according to claim 1, further comprising a device for analyzing video information and recognizing people and objects.

[0784] "Application Example 1"

[0785] (Claim 1)

[0786] A device for acquiring data,

[0787] A device for performing preprocessing to process the aforementioned data,

[0788] A device that analyzes data in multiple information formats and extracts features,

[0789] A device for determining the situation based on the aforementioned characteristics,

[0790] A device that generates action proposals based on the aforementioned judgment results,

[0791] A device for presenting the aforementioned proposal to the user,

[0792] A system including a device that generates real-time warnings in response to detected situations and notifies users.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising a device for analyzing audio data and performing speech recognition.

[0795] (Claim 3)

[0796] The system according to claim 1, further comprising a device for analyzing visual data and recognizing people and objects.

[0797] "Example 2 of combining an emotion engine"

[0798] (Claim 1)

[0799] A means of acquiring data including the user's gaze and facial expressions,

[0800] A means for encrypting and transmitting the data using the SSL / TLS protocol,

[0801] A method for analyzing data for each modality—video, audio, and text—and extracting emotions,

[0802] Means of generating,

[0803] A means of generating action suggestions based on prompt sentences using a generative AI model,

[0804] A system including means for presenting the proposal to the user.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for extracting emotions from voice tone and language patterns.

[0807] (Claim 3)

[0808] The system according to claim 1, further comprising means for performing video analysis using facial recognition technology.

[0809] "Application example 2 when combining with an emotional engine"

[0810] (Claim 1)

[0811] A device for acquiring data,

[0812] A device for performing preprocessing to process the aforementioned data,

[0813] A device that analyzes data from multiple modalities and extracts feature information,

[0814] A device that determines the situation based on the aforementioned characteristic information,

[0815] A device that generates action proposals based on the aforementioned judgment results,

[0816] A device for presenting the aforementioned proposal to the user,

[0817] A system that includes a device that analyzes user sentiment information and presents appropriate content.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising a device for analyzing audio data and performing speech recognition.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising a device for analyzing video data and identifying people and objects. [Explanation of Symbols]

[0822] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring data, Means for performing preprocessing to process the aforementioned data, A means of analyzing data from multiple modalities and extracting features, A means for determining the situation based on the aforementioned characteristics, Means for generating action proposals based on the aforementioned judgment results, A system including means for presenting the aforementioned proposal to a user.

2. The system according to claim 1, further comprising means for analyzing audio data and performing speech recognition.

3. The system according to claim 1, further comprising means for analyzing video data and recognizing people and objects.

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