system

Abstract

We provide the system. [Solution] A device for analyzing visual and auditory information collected in the work environment, A device that identifies tasks that can be automated based on the aforementioned analysis, A device that proposes an optimal automation method for the aforementioned task, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] In the business processes within an enterprise, there are many repetitive tasks, and efficiently automating these is the key to improving productivity. However, currently, it is difficult to identify which tasks should be automated and how, and furthermore, it takes time and resources to select appropriate automation means. Also, there is not yet a sufficient mechanism to support a smooth introduction for promoting automation. It is necessary to provide a system for solving these problems and realizing the efficiency improvement and productivity improvement of business.

Means for Solving the Problems

[0005] This invention analyzes business processes in detail using a device that analyzes visual and auditory information acquired in the work environment. By including a device that identifies tasks that can be automated from the analyzed information, it efficiently extracts repetitive tasks. Furthermore, by including a device that proposes the most suitable automation means for the identified tasks, it effectively utilizes resources and supports the introduction of appropriate tools. This provides a system that can improve the overall efficiency and productivity of business operations.

[0006] "Visual information" refers to video data collected in the work environment using visual devices such as cameras.

[0007] "Acoustic information" refers to audio data acquired in a work environment using audio collection devices such as microphones.

[0008] An "analysis device" is a device that processes collected data and extracts important information and patterns from business processes.

[0009] "Automable tasks" are tasks within a business process that involve repetitive and patterned procedures and can be performed mechanically, either partially or entirely.

[0010] "Means of automation" refer to appropriate tools and methods for performing tasks that you want to automate, and include, for example, RPA (Robotic Process Automation) and chatbots.

[0011] The "proposed device" is a device that selects the optimal means for automating tasks based on the analysis results and notifies the user. [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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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] This invention provides an advanced analysis system that utilizes visual and acoustic information for the purpose of improving the efficiency of business processes. This system collects visual and acoustic information using devices installed in the work environment, processes this information with an analysis device to identify tasks that can be automated in the work, and then proposes the most suitable automation method to the user.

[0034] Specifically, terminals placed in the work environment use cameras and microphones to collect video and audio during work processes. This allows for the acquisition of visible work procedures and voice instructions as digital data. The collected data is transmitted to a server via the network, where detailed analysis is performed in the next step.

[0035] The server divides visual information frame by frame and analyzes human actions and changes on the screen. For acoustic information, speech recognition technology is used to transcribe it into text, and machine learning algorithms are used to extract important phrases and keywords. This makes it possible to identify areas within the overall business process that are suitable for automation, such as repetitive tasks.

[0036] Next, the server selects the optimal automation method based on past data for the identified automatable tasks and generates a proposal. For example, if analysis reveals that data entry is performed frequently, the use of an RPA tool will be recommended. If it is found that there are many customer inquiries, the introduction of an intelligent chatbot may be considered.

[0037] This recommendation information is provided to the user as a report. Based on the report, the user decides whether to implement the automation tool, and the server supports the implementation process. This includes providing tool configuration instructions and technical coaching during implementation.

[0038] As a concrete example, in one office environment, the content of meetings is routinely accumulated as audio data, and by analyzing this data, the task of creating meeting minutes can be automated. In another case, the operations on a manufacturing line are recorded as video data, allowing for the extraction of inefficient processes and the identification of processes that can be automated or made more efficient.

[0039] Thus, this system provides concrete means to support the streamlining of the entire business process and improve productivity through visual and acoustic analysis.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal uses cameras and microphones placed within the work environment to collect visual and auditory data in real time. The collected data is temporarily stored to streamline processing.

[0043] Step 2:

[0044] The data collected by the device is processed in batches at regular intervals and sent to the server via the secure upload system. The data is encrypted in a way that respects privacy.

[0045] Step 3:

[0046] The server divides the received video data into frames and applies image recognition algorithms to identify human movements, operations, and changes in the screen. At this point, process points are identified.

[0047] Step 4:

[0048] The server converts audio data into text using speech recognition technology, and then performs natural language processing on that text data. This allows for the extraction of important business-related instructions and key points of conversations.

[0049] Step 5:

[0050] The server integrates the analysis results from both video and audio and reviews the entire business process. Based on this review, it identifies repetitive tasks that can be automated and processes that have room for improvement.

[0051] Step 6:

[0052] Based on the identified tasks, the server selects the most suitable automation method. An AI algorithm, using past data, suggests the most effective method from among RPA tools, chatbots, and other options.

[0053] Step 7:

[0054] The server generates a report of automation suggestions and notifies the user. The user can receive the suggestions via email or a dedicated dashboard.

[0055] Step 8:

[0056] Once the user reviews the proposal and decides to implement the automation tool, the server provides detailed instructions and implementation support to ensure a smooth tool deployment.

[0057] Step 9:

[0058] The server re-collects and analyzes business process data after implementation, measures the impact of automation on productivity, and provides users with feedback to encourage continuous improvement.

[0059] (Example 1)

[0060] 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."

[0061] In today's business environment, there is a demand for the effective use of diverse data and the streamlining of business processes. However, conventional systems often lack the ability to adequately analyze visual and auditory information and identify tasks that can be automated. Furthermore, there is a challenge in proposing the optimal automation method based on these results and applying it to actual business operations.

[0062] 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.

[0063] In this invention, the server includes means for dividing visual information into frames for analyzing information collected in the work environment, means for converting acoustic information into text data using speech recognition technology, and means for extracting important data using machine learning algorithms. This makes it possible to identify tasks that can be automated based on visual and acoustic data and to propose suitable automation means.

[0064] "Work environment" refers to the physical or virtual location or situation in which work is performed within a company or organization.

[0065] "Information-collecting devices" refer to hardware devices installed in the work environment to acquire visual and auditory information.

[0066] "Visual information" refers to video data acquired from cameras, image sensors, and other sources.

[0067] "Acoustic information" refers to sound data acquired from microphones or similar audio input devices.

[0068] "Methods for dividing into frames" refer to analytical techniques for separating video information into a series of still images (frames).

[0069] "Speech recognition technology" refers to the technology that analyzes speech and converts it into text.

[0070] A "machine learning algorithm" refers to a computer program that learns specific patterns or rules from data.

[0071] "Automated tasks" refer to business tasks that are performed repetitively according to specific procedures and can therefore be automated by machines or software.

[0072] "Automation methods" refer to the means and technologies implemented to improve efficiency in business processes.

[0073] "Providing proposals as a report" means documenting analysis results and recommendations and communicating them to the user in a report format.

[0074] A "generative AI model" refers to an artificial intelligence program that generates new information or prompts based on data it has learned in advance.

[0075] This invention provides a system for improving the efficiency of processes in the work environment. Specific embodiments for carrying out the invention are described below.

[0076] The terminals are installed in the work environment and collect visual and acoustic information using high-resolution cameras and noise-canceling microphones. This allows for the acquisition of digital information of meetings, actions during work processes, and conversations.

[0077] The collected information is securely transmitted to the server via the network. Data security is ensured during this process using encryption technologies such as SSL / TLS.

[0078] The server utilizes advanced software to analyze the received data. Visual information is divided frame by frame, and machine learning algorithms are used to detect human movements and changes on the screen. For acoustic information, speech recognition technology is applied to convert speech to text, and natural language processing techniques are used to extract important phrases.

[0079] The analysis results are used to identify tasks that can be automated. For the identified processes, the server suggests the most suitable automation method, such as RPA tools or chatbots, and provides this to the user as a report. Based on this report, the user can decide which automation method to implement.

[0080] For example, this could involve automatically accumulating audio data of office meetings and using it to create meeting minutes, or analyzing video footage of manufacturing lines to identify inefficient processes. This would lead to overall streamlining of operations.

[0081] An example of a prompt message given to the AI ​​is, "Analyze meeting audio data in an office environment and suggest ways to streamline meeting minute creation."

[0082] This system provides concrete means to improve productivity by effectively utilizing visual and auditory information to help automate business processes.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The terminal uses a high-resolution camera and noise-canceling microphone installed in the work environment to collect visual and acoustic information. Real-time video and audio are captured as input. The output is video and audio files as digital data. At this time, the terminal automatically starts recording video and audio, saving all actions and conversations during work as digital content.

[0086] Step 2:

[0087] The terminal encrypts the collected video and audio files using a security protocol (e.g., SSL / TLS) and transmits them to the server over the network. The input is the untransmitted digital data collected in step 1, and the output is the secure, encrypted data transfer to the server. Data protection and transfer speed are paramount in this process.

[0088] Step 3:

[0089] The server divides the received visual information frame by frame and uses machine learning algorithms to analyze human actions and changes on the screen. The input is encrypted visual information, and the output is a list of structured behavioral patterns and a record of changes. To efficiently process the large amount of image data, the server performs the analysis using parallel processing.

[0090] Step 4:

[0091] The server converts acoustic information into text data through a speech recognition engine and extracts important phrases using natural language processing techniques. The input is encrypted audio information, and the output is transcribed conversation data and extracted key phrases. This step performs accurate and rapid speech-to-text conversion and analysis.

[0092] Step 5:

[0093] Based on the analysis results, the server identifies tasks that can be automated and proposes the most suitable automation methods. The input is the analysis data obtained in steps 3 and 4, and the output is a list of recommended automation methods. Here, statistical data and past cases are referred to to select the most effective solution.

[0094] Step 6:

[0095] The user receives reports from the server and considers implementing automation measures in their operations. The input is an automation suggestion report from the server, and the output is an action plan based on the user's decision. At this stage, the user develops a feasible strategy and prepares to move on to the next step.

[0096] (Application Example 1)

[0097] 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."

[0098] In manufacturing environments, the difficulty in identifying and proposing efficient work procedures is a challenge that delays the automation of business processes. In particular, a key challenge is how to quickly identify repetitive or inefficient processes performed manually and propose the most suitable automation methods.

[0099] 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.

[0100] In this invention, the server includes means for analyzing visual and acoustic information collected in the work environment, means for identifying tasks that can be automated based on the analysis, means for proposing the most suitable automation means for the tasks, means for identifying inefficient processes by having a monitoring device collect and analyze information in the manufacturing process, and means for supporting the efficiency of work processes based on the proposed automation means. This enables efficient automation of business processes in the manufacturing site.

[0101] "Work environment" refers to the place where work is performed, such as manufacturing processes or offices.

[0102] "Visual information" refers to video data collected by cameras and other image acquisition devices.

[0103] "Acoustic information" refers to audio data collected by microphones and other sound acquisition devices.

[0104] "Analysis methods" refer to the process of analyzing collected information to identify trends and specific patterns.

[0105] "Automated tasks" refer to tasks that can be completed mechanically or programmatically without requiring human intervention.

[0106] "Automation means" refers to technologies or devices introduced to perform specific tasks automatically.

[0107] An "inefficient process" refers to a manufacturing stage that requires excessive time or cost and therefore has room for improvement.

[0108] A "surveillance device" refers to a device installed to collect visual and auditory information.

[0109] "Efficiency improvement" refers to reducing time and resources in business processes and improving the productivity of operations.

[0110] The system implementing this invention consists of collecting visual and acoustic information using monitoring devices installed in the work environment, and analyzing this data on a central server. Terminals use cameras and microphones to acquire video and audio data of the work in real time. The collected data is transmitted to the server via the network.

[0111] The server uses Python, the machine learning library TENSORFLOW®, and the image processing library OpenCV to analyze visual information frame by frame and identify human movements and screen changes. For acoustic information, the Google® Cloud Speech-to-Text API is used to convert audio data into text data, which is then analyzed using natural language processing techniques to identify important phrases and instructions.

[0112] Based on the analysis results, the server identifies tasks that can be automated and recommends the optimal automation method for those tasks. This process uses a generative AI model to suggest methods for maximizing the efficiency of business processes. As a result, users can implement automation based on the suggested methods and improve work efficiency.

[0113] As a concrete example, in a food manufacturing plant, monitoring devices may patrol the bread production line, collecting and analyzing video and audio of the filling process to identify inefficient work and recommend the introduction of robots in those processes. In this way, successful automation has the potential to dramatically improve production efficiency.

[0114] An example of a prompt message would be: "The robot monitoring the equipment has identified the following inefficient tasks. Please perform a detailed data analysis and suggest the best automation solutions." Through this process, users can gain useful insights to improve their processes and increase productivity.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The device captures visual information of the work environment using a camera and records audio information using a microphone. The input at this stage is the video and audio of the actual work. The output is a data file of the visual and audio information converted into a digital format. Specifically, camera capture and microphone recording occur simultaneously.

[0118] Step 2:

[0119] The terminal transmits the acquired digital data to the server in real time via the network. The input is the digital data converted in step 1. The output is the data transferred in a format accessible to the server. Specifically, data transfer is performed using a communication protocol (e.g., TCP / IP).

[0120] Step 3:

[0121] The server divides the received visual information into frames and analyzes each frame using OpenCV. The input is the visual data sent from the terminal. The output is the analysis result that identifies patterns and changes in motion. Specifically, frame-by-frame processing and motion detection / identification are performed.

[0122] Step 4:

[0123] The server converts acoustic information into text data using the Google Cloud Speech-to-Text API and extracts important phrases and commands through natural language processing. The input is acoustic data sent from the terminal. The output is the analyzed text data and the extracted important phrases. Specifically, the process involves transcribing audio data into text and extracting keywords.

[0124] Step 5:

[0125] The server combines the frame analysis results and the speech analysis results to identify tasks that can be automated using a machine learning model. The input is the analysis results obtained from steps 3 and 4. The output is a list of tasks that can be automated. Specific actions include analysis and prediction by a generative AI model.

[0126] Step 6:

[0127] The server prompts the user with the optimal automation method for the identified automatable tasks. The input is the task list obtained in step 5. The output is a user-readable suggestion report. Specific actions include suggestion generation and prompt creation based on data analysis.

[0128] 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.

[0129] This invention enables more effective automation suggestions by combining a system that uses visual and auditory information for analyzing business processes with an emotion engine that recognizes user emotions. This system is realized by collecting data from both visual and auditory perspectives using devices installed in the work environment.

[0130] First, the terminal collects video and audio in real time during work using cameras and microphones placed in the office environment. This data is temporarily stored and then sent to a server for further processing.

[0131] The server divides the received video data frame by frame and uses image analysis technology to analyze the specific actions involved in the work. Simultaneously, it converts audio data into text using speech recognition technology and analyzes the content using natural language processing technology. This analysis clarifies important instructions and repetitive steps in the business process.

[0132] Furthermore, the server's emotion engine analyzes the user's voice and facial expressions to recognize their emotions. Based on this information, stress points within the work environment and the user's emotional triggers can be identified and reflected in automation suggestions. For example, if it is recognized that a user is experiencing stress while performing a particular task, automating or improving that task will be prioritized.

[0133] The analysis results are presented to the user in the form of a report that includes optimized suggestions for automation. This allows the user to consider adopting specific measures that can help improve their business processes. If the user approves the suggested automation measures, the system will assist with their implementation and support their operation in actual business operations.

[0134] As a concrete example, considering the case of a customer support center, an emotion engine analyzes the voice and video of employees during interactions, and automated tools such as optimizing response scripts and utilizing chatbots are proposed to reduce the burden incurred during conversations with customers.

[0135] Thus, by incorporating an emotion engine, the present invention provides a system that enables personalized automation suggestions that take into account the user's emotional state, thereby achieving further improvements in operational efficiency.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The terminal uses cameras and microphones installed within the office to collect visual and auditory information about work activities in real time. The collected data is temporarily stored on the terminal.

[0139] Step 2:

[0140] The device periodically compresses the data it collects and securely uploads it to the server. The uploaded data is encrypted and protected using a secure method.

[0141] Step 3:

[0142] The server divides the visual data frame by frame and uses image recognition technology to analyze the business process. This analysis identifies and digitizes human movements and screen operations.

[0143] Step 4:

[0144] The server converts the audio data into text using speech recognition technology and analyzes it through natural language processing algorithms. This extracts the key points of conversations and instructions related to the work.

[0145] Step 5:

[0146] The server uses an emotion engine to analyze the user's emotional state from audio and video data. It identifies emotions from facial expression analysis and voice tone, and determines the user's stress level and satisfaction level.

[0147] Step 6:

[0148] The server integrates the analysis results and identifies tasks and processes that can and need to be automated. Prioritizing automation targets is selected while considering the user's emotional state.

[0149] Step 7:

[0150] The server selects the most suitable automation method for the identified task and creates a proposal. Based on AI analysis, tools such as RPA tools and chatbots are selected and customized according to the user's emotional state.

[0151] Step 8:

[0152] The server generates automation suggestions in report format and notifies the user. The user reviews the suggestions via email or a dashboard and receives detailed explanations as needed.

[0153] Step 9:

[0154] Once the user reviews the proposal and decides to implement the automation tool, the server provides support for the implementation. This includes installation guides and assistance with initial setup.

[0155] Step 10:

[0156] To measure the effectiveness of the system after the server was implemented, data will be collected and analyzed again. This analysis will confirm improvements in productivity and reductions in workload, and continuous improvement measures will be fed back to the users.

[0157] (Example 2)

[0158] 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".

[0159] In the workplace, a challenge when promoting automation to improve work efficiency is that conventional systems fail to adequately consider the context of the work and the emotions of the users. As a result, proposed automation methods may not lead to improvements in the overall business process. This invention aims to provide more practical and effective automation proposals by comprehensively analyzing visual and auditory information and further taking into account the emotional state of the user.

[0160] 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.

[0161] In this invention, the server includes means for analyzing video and audio data collected in the work environment, means for identifying automatable work procedures and generating optimization suggestions, and means for analyzing the user's voice and facial expressions to recognize their emotional state and reflect the results in the automation suggestions. This makes it possible to provide specific and effective automation suggestions that promote the improvement of business processes.

[0162] "Video data" refers to visual information recorded in a digital format within a work environment.

[0163] "Audio data" refers to acoustic information recorded in a digital format within a work environment.

[0164] "Analysis" refers to processing the obtained data, extracting information related to business processes, and interpreting it.

[0165] "Automable work procedures" are parts of a task that can be automated or made more efficient by having a system intervene to reduce manual work.

[0166] An "optimization suggestion" is a specific method or means that the system suggests to improve the efficiency of operations based on the analysis results.

[0167] "Analysis of voice and facial expressions" involves analyzing characteristics such as tone of sound and facial movements to infer emotions and states of mind.

[0168] "Emotional state" refers to the user's psychological or emotional state, including factors that contribute to workload and stress in their work.

[0169] A "report" is a document that organizes the analysis results and optimization suggestions and provides them to the user.

[0170] This invention is a system that analyzes video and audio data to generate automated suggestions that take into account the user's emotional state, with the aim of improving the efficiency of work in the work environment.

[0171] The terminal uses cameras and microphones installed in the office environment to capture video and audio in real time during work. The cameras are positioned to cover a wide area of ​​the work area, and the microphones are positioned to effectively collect audio. The collected data is encoded and temporarily stored on the terminal.

[0172] The server receives video data transmitted from the terminal and analyzes it frame by frame using image analysis libraries such as "OpenCV". Similarly, audio data is converted into text data using speech recognition software such as "Google Speech-to-Text". The converted text data is then processed using the "NLTK" library to extract important instructions and rules related to the business.

[0173] The server also analyzes the user's voice tone and facial expressions, and uses an emotion engine to recognize the user's emotional state. This analysis utilizes tools such as "Microsoft® Azure® Face API" and "IBM Watson® Tone Analyzer." Based on this information, automated suggestions are optimized to reduce the user's psychological burden.

[0174] The generated automation suggestions are notified to the user in report format. Based on this, the user can decide whether to adopt the suggested automation methods into their business processes. If the suggestions provided by the system are approved, the system will assist in their implementation and support improvements in business efficiency.

[0175] A concrete example is the automation tools proposed to reduce the burden on employees in customer support centers. This could include optimizing response scripts or introducing chatbots. This reduces the workload on employees and improves the efficiency of customer service.

[0176] An example of an input prompt for the generating AI model is: "To improve the operational efficiency of the customer support center, perform an emotional analysis to determine which tasks employees find stressful and propose solutions for improvement."

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The terminal collects video and audio data using cameras and microphones in the work environment. The input is real-time video and audio, which are stored on the terminal as temporary files in digital format. After storage, the data is transmitted to the server via a secure network.

[0180] Step 2:

[0181] The server receives video data transmitted from the terminal and divides it into frames using the "OpenCV" library. The input is the received video data, and the output is the analyzed motion information. It detects specific actions, such as "checking documents" or "computer operation," and generates an action log based on these actions.

[0182] Step 3:

[0183] The server converts audio data into text data using tools such as "Google Speech-to-Text." The input is audio data, and the output is conversation information in text format. This text data is then processed using "NLTK" to extract business procedures and important communication content.

[0184] Step 4:

[0185] The server uses an emotion engine to analyze the user's voice tone and facial expression data. The input is voice tone and facial expression information in the video, and the output is the user's emotional state. This uses "Microsoft Azure Face API" and "IBM Watson Tone Analyzer" to identify the user's emotional trigger points.

[0186] Step 5:

[0187] The server generates automation suggestions based on the analysis results to date. Inputs include action logs, conversation content, and emotional state data, while output is an optimized automation suggestion that includes improvement proposals. Specifically, it suggests methods for automating tasks and the necessary tools.

[0188] Step 6:

[0189] The server compiles the generated automation suggestions into a report and notifies the user. The input is the generated report data, and the output is a document sent to the user. Based on the report, the user can consider specific measures for improving their business processes.

[0190] (Application Example 2)

[0191] 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".

[0192] In the workplace, there is a growing need to improve work efficiency by considering the emotional state of workers. However, conventional automation systems are insufficient for providing real-time guidance based on workers' emotions. Therefore, a new method is needed that reduces the burden on workers while further increasing the overall efficiency of operations.

[0193] 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.

[0194] In this invention, the server includes means for analyzing visual and auditory information collected in the work environment, means for identifying tasks that can be automated based on the analysis, means for recognizing the emotional state of the worker, means for providing real-time advice to the worker based on the emotional state, and means for proposing the most suitable automation means for the task. This makes it possible to optimize work processes while taking into account the emotional state of the worker.

[0195] "Work environment" refers to the space or system in which daily work is carried out within a company or organization.

[0196] "Visual information" refers to images and video data obtained through cameras and other image acquisition devices.

[0197] "Acoustic information" refers to audio data obtained through microphones and other sound acquisition devices.

[0198] "Means of analysis" refers to the processes and techniques used to process input data and extract or understand specific information.

[0199] "Automated tasks" refer to business processes that can be performed by machines or software without human intervention.

[0200] A "worker" refers to a person who is engaged in a business process and performs a specific task.

[0201] "Emotional state" refers to information that indicates the psychological and emotional state of a worker.

[0202] "Means of providing real-time advice" refers to processes and technologies for providing immediate advice and guidance to workers.

[0203] "Suggested means" refers to a method or process for indicating the optimal course of action or options based on the analysis results.

[0204] This invention is a system for recognizing the emotional state of workers by analyzing visual and auditory information in the work environment, and then providing real-time automated advice. This system collects data from the work environment using a terminal equipped with a camera and microphone. The data collected by the terminal is transmitted to a server, which analyzes the data to recognize the emotional state of the workers.

[0205] Specifically, the server uses the OpenCV library to process visual information and analyze the worker's facial expressions. It also uses the speech_recognition library to convert acoustic information into text data and recognizes emotions from the spoken content through natural language processing. Based on the emotional information obtained in this way, the server identifies areas where the worker is experiencing stress and provides real-time advice on the identified problems. This advice includes suggestions for improving work procedures or automating them.

[0206] For example, if a worker on a manufacturing line is taking too long on a particular operation, the system analyzes that operation and proposes an efficient work procedure, taking into account the worker's stress level. The program in this system utilizes a generative AI model to generate optimal advice tailored to specific situations.

[0207] An example of a prompt message for the generated AI model would be, "Please tell us the points where the worker feels stressed during the current task, and then provide the best automation suggestions to address them." This allows the server to provide quick and appropriate support to the worker, thereby improving the overall efficiency of the work.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The terminal uses its camera and microphone to collect visual and auditory information in real time within the work environment. This collected data is then transferred to a server.

[0211] Step 2:

[0212] The server divides the received visual information into frames and analyzes the worker's facial expressions using the OpenCV library. Here, image data is used as input, and the characteristic points and changes in facial expressions are analyzed to output the emotional state.

[0213] Step 3:

[0214] The server converts acoustic information into text data via speech recognition technology. Using the Speech_recognition library, it converts the audio data into text, and then outputs the content and emotion of the speech by inputting that text and performing natural language processing.

[0215] Step 4:

[0216] The server's emotion engine recognizes the worker's emotional state from frame analysis and spoken content. Using the results of facial expression analysis and text analysis already obtained, it identifies situations and points where the worker is experiencing stress, and this is output as a recognition of the worker's emotional state.

[0217] Step 5:

[0218] The server uses a generative AI model to generate real-time advice for the worker based on their emotional state. The prompt used for the generative AI model is, "Please tell us what points in the current task cause the worker stress, and provide the best automation suggestions to address them." As a result, specific suggestions for improving the work procedure are output.

[0219] Step 6:

[0220] The system notifies the user of advice, allowing them to review the suggested improvements and use them to enhance efficiency in their actual work. Here, the server generates advice and notifies the user, and the content of that notification is output as work support information for the user.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention provides an advanced analysis system that utilizes visual and acoustic information for the purpose of improving the efficiency of business processes. This system collects visual and acoustic information using devices installed in the work environment, processes this information with an analysis device to identify tasks that can be automated in the work, and then proposes the most suitable automation method to the user.

[0238] Specifically, terminals placed in the work environment use cameras and microphones to collect video and audio during work processes. This allows for the acquisition of visible work procedures and voice instructions as digital data. The collected data is transmitted to a server via the network, where detailed analysis is performed in the next step.

[0239] The server divides visual information frame by frame and analyzes human actions and changes on the screen. For acoustic information, speech recognition technology is used to transcribe it into text, and machine learning algorithms are used to extract important phrases and keywords. This makes it possible to identify areas within the overall business process that are suitable for automation, such as repetitive tasks.

[0240] Next, the server selects the optimal automation method based on past data for the identified automatable tasks and generates a proposal. For example, if analysis reveals that data entry is performed frequently, the use of an RPA tool will be recommended. If it is found that there are many customer inquiries, the introduction of an intelligent chatbot may be considered.

[0241] This recommendation information is provided to the user as a report. Based on the report, the user decides whether to implement the automation tool, and the server supports the implementation process. This includes providing tool configuration instructions and technical coaching during implementation.

[0242] As a concrete example, in one office environment, the content of meetings is routinely accumulated as audio data, and by analyzing this data, the task of creating meeting minutes can be automated. In another case, the operations on a manufacturing line are recorded as video data, allowing for the extraction of inefficient processes and the identification of processes that can be automated or made more efficient.

[0243] Thus, this system provides concrete means to support the streamlining of the entire business process and improve productivity through visual and acoustic analysis.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The terminal uses cameras and microphones placed within the work environment to collect visual and auditory data in real time. The collected data is temporarily stored to streamline processing.

[0247] Step 2:

[0248] The data collected by the device is processed in batches at regular intervals and sent to the server via the secure upload system. The data is encrypted in a way that respects privacy.

[0249] Step 3:

[0250] The server divides the received video data into frames and applies image recognition algorithms to identify human movements, operations, and screen changes. At this point, process points are identified.

[0251] Step 4:

[0252] The server converts audio data into text using speech recognition technology, and then performs natural language processing on that text data. This allows for the extraction of important business-related instructions and key points of conversations.

[0253] Step 5:

[0254] The server integrates the analysis results from both video and audio and reviews the entire business process. Based on this review, it identifies repetitive tasks that can be automated and processes that have room for improvement.

[0255] Step 6:

[0256] Based on the identified tasks, the server selects the most suitable automation method. An AI algorithm, using past data, suggests the most effective method from among RPA tools, chatbots, and other options.

[0257] Step 7:

[0258] The server generates a report of automation suggestions and notifies the user. The user can receive the suggestions via email or a dedicated dashboard.

[0259] Step 8:

[0260] Once the user reviews the proposal and decides to implement the automation tool, the server provides detailed instructions and implementation support to ensure a smooth tool deployment.

[0261] Step 9:

[0262] The server re-collects and analyzes business process data after implementation, measures the impact of automation on productivity, and provides users with feedback to encourage continuous improvement.

[0263] (Example 1)

[0264] 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."

[0265] In today's business environment, there is a demand for the effective use of diverse data and the streamlining of business processes. However, conventional systems often lack the ability to adequately analyze visual and auditory information and identify tasks that can be automated. Furthermore, there is a challenge in proposing the optimal automation method based on these results and applying it to actual business operations.

[0266] 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.

[0267] In this invention, the server includes means for dividing visual information into frames for analyzing information collected in the work environment, means for converting acoustic information into text data using speech recognition technology, and means for extracting important data using machine learning algorithms. This makes it possible to identify tasks that can be automated based on visual and acoustic data and to propose suitable automation means.

[0268] "Work environment" refers to the physical or virtual location or situation in which work is performed within a company or organization.

[0269] "Information-collecting devices" refer to hardware devices installed in the work environment to acquire visual and auditory information.

[0270] "Visual information" refers to video data acquired from cameras, image sensors, and other sources.

[0271] "Acoustic information" refers to sound data acquired from microphones or similar audio input devices.

[0272] "Methods for dividing into frames" refer to analytical techniques for separating video information into a series of still images (frames).

[0273] "Speech recognition technology" refers to the technology that analyzes speech and converts it into text.

[0274] A "machine learning algorithm" refers to a computer program that learns specific patterns or rules from data.

[0275] "Automated tasks" refer to business tasks that are performed repetitively according to specific procedures and can therefore be automated by machines or software.

[0276] "Automation methods" refer to the means and technologies implemented to improve efficiency in business processes.

[0277] "Providing proposals as a report" means documenting analysis results and recommendations and communicating them to the user in a report format.

[0278] A "generative AI model" refers to an artificial intelligence program that generates new information or prompts based on data it has learned in advance.

[0279] This invention provides a system for improving the efficiency of processes in the work environment. Specific embodiments for carrying out the invention are described below.

[0280] The terminals are installed in the work environment and collect visual and acoustic information using high-resolution cameras and noise-canceling microphones. This allows for the acquisition of digital information of meetings, actions during work processes, and conversations.

[0281] The collected information is securely transmitted to the server via the network. Data security is ensured during this process using encryption technologies such as SSL / TLS.

[0282] The server utilizes advanced software to analyze the received data. Visual information is divided frame by frame, and machine learning algorithms are used to detect human movements and changes on the screen. For acoustic information, speech recognition technology is applied to convert speech to text, and natural language processing techniques are used to extract important phrases.

[0283] The analysis results are used to identify tasks that can be automated. For the identified processes, the server suggests the most suitable automation method, such as RPA tools or chatbots, and provides this to the user as a report. Based on this report, the user can decide which automation method to implement.

[0284] For example, there are cases where the content of a meeting in the office is automatically accumulated as voice data and utilized for creating meeting minutes, or cases where the video of a manufacturing line is analyzed to identify inefficient processes. This leads to the rationalization of the entire business.

[0285] As an example of a prompt sentence, an instruction such as "Propose a method to analyze the meeting voice data in the office environment and improve the efficiency of creating meeting minutes." is given to the AI.

[0286] This system provides specific means to assist in the automation of business processes and achieve productivity improvement by effectively utilizing visual and acoustic information.

[0287] The flow of the specific process in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The terminal uses a high-resolution camera and a noise-canceling microphone installed in the business environment to collect visual information and acoustic information. Real-time video and audio are captured as input. The output is video files and audio files as digital data. At this time, the terminal automatically starts recording and saves all operations and conversations during the business as digital content.

[0290] Step 2:

[0291] The terminal encrypts the collected video files and audio files using a security protocol (e.g., SSL / TLS) and transmits them to the server via the network. The input is the untransmitted digital data collected in Step 1, and the output is the data transfer to the server in a securely encrypted state. In this process, the protection of data and the transfer speed are emphasized.

[0292] Step 3:

[0293] The server divides the received visual information frame by frame and uses machine learning algorithms to analyze human actions and changes on the screen. The input is encrypted visual information, and the output is a list of structured behavioral patterns and a record of changes. To efficiently process the large amount of image data, the server performs the analysis using parallel processing.

[0294] Step 4:

[0295] The server converts acoustic information into text data through a speech recognition engine and extracts important phrases using natural language processing techniques. The input is encrypted audio information, and the output is transcribed conversation data and extracted key phrases. This step performs accurate and rapid speech-to-text conversion and analysis.

[0296] Step 5:

[0297] Based on the analysis results, the server identifies tasks that can be automated and proposes the most suitable automation methods. The input is the analysis data obtained in steps 3 and 4, and the output is a list of recommended automation methods. Here, statistical data and past cases are referred to to select the most effective solution.

[0298] Step 6:

[0299] The user receives reports from the server and considers implementing automation measures in their operations. The input is an automation suggestion report from the server, and the output is an action plan based on the user's decision. At this stage, the user develops a feasible strategy and prepares to move on to the next step.

[0300] (Application Example 1)

[0301] 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."

[0302] In the manufacturing site, there is a problem that the automation of business processes is delayed because it is difficult to identify and propose efficient work procedures. In particular, how to quickly identify repetitive or inefficient processes performed manually and propose optimal automation means is an important issue.

[0303] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0304] In this invention, the server includes means for analyzing visual information and acoustic information collected in a business environment, means for identifying tasks that can be automated based on the analysis, means for proposing optimal automation means for the tasks, means for identifying inefficient processes by a monitoring device collecting and analyzing information in a manufacturing process, and means for supporting the efficiency improvement of work processes based on the proposed automation means. Thereby, efficient automation of business processes in the manufacturing site becomes possible.

[0305] The "business environment" refers to a place where business operations such as manufacturing processes and offices are carried out.

[0306] The "visual information" refers to video data collected by a camera or other image acquisition device.

[0307] The "acoustic information" refers to audio data collected by a microphone or other voice acquisition device.

[0308] The "analysis means" refers to a process for analyzing the collected information and identifying trends and specific patterns.

[0309] The "task that can be automated" refers to a task that can be completed mechanically or programmatically without the need for human operation.

[0310] The "automation means" refers to a technology or device introduced to automatically execute a specific task.

[0311] An "inefficient process" refers to a manufacturing stage that requires excessive time or cost and therefore has room for improvement.

[0312] A "surveillance device" refers to a device installed to collect visual and auditory information.

[0313] "Efficiency improvement" refers to reducing time and resources in business processes and improving the productivity of operations.

[0314] The system implementing this invention consists of collecting visual and acoustic information using monitoring devices installed in the work environment, and analyzing this data on a central server. Terminals use cameras and microphones to acquire video and audio data of the work in real time. The collected data is transmitted to the server via the network.

[0315] The server uses Python, the machine learning library TensorFlow, and the image processing library OpenCV to analyze visual information frame by frame and identify human movements and screen changes. For acoustic information, the Google Cloud Speech-to-Text API is used to convert audio data into text data, which is then analyzed using natural language processing techniques to identify important phrases and instructions.

[0316] Based on the analysis results, the server identifies tasks that can be automated and recommends the optimal automation method for those tasks. This process uses a generative AI model to suggest methods for maximizing the efficiency of business processes. As a result, users can implement automation based on the suggested methods and improve work efficiency.

[0317] As a concrete example, in a food manufacturing plant, monitoring devices may patrol the bread production line, collecting and analyzing video and audio of the filling process to identify inefficient work and recommend the introduction of robots in those processes. In this way, successful automation has the potential to dramatically improve production efficiency.

[0318] An example of a prompt message would be: "The robot monitoring the equipment has identified the following inefficient tasks. Please perform a detailed data analysis and suggest the best automation solutions." Through this process, users can gain useful insights to improve their processes and increase productivity.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The device captures visual information of the work environment using a camera and records audio information using a microphone. The input at this stage is the video and audio of the actual work. The output is a data file of the visual and audio information converted into a digital format. Specifically, camera capture and microphone recording occur simultaneously.

[0322] Step 2:

[0323] The terminal transmits the acquired digital data to the server in real time via the network. The input is the digital data converted in step 1. The output is the data transferred in a format accessible to the server. Specifically, data transfer is performed using a communication protocol (e.g., TCP / IP).

[0324] Step 3:

[0325] The server divides the received visual information into frames and analyzes each frame using OpenCV. The input is the visual data sent from the terminal. The output is the analysis result, which identifies patterns and changes in movement. Specifically, the process involves processing each frame and detecting and identifying movement.

[0326] Step 4:

[0327] The server converts acoustic information into text data using the Google Cloud Speech-to-Text API and extracts important phrases and commands through natural language processing. The input is acoustic data sent from the terminal. The output is the analyzed text data and the extracted important phrases. Specifically, the process involves transcribing audio data into text and extracting keywords.

[0328] Step 5:

[0329] The server combines the frame analysis results and the speech analysis results to identify tasks that can be automated using a machine learning model. The input is the analysis results obtained from steps 3 and 4. The output is a list of tasks that can be automated. Specific actions include analysis and prediction by a generative AI model.

[0330] Step 6:

[0331] The server prompts the user with the optimal automation method for the identified automatable tasks. The input is the task list obtained in step 5. The output is a user-readable suggestion report. Specific actions include suggestion generation and prompt creation based on data analysis.

[0332] 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.

[0333] This invention enables more effective automation suggestions by combining a system that uses visual and auditory information for analyzing business processes with an emotion engine that recognizes user emotions. This system is realized by collecting data from both visual and auditory perspectives using devices installed in the work environment.

[0334] First, the terminal collects video and audio in real time during work using cameras and microphones placed in the office environment. This data is temporarily stored and then sent to a server for further processing.

[0335] The server divides the received video data frame by frame and uses image analysis technology to analyze the specific actions involved in the work. Simultaneously, it converts audio data into text using speech recognition technology and analyzes the content using natural language processing technology. This analysis clarifies important instructions and repetitive steps in the business process.

[0336] Furthermore, the server's emotion engine analyzes the user's voice and facial expressions to recognize their emotions. Based on this information, stress points within the work environment and the user's emotional triggers can be identified and reflected in automation suggestions. For example, if it is recognized that a user is experiencing stress while performing a particular task, automating or improving that task will be prioritized.

[0337] The analysis results are presented to the user in the form of a report that includes optimized suggestions for automation. This allows the user to consider adopting specific measures that can help improve their business processes. If the user approves the suggested automation measures, the system will assist with their implementation and support their operation in actual business operations.

[0338] As a concrete example, considering the case of a customer support center, an emotion engine analyzes the voice and video of employees during interactions, and automated tools such as optimizing response scripts and utilizing chatbots are proposed to reduce the burden incurred during conversations with customers.

[0339] Thus, by incorporating an emotion engine, the present invention provides a system that enables personalized automation suggestions that take into account the user's emotional state, thereby achieving further improvements in operational efficiency.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The terminal uses cameras and microphones installed within the office to collect visual and auditory information about work activities in real time. The collected data is temporarily stored on the terminal.

[0343] Step 2:

[0344] The device periodically compresses the data it collects and securely uploads it to the server. The uploaded data is encrypted and protected using a secure method.

[0345] Step 3:

[0346] The server divides the visual data frame by frame and uses image recognition technology to analyze the business process. This analysis identifies and digitizes human movements and screen operations.

[0347] Step 4:

[0348] The server converts the audio data into text using speech recognition technology and analyzes it through natural language processing algorithms. This extracts the key points of conversations and instructions related to the work.

[0349] Step 5:

[0350] The server uses an emotion engine to analyze the user's emotional state from audio and video data. It identifies emotions from facial expression analysis and voice tone, and determines the user's stress level and satisfaction level.

[0351] Step 6:

[0352] The server integrates the analysis results and identifies tasks and processes that can and need to be automated. Prioritizing automation targets is selected while considering the user's emotional state.

[0353] Step 7:

[0354] The server selects the most suitable automation method for the identified task and creates a proposal. Based on AI analysis, tools such as RPA tools and chatbots are selected and customized according to the user's emotional state.

[0355] Step 8:

[0356] The server generates automation suggestions in report format and notifies the user. The user reviews the suggestions via email or a dashboard and receives detailed explanations as needed.

[0357] Step 9:

[0358] Once the user reviews the proposal and decides to implement the automation tool, the server provides support for the implementation. This includes installation guides and assistance with initial setup.

[0359] Step 10:

[0360] To measure the effectiveness of the system after the server was implemented, data will be collected and analyzed again. This analysis will confirm improvements in productivity and reductions in workload, and continuous improvement measures will be fed back to the users.

[0361] (Example 2)

[0362] 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".

[0363] In the workplace, a challenge when promoting automation to improve work efficiency is that conventional systems fail to adequately consider the context of the work and the emotions of the users. As a result, proposed automation methods may not lead to improvements in the overall business process. This invention aims to provide more practical and effective automation proposals by comprehensively analyzing visual and auditory information and further taking into account the emotional state of the user.

[0364] 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.

[0365] In this invention, the server includes means for analyzing video and audio data collected in the work environment, means for identifying automatable work procedures and generating optimization suggestions, and means for analyzing the user's voice and facial expressions to recognize their emotional state and reflect the results in the automation suggestions. This makes it possible to provide specific and effective automation suggestions that promote the improvement of business processes.

[0366] "Video data" refers to visual information recorded in a digital format within a work environment.

[0367] "Audio data" refers to acoustic information recorded in a digital format within a work environment.

[0368] "Analysis" refers to processing the obtained data, extracting information related to business processes, and interpreting it.

[0369] "Automable work procedures" are parts of a task that can be automated or made more efficient by having a system intervene to reduce manual work.

[0370] An "optimization suggestion" is a specific method or means that the system suggests to improve the efficiency of operations based on the analysis results.

[0371] "Analysis of voice and facial expressions" involves analyzing characteristics such as tone of sound and facial movements to infer emotions and states of mind.

[0372] "Emotional state" refers to the user's psychological or emotional state, including factors that contribute to workload and stress in their work.

[0373] A "report" is a document that organizes the analysis results and optimization suggestions and provides them to the user.

[0374] This invention is a system that analyzes video and audio data to generate automated suggestions that take into account the user's emotional state, with the aim of improving the efficiency of work in the work environment.

[0375] The terminal uses cameras and microphones installed in the office environment to capture video and audio in real time during work. The cameras are positioned to cover a wide area of ​​the work area, and the microphones are positioned to effectively collect audio. The collected data is encoded and temporarily stored on the terminal.

[0376] The server receives video data transmitted from the terminal and analyzes it frame by frame using image analysis libraries such as "OpenCV". Similarly, audio data is converted into text data using speech recognition software such as "Google Speech-to-Text". The converted text data is then processed using the "NLTK" library to extract important instructions and rules related to the business.

[0377] The server also analyzes the user's voice tone and facial expressions, and uses an emotion engine to recognize the user's emotional state. This analysis utilizes tools such as the "Microsoft Azure Face API" and "IBM Watson Tone Analyzer." Based on this information, automated suggestions are optimized to reduce the user's psychological burden.

[0378] The generated automation suggestions are notified to the user in report format. Based on this, the user can decide whether to adopt the suggested automation methods into their business processes. If the suggestions provided by the system are approved, the system will assist in their implementation and support improvements in business efficiency.

[0379] A concrete example is the automation tools proposed to reduce the burden on employees in customer support centers. This could include optimizing response scripts or introducing chatbots. This reduces the workload on employees and improves the efficiency of customer service.

[0380] An example of an input prompt for the generating AI model is: "To improve the operational efficiency of the customer support center, perform an emotional analysis to determine which tasks employees find stressful and propose solutions for improvement."

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] The terminal collects video and audio data using cameras and microphones in the work environment. The input is real-time video and audio, which are stored on the terminal as temporary files in digital format. After storage, the data is transmitted to the server via a secure network.

[0384] Step 2:

[0385] The server receives video data transmitted from the terminal and divides it into frames using the "OpenCV" library. The input is the received video data, and the output is the analyzed motion information. It detects specific actions, such as "checking documents" or "computer operation," and generates an action log based on these actions.

[0386] Step 3:

[0387] The server converts audio data into text data using tools such as "Google Speech-to-Text." The input is audio data, and the output is conversation information in text format. This text data is then processed using "NLTK" to extract business procedures and important communication content.

[0388] Step 4:

[0389] The server uses an emotion engine to analyze the user's voice tone and facial expression data. The input is voice tone and facial expression information in the video, and the output is the user's emotional state. This uses "Microsoft Azure Face API" and "IBM Watson Tone Analyzer" to identify the user's emotional trigger points.

[0390] Step 5:

[0391] The server generates automation suggestions based on the analysis results to date. Inputs include action logs, conversation content, and emotional state data, while output is an optimized automation suggestion that includes improvement proposals. Specifically, it suggests methods for automating tasks and the necessary tools.

[0392] Step 6:

[0393] The server compiles the generated automation suggestions into a report and notifies the user. The input is the generated report data, and the output is a document sent to the user. Based on the report, the user can consider specific measures for improving their business processes.

[0394] (Application Example 2)

[0395] 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."

[0396] In the workplace, there is a growing need to improve work efficiency by considering the emotional state of workers. However, conventional automation systems are insufficient for providing real-time guidance based on workers' emotions. Therefore, a new method is needed that reduces the burden on workers while further increasing the overall efficiency of operations.

[0397] 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.

[0398] In this invention, the server includes means for analyzing visual and auditory information collected in the work environment, means for identifying tasks that can be automated based on the analysis, means for recognizing the emotional state of the worker, means for providing real-time advice to the worker based on the emotional state, and means for proposing the most suitable automation means for the task. This makes it possible to optimize work processes while taking into account the emotional state of the worker.

[0399] "Work environment" refers to the space or system in which daily work is carried out within a company or organization.

[0400] "Visual information" refers to images and video data obtained through cameras and other image acquisition devices.

[0401] "Acoustic information" refers to audio data obtained through microphones and other sound acquisition devices.

[0402] "Means of analysis" refers to the processes and techniques used to process input data and extract or understand specific information.

[0403] "Automated tasks" refer to business processes that can be performed by machines or software without human intervention.

[0404] A "worker" refers to a person who is engaged in a business process and performs a specific task.

[0405] "Emotional state" refers to information that indicates the psychological and emotional state of a worker.

[0406] "Means of providing real-time advice" refers to processes and technologies for providing immediate advice and guidance to workers.

[0407] "Suggested means" refers to a method or process for indicating the optimal course of action or options based on the analysis results.

[0408] This invention is a system for recognizing the emotional state of workers by analyzing visual and auditory information in the work environment, and then providing real-time automated advice. This system collects data from the work environment using a terminal equipped with a camera and microphone. The data collected by the terminal is transmitted to a server, which analyzes the data to recognize the emotional state of the workers.

[0409] Specifically, the server uses the OpenCV library to process visual information and analyze the worker's facial expressions. It also uses the speech_recognition library to convert acoustic information into text data and recognizes emotions from the spoken content through natural language processing. Based on the emotional information obtained in this way, the server identifies areas where the worker is experiencing stress and provides real-time advice on the identified problems. This advice includes suggestions for improving work procedures or automating them.

[0410] For example, if a worker on a manufacturing line is taking too long on a particular operation, the system analyzes that operation and proposes an efficient work procedure, taking into account the worker's stress level. The program in this system utilizes a generative AI model to generate optimal advice tailored to specific situations.

[0411] An example of a prompt message for the generated AI model would be, "Please tell us the points where the worker feels stressed during the current task, and then provide the best automation suggestions to address them." This allows the server to provide quick and appropriate support to the worker, thereby improving the overall efficiency of the work.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The terminal uses its camera and microphone to collect visual and auditory information in real time within the work environment. This collected data is then transferred to a server.

[0415] Step 2:

[0416] The server divides the received visual information into frames and analyzes the worker's facial expressions using the OpenCV library. Here, image data is used as input, and the characteristic points and changes in facial expressions are analyzed to output the emotional state.

[0417] Step 3:

[0418] The server converts acoustic information into text data via speech recognition technology. Using the Speech_recognition library, it converts the audio data into text, and then uses that text as input for natural language processing to output the content and emotion of the speech.

[0419] Step 4:

[0420] The server's emotion engine recognizes the worker's emotional state from frame analysis and spoken content. Using the results of facial expression analysis and text analysis already obtained, it identifies situations and points where the worker is experiencing stress, and this is output as a recognition of the worker's emotional state.

[0421] Step 5:

[0422] The server uses a generative AI model to generate real-time advice for the worker based on their emotional state. The prompt used for the generative AI model is, "Please tell us what points in the current task cause the worker stress, and provide the best automation suggestions to address them." As a result, specific suggestions for improving the work procedure are output.

[0423] Step 6:

[0424] The system notifies the user of advice, allowing them to review the suggested improvements and use them to enhance efficiency in their actual work. Here, the server generates advice and notifies the user, and the content of that notification is output as work support information for the user.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This invention provides an advanced analysis system that utilizes visual and acoustic information for the purpose of improving the efficiency of business processes. This system collects visual and acoustic information using devices installed in the work environment, processes this information with an analysis device to identify tasks that can be automated in the work, and then proposes the most suitable automation method to the user.

[0442] Specifically, terminals placed in the work environment use cameras and microphones to collect video and audio during work processes. This allows for the acquisition of visible work procedures and voice instructions as digital data. The collected data is transmitted to a server via the network, where detailed analysis is performed in the next step.

[0443] The server divides visual information frame by frame and analyzes human actions and changes on the screen. For acoustic information, speech recognition technology is used to transcribe it into text, and machine learning algorithms are used to extract important phrases and keywords. This makes it possible to identify areas within the overall business process that are suitable for automation, such as repetitive tasks.

[0444] Next, the server selects the optimal automation method based on past data for the identified automatable tasks and generates a proposal. For example, if analysis reveals that data entry is performed frequently, the use of an RPA tool will be recommended. If it is found that there are many customer inquiries, the introduction of an intelligent chatbot may be considered.

[0445] This recommendation information is provided to the user as a report. Based on the report, the user decides whether to implement the automation tool, and the server supports the implementation process. This includes providing tool configuration instructions and technical coaching during implementation.

[0446] As a concrete example, in one office environment, the content of meetings is routinely accumulated as audio data, and by analyzing this data, the task of creating meeting minutes can be automated. In another case, the operations on a manufacturing line are recorded as video data, allowing for the extraction of inefficient processes and the identification of processes that can be automated or made more efficient.

[0447] Thus, this system provides concrete means to support the streamlining of the entire business process and improve productivity through visual and acoustic analysis.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The terminal uses cameras and microphones placed within the work environment to collect visual and auditory data in real time. The collected data is temporarily stored to streamline processing.

[0451] Step 2:

[0452] The data collected by the device is processed in batches at regular intervals and sent to the server via the secure upload system. The data is encrypted in a way that respects privacy.

[0453] Step 3:

[0454] The server divides the received video data into frames and applies image recognition algorithms to identify human movements, operations, and screen changes. At this point, process points are identified.

[0455] Step 4:

[0456] The server converts audio data into text using speech recognition technology, and then performs natural language processing on that text data. This allows for the extraction of important business-related instructions and key points of conversations.

[0457] Step 5:

[0458] The server integrates the analysis results from both video and audio and reviews the entire business process. Based on this review, it identifies repetitive tasks that can be automated and processes that have room for improvement.

[0459] Step 6:

[0460] Based on the identified tasks, the server selects the most suitable automation method. An AI algorithm, using past data, suggests the most effective method from among RPA tools, chatbots, and other options.

[0461] Step 7:

[0462] The server generates a report of automation suggestions and notifies the user. The user can receive the suggestions via email or a dedicated dashboard.

[0463] Step 8:

[0464] Once the user reviews the proposal and decides to implement the automation tool, the server provides detailed instructions and implementation support to ensure a smooth tool deployment.

[0465] Step 9:

[0466] The server re-collects and analyzes business process data after implementation, measures the impact of automation on productivity, and provides users with feedback to encourage continuous improvement.

[0467] (Example 1)

[0468] 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."

[0469] In today's business environment, there is a demand for the effective use of diverse data and the streamlining of business processes. However, conventional systems often lack the ability to adequately analyze visual and auditory information and identify tasks that can be automated. Furthermore, there is a challenge in proposing the optimal automation method based on these results and applying it to actual business operations.

[0470] 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.

[0471] In this invention, the server includes means for dividing visual information into frames for analyzing information collected in the work environment, means for converting acoustic information into text data using speech recognition technology, and means for extracting important data using machine learning algorithms. This makes it possible to identify tasks that can be automated based on visual and acoustic data and to propose suitable automation means.

[0472] "Work environment" refers to the physical or virtual location or situation in which work is performed within a company or organization.

[0473] "Information-collecting devices" refer to hardware devices installed in the work environment to acquire visual and auditory information.

[0474] "Visual information" refers to video data acquired from cameras, image sensors, and other sources.

[0475] "Acoustic information" refers to sound data acquired from microphones or similar audio input devices.

[0476] "Methods for dividing into frames" refer to analytical techniques for separating video information into a series of still images (frames).

[0477] "Speech recognition technology" refers to the technology that analyzes speech and converts it into text.

[0478] A "machine learning algorithm" refers to a computer program that learns specific patterns or rules from data.

[0479] "Automated tasks" refer to business tasks that are performed repetitively according to specific procedures and can therefore be automated by machines or software.

[0480] "Automation methods" refer to the means and technologies implemented to improve efficiency in business processes.

[0481] "Providing proposals as a report" means documenting analysis results and recommendations and communicating them to the user in a report format.

[0482] A "generative AI model" refers to an artificial intelligence program that generates new information or prompts based on data it has learned in advance.

[0483] This invention provides a system for improving the efficiency of processes in the work environment. Specific embodiments for carrying out the invention are described below.

[0484] The terminals are installed in the work environment and collect visual and acoustic information using high-resolution cameras and noise-canceling microphones. This allows for the acquisition of digital information of meetings, actions during work processes, and conversations.

[0485] The collected information is securely transmitted to the server via the network. Data security is ensured during this process using encryption technologies such as SSL / TLS.

[0486] The server utilizes advanced software to analyze the received data. Visual information is divided frame by frame, and machine learning algorithms are used to detect human movements and changes on the screen. For acoustic information, speech recognition technology is applied to convert speech to text, and natural language processing techniques are used to extract important phrases.

[0487] The analysis results are used to identify tasks that can be automated. For the identified processes, the server suggests the most suitable automation method, such as RPA tools or chatbots, and provides this to the user as a report. Based on this report, the user can decide which automation method to implement.

[0488] For example, this could involve automatically accumulating audio data of office meetings and using it to create meeting minutes, or analyzing video footage of manufacturing lines to identify inefficient processes. This would lead to overall streamlining of operations.

[0489] An example of a prompt message given to the AI ​​is, "Analyze meeting audio data in an office environment and suggest ways to streamline meeting minute creation."

[0490] This system provides concrete means to improve productivity by effectively utilizing visual and auditory information to help automate business processes.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The terminal uses a high-resolution camera and noise-canceling microphone installed in the work environment to collect visual and acoustic information. Real-time video and audio are captured as input. The output is video and audio files as digital data. At this time, the terminal automatically starts recording video and audio, saving all actions and conversations during work as digital content.

[0494] Step 2:

[0495] The terminal encrypts the collected video and audio files using a security protocol (e.g., SSL / TLS) and transmits them to the server over the network. The input is the untransmitted digital data collected in step 1, and the output is the secure, encrypted data transfer to the server. Data protection and transfer speed are paramount in this process.

[0496] Step 3:

[0497] The server divides the received visual information frame by frame and uses machine learning algorithms to analyze human actions and changes on the screen. The input is encrypted visual information, and the output is a list of structured behavioral patterns and a record of changes. To efficiently process the large amount of image data, the server performs the analysis using parallel processing.

[0498] Step 4:

[0499] The server converts acoustic information into text data through a speech recognition engine and extracts important phrases using natural language processing techniques. The input is encrypted audio information, and the output is transcribed conversation data and extracted key phrases. This step performs accurate and rapid speech-to-text conversion and analysis.

[0500] Step 5:

[0501] Based on the analysis results, the server identifies tasks that can be automated and proposes the most suitable automation methods. The input is the analysis data obtained in steps 3 and 4, and the output is a list of recommended automation methods. Here, statistical data and past cases are referred to to select the most effective solution.

[0502] Step 6:

[0503] The user receives reports from the server and considers implementing automation measures in their operations. The input is an automation suggestion report from the server, and the output is an action plan based on the user's decision. At this stage, the user develops a feasible strategy and prepares to move on to the next step.

[0504] (Application Example 1)

[0505] 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."

[0506] In manufacturing environments, the difficulty in identifying and proposing efficient work procedures is a challenge that delays the automation of business processes. In particular, a key challenge is how to quickly identify repetitive or inefficient processes performed manually and propose the most suitable automation methods.

[0507] 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.

[0508] In this invention, the server includes means for analyzing visual and acoustic information collected in the work environment, means for identifying tasks that can be automated based on the analysis, means for proposing the most suitable automation means for the tasks, means for identifying inefficient processes by having a monitoring device collect and analyze information in the manufacturing process, and means for supporting the efficiency of work processes based on the proposed automation means. This enables efficient automation of business processes in the manufacturing site.

[0509] "Work environment" refers to the place where work is performed, such as manufacturing processes or offices.

[0510] "Visual information" refers to video data collected by cameras and other image acquisition devices.

[0511] "Acoustic information" refers to audio data collected by microphones and other sound acquisition devices.

[0512] "Analysis methods" refer to the process of analyzing collected information to identify trends and specific patterns.

[0513] "Automated tasks" refer to tasks that can be completed mechanically or programmatically without requiring human intervention.

[0514] "Automation means" refers to technologies or devices introduced to perform specific tasks automatically.

[0515] An "inefficient process" refers to a manufacturing stage that requires excessive time or cost and therefore has room for improvement.

[0516] A "surveillance device" refers to a device installed to collect visual and auditory information.

[0517] "Efficiency improvement" refers to reducing time and resources in business processes and improving the productivity of operations.

[0518] The system implementing this invention consists of collecting visual and acoustic information using monitoring devices installed in the work environment, and analyzing this data on a central server. Terminals use cameras and microphones to acquire video and audio data of the work in real time. The collected data is transmitted to the server via the network.

[0519] The server uses Python, the machine learning library TensorFlow, and the image processing library OpenCV to analyze visual information frame by frame and identify human movements and screen changes. For acoustic information, the Google Cloud Speech-to-Text API is used to convert audio data into text data, which is then analyzed using natural language processing techniques to identify important phrases and instructions.

[0520] Based on the analysis results, the server identifies tasks that can be automated and recommends the optimal automation method for those tasks. This process uses a generative AI model to suggest methods for maximizing the efficiency of business processes. As a result, users can implement automation based on the suggested methods and improve work efficiency.

[0521] As a concrete example, in a food manufacturing plant, monitoring devices may patrol the bread production line, collecting and analyzing video and audio of the filling process to identify inefficient work and recommend the introduction of robots in those processes. In this way, successful automation has the potential to dramatically improve production efficiency.

[0522] An example of a prompt message would be: "The robot monitoring the equipment has identified the following inefficient tasks. Please perform a detailed data analysis and suggest the best automation solutions." Through this process, users can gain useful insights to improve their processes and increase productivity.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The device captures visual information of the work environment using a camera and records audio information using a microphone. The input at this stage is the video and audio of the actual work. The output is a data file of the visual and audio information converted into a digital format. Specifically, camera capture and microphone recording occur simultaneously.

[0526] Step 2:

[0527] The terminal transmits the acquired digital data to the server in real time via the network. The input is the digital data converted in step 1. The output is the data transferred in a format accessible to the server. Specifically, data transfer is performed using a communication protocol (e.g., TCP / IP).

[0528] Step 3:

[0529] The server divides the received visual information into frames and analyzes each frame using OpenCV. The input is the visual data sent from the terminal. The output is the analysis result, which identifies patterns and changes in movement. Specifically, the process involves processing each frame and detecting and identifying movement.

[0530] Step 4:

[0531] The server converts acoustic information into text data using the Google Cloud Speech-to-Text API and extracts important phrases and commands through natural language processing. The input is acoustic data sent from the terminal. The output is the analyzed text data and the extracted important phrases. Specifically, the process involves transcribing audio data into text and extracting keywords.

[0532] Step 5:

[0533] The server combines the frame analysis results and the speech analysis results to identify tasks that can be automated using a machine learning model. The input is the analysis results obtained from steps 3 and 4. The output is a list of tasks that can be automated. Specific actions include analysis and prediction by a generative AI model.

[0534] Step 6:

[0535] The server prompts the user with the optimal automation method for the identified automatable tasks. The input is the task list obtained in step 5. The output is a user-readable suggestion report. Specific actions include suggestion generation and prompt creation based on data analysis.

[0536] 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.

[0537] This invention enables more effective automation suggestions by combining a system that uses visual and auditory information for analyzing business processes with an emotion engine that recognizes user emotions. This system is realized by collecting data from both visual and auditory perspectives using devices installed in the work environment.

[0538] First, the terminal collects video and audio in real time during work using cameras and microphones placed in the office environment. This data is temporarily stored and then sent to a server for further processing.

[0539] The server divides the received video data frame by frame and uses image analysis technology to analyze the specific actions involved in the work. Simultaneously, it converts audio data into text using speech recognition technology and analyzes the content using natural language processing technology. This analysis clarifies important instructions and repetitive steps in the business process.

[0540] Furthermore, the server's emotion engine analyzes the user's voice and facial expressions to recognize their emotions. Based on this information, stress points within the work environment and the user's emotional triggers can be identified and reflected in automation suggestions. For example, if it is recognized that a user is experiencing stress while performing a particular task, automating or improving that task will be prioritized.

[0541] The analysis results are presented to the user in the form of a report that includes optimized suggestions for automation. This allows the user to consider adopting specific measures that can help improve their business processes. If the user approves the suggested automation measures, the system will assist with their implementation and support their operation in actual business operations.

[0542] As a concrete example, considering the case of a customer support center, an emotion engine analyzes the voice and video of employees during interactions, and automated tools such as optimizing response scripts and utilizing chatbots are proposed to reduce the burden incurred during conversations with customers.

[0543] Thus, by incorporating an emotion engine, the present invention provides a system that enables personalized automation suggestions that take into account the user's emotional state, thereby achieving further improvements in operational efficiency.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The terminal uses cameras and microphones installed within the office to collect visual and auditory information about work activities in real time. The collected data is temporarily stored on the terminal.

[0547] Step 2:

[0548] The device periodically compresses the data it collects and securely uploads it to the server. The uploaded data is encrypted and protected using a secure method.

[0549] Step 3:

[0550] The server divides the visual data frame by frame and uses image recognition technology to analyze the business process. This analysis identifies and digitizes human movements and screen operations.

[0551] Step 4:

[0552] The server converts the audio data into text using speech recognition technology and analyzes it through natural language processing algorithms. This extracts the key points of conversations and instructions related to the work.

[0553] Step 5:

[0554] The server uses an emotion engine to analyze the user's emotional state from audio and video data. It identifies emotions from facial expression analysis and voice tone, and determines the user's stress level and satisfaction level.

[0555] Step 6:

[0556] The server integrates the analysis results and identifies tasks and processes that can and need to be automated. Prioritizing automation targets is selected while considering the user's emotional state.

[0557] Step 7:

[0558] The server selects the most suitable automation method for the identified task and creates a proposal. Based on AI analysis, tools such as RPA tools and chatbots are selected and customized according to the user's emotional state.

[0559] Step 8:

[0560] The server generates automation suggestions in report format and notifies the user. The user reviews the suggestions via email or a dashboard and receives detailed explanations as needed.

[0561] Step 9:

[0562] Once the user reviews the proposal and decides to implement the automation tool, the server provides support for the implementation. This includes installation guides and assistance with initial setup.

[0563] Step 10:

[0564] To measure the effectiveness of the system after the server was implemented, data will be collected and analyzed again. This analysis will confirm improvements in productivity and reductions in workload, and continuous improvement measures will be fed back to the users.

[0565] (Example 2)

[0566] 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."

[0567] In the workplace, a challenge when promoting automation to improve work efficiency is that conventional systems fail to adequately consider the context of the work and the emotions of the users. As a result, proposed automation methods may not lead to improvements in the overall business process. This invention aims to provide more practical and effective automation proposals by comprehensively analyzing visual and auditory information and further taking into account the emotional state of the user.

[0568] 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.

[0569] In this invention, the server includes means for analyzing video and audio data collected in the work environment, means for identifying automatable work procedures and generating optimization suggestions, and means for analyzing the user's voice and facial expressions to recognize their emotional state and reflect the results in the automation suggestions. This makes it possible to provide specific and effective automation suggestions that promote the improvement of business processes.

[0570] "Video data" refers to visual information recorded in a digital format within a work environment.

[0571] "Audio data" refers to acoustic information recorded in a digital format within a work environment.

[0572] "Analysis" refers to processing the obtained data, extracting information related to business processes, and interpreting it.

[0573] "Automable work procedures" are parts of a task that can be automated or made more efficient by having a system intervene to reduce manual work.

[0574] An "optimization suggestion" is a specific method or means that the system suggests to improve the efficiency of operations based on the analysis results.

[0575] "Analysis of voice and facial expressions" involves analyzing characteristics such as tone of sound and facial movements to infer emotions and states of mind.

[0576] "Emotional state" refers to the user's psychological or emotional state, including factors that contribute to workload and stress in their work.

[0577] A "report" is a document that organizes the analysis results and optimization suggestions and provides them to the user.

[0578] This invention is a system that analyzes video and audio data to generate automated suggestions that take into account the user's emotional state, with the aim of improving the efficiency of work in the work environment.

[0579] The terminal uses cameras and microphones installed in the office environment to capture video and audio in real time during work. The cameras are positioned to cover a wide area of ​​the work area, and the microphones are positioned to effectively collect audio. The collected data is encoded and temporarily stored on the terminal.

[0580] The server receives video data transmitted from the terminal and analyzes it frame by frame using image analysis libraries such as "OpenCV". Similarly, audio data is converted into text data using speech recognition software such as "Google Speech-to-Text". The converted text data is then processed using the "NLTK" library to extract important instructions and rules related to the business.

[0581] The server also analyzes the user's voice tone and facial expressions, and uses an emotion engine to recognize the user's emotional state. This analysis utilizes tools such as the "Microsoft Azure Face API" and "IBM Watson Tone Analyzer." Based on this information, automated suggestions are optimized to reduce the user's psychological burden.

[0582] The generated automation suggestions are notified to the user in report format. Based on this, the user can decide whether to adopt the suggested automation methods into their business processes. If the suggestions provided by the system are approved, the system will assist in their implementation and support improvements in business efficiency.

[0583] A concrete example is the automation tools proposed to reduce the burden on employees in customer support centers. This could include optimizing response scripts or introducing chatbots. This reduces the workload on employees and improves the efficiency of customer service.

[0584] An example of an input prompt for the generating AI model is: "To improve the operational efficiency of the customer support center, perform an emotional analysis to determine which tasks employees find stressful and propose solutions for improvement."

[0585] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0586] Step 1:

[0587] The terminal collects video and audio data using cameras and microphones in the work environment. The input is real-time video and audio, which are stored on the terminal as temporary files in digital format. After storage, the data is transmitted to the server via a secure network.

[0588] Step 2:

[0589] The server receives video data transmitted from the terminal and divides it into frames using the "OpenCV" library. The input is the received video data, and the output is the analyzed motion information. It detects specific actions, such as "checking documents" or "computer operation," and generates an action log based on these actions.

[0590] Step 3:

[0591] The server converts audio data into text data using tools such as "Google Speech-to-Text." The input is audio data, and the output is conversation information in text format. This text data is then processed using "NLTK" to extract business procedures and important communication content.

[0592] Step 4:

[0593] The server uses an emotion engine to analyze the user's voice tone and facial expression data. The input is voice tone and facial expression information in the video, and the output is the user's emotional state. This uses "Microsoft Azure Face API" and "IBM Watson Tone Analyzer" to identify the user's emotional trigger points.

[0594] Step 5:

[0595] The server generates automation suggestions based on the analysis results to date. Inputs include action logs, conversation content, and emotional state data, while output is an optimized automation suggestion that includes improvement proposals. Specifically, it suggests methods for automating tasks and the necessary tools.

[0596] Step 6:

[0597] The server compiles the generated automation suggestions into a report and notifies the user. The input is the generated report data, and the output is a document sent to the user. Based on the report, the user can consider specific measures for improving their business processes.

[0598] (Application Example 2)

[0599] 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."

[0600] In the workplace, there is a growing need to improve work efficiency by considering the emotional state of workers. However, conventional automation systems are insufficient for providing real-time guidance based on workers' emotions. Therefore, a new method is needed that reduces the burden on workers while further increasing the overall efficiency of operations.

[0601] 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.

[0602] In this invention, the server includes means for analyzing visual and auditory information collected in the work environment, means for identifying tasks that can be automated based on the analysis, means for recognizing the emotional state of the worker, means for providing real-time advice to the worker based on the emotional state, and means for proposing the most suitable automation means for the task. This makes it possible to optimize work processes while taking into account the emotional state of the worker.

[0603] "Work environment" refers to the space or system in which daily work is carried out within a company or organization.

[0604] "Visual information" refers to images and video data obtained through cameras and other image acquisition devices.

[0605] "Acoustic information" refers to audio data obtained through microphones and other sound acquisition devices.

[0606] "Means of analysis" refers to the processes and techniques used to process input data and extract or understand specific information.

[0607] "Automated tasks" refer to business processes that can be performed by machines or software without human intervention.

[0608] A "worker" refers to a person who is engaged in a business process and performs a specific task.

[0609] "Emotional state" refers to information that indicates the psychological and emotional state of a worker.

[0610] "Means of providing real-time advice" refers to processes and technologies for providing immediate advice and guidance to workers.

[0611] "Suggested means" refers to a method or process for indicating the optimal course of action or options based on the analysis results.

[0612] This invention is a system for recognizing the emotional state of workers by analyzing visual and auditory information in the work environment, and then providing real-time automated advice. This system collects data from the work environment using a terminal equipped with a camera and microphone. The data collected by the terminal is transmitted to a server, which analyzes the data to recognize the emotional state of the workers.

[0613] Specifically, the server uses the OpenCV library to process visual information and analyze the worker's facial expressions. It also uses the speech_recognition library to convert acoustic information into text data and recognizes emotions from the spoken content through natural language processing. Based on the emotional information obtained in this way, the server identifies areas where the worker is experiencing stress and provides real-time advice on the identified problems. This advice includes suggestions for improving work procedures or automating them.

[0614] For example, if a worker on a manufacturing line is taking too long on a particular operation, the system analyzes that operation and proposes an efficient work procedure, taking into account the worker's stress level. The program in this system utilizes a generative AI model to generate optimal advice tailored to specific situations.

[0615] An example of a prompt message for the generated AI model would be, "Please tell us the points where the worker feels stressed during the current task, and then provide the best automation suggestions to address them." This allows the server to provide quick and appropriate support to the worker, thereby improving the overall efficiency of the work.

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The terminal uses its camera and microphone to collect visual and auditory information in real time within the work environment. This collected data is then transferred to a server.

[0619] Step 2:

[0620] The server divides the received visual information into frames and analyzes the worker's facial expressions using the OpenCV library. Here, image data is used as input, and the characteristic points and changes in facial expressions are analyzed to output the emotional state.

[0621] Step 3:

[0622] The server converts acoustic information into text data via speech recognition technology. Using the Speech_recognition library, it converts the audio data into text, and then uses that text as input for natural language processing to output the content and emotion of the speech.

[0623] Step 4:

[0624] The server's emotion engine recognizes the worker's emotional state from frame analysis and spoken content. Using the results of facial expression analysis and text analysis already obtained, it identifies situations and points where the worker is experiencing stress, and this is output as a recognition of the worker's emotional state.

[0625] Step 5:

[0626] The server uses a generative AI model to generate real-time advice for the worker based on their emotional state. The prompt used for the generative AI model is, "Please tell us what points in the current task cause the worker stress, and provide the best automation suggestions to address them." As a result, specific suggestions for improving the work procedure are output.

[0627] Step 6:

[0628] The system notifies the user of advice, allowing them to review the suggested improvements and use them to enhance efficiency in their actual work. Here, the server generates advice and notifies the user, and the content of that notification is output as work support information for the user.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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".

[0646] This invention provides an advanced analysis system that utilizes visual and acoustic information for the purpose of improving the efficiency of business processes. This system collects visual and acoustic information using devices installed in the work environment, processes this information with an analysis device to identify tasks that can be automated in the work, and then proposes the most suitable automation method to the user.

[0647] Specifically, terminals placed in the work environment use cameras and microphones to collect video and audio during work processes. This allows for the acquisition of visible work procedures and voice instructions as digital data. The collected data is transmitted to a server via the network, where detailed analysis is performed in the next step.

[0648] The server divides visual information frame by frame and analyzes human actions and changes on the screen. For acoustic information, speech recognition technology is used to transcribe it into text, and machine learning algorithms are used to extract important phrases and keywords. This makes it possible to identify areas within the overall business process that are suitable for automation, such as repetitive tasks.

[0649] Next, the server selects the optimal automation method based on past data for the identified automatable tasks and generates a proposal. For example, if analysis reveals that data entry is performed frequently, the use of an RPA tool will be recommended. If it is found that there are many customer inquiries, the introduction of an intelligent chatbot may be considered.

[0650] This recommendation information is provided to the user as a report. Based on the report, the user decides whether to implement the automation tool, and the server supports the implementation process. This includes providing tool configuration instructions and technical coaching during implementation.

[0651] As a concrete example, in one office environment, the content of meetings is routinely accumulated as audio data, and by analyzing this data, the task of creating meeting minutes can be automated. In another case, the operations on a manufacturing line are recorded as video data, allowing for the extraction of inefficient processes and the identification of processes that can be automated or made more efficient.

[0652] Thus, this system provides concrete means to support the streamlining of the entire business process and improve productivity through visual and acoustic analysis.

[0653] The following describes the processing flow.

[0654] Step 1:

[0655] The terminal uses cameras and microphones placed within the work environment to collect visual and auditory data in real time. The collected data is temporarily stored to streamline processing.

[0656] Step 2:

[0657] The data collected by the device is processed in batches at regular intervals and sent to the server via the secure upload system. The data is encrypted in a way that respects privacy.

[0658] Step 3:

[0659] The server divides the received video data into frames and applies image recognition algorithms to identify human movements, operations, and screen changes. At this point, process points are identified.

[0660] Step 4:

[0661] The server converts audio data into text using speech recognition technology, and then performs natural language processing on that text data. This allows for the extraction of important business-related instructions and key points of conversations.

[0662] Step 5:

[0663] The server integrates the analysis results from both video and audio and reviews the entire business process. Based on this review, it identifies repetitive tasks that can be automated and processes that have room for improvement.

[0664] Step 6:

[0665] Based on the identified tasks, the server selects the most suitable automation method. An AI algorithm, using past data, suggests the most effective method from among RPA tools, chatbots, and other options.

[0666] Step 7:

[0667] The server generates a report of automation suggestions and notifies the user. The user can receive the suggestions via email or a dedicated dashboard.

[0668] Step 8:

[0669] Once the user reviews the proposal and decides to implement the automation tool, the server provides detailed instructions and implementation support to ensure a smooth tool deployment.

[0670] Step 9:

[0671] The server re-collects and analyzes business process data after implementation, measures the impact of automation on productivity, and provides users with feedback to encourage continuous improvement.

[0672] (Example 1)

[0673] 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".

[0674] In today's business environment, there is a demand for the effective use of diverse data and the streamlining of business processes. However, conventional systems often lack the ability to adequately analyze visual and auditory information and identify tasks that can be automated. Furthermore, there is a challenge in proposing the optimal automation method based on these results and applying it to actual business operations.

[0675] 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.

[0676] In this invention, the server includes means for dividing visual information into frames for analyzing information collected in the work environment, means for converting acoustic information into text data using speech recognition technology, and means for extracting important data using machine learning algorithms. This makes it possible to identify tasks that can be automated based on visual and acoustic data and to propose suitable automation means.

[0677] "Work environment" refers to the physical or virtual location or situation in which work is performed within a company or organization.

[0678] "Information-collecting devices" refer to hardware devices installed in the work environment to acquire visual and auditory information.

[0679] "Visual information" refers to video data acquired from cameras, image sensors, and other sources.

[0680] "Acoustic information" refers to sound data acquired from microphones or similar audio input devices.

[0681] "Methods for dividing into frames" refer to analytical techniques for separating video information into a series of still images (frames).

[0682] "Speech recognition technology" refers to the technology that analyzes speech and converts it into text.

[0683] A "machine learning algorithm" refers to a computer program that learns specific patterns or rules from data.

[0684] "Automated tasks" refer to business tasks that are performed repetitively according to specific procedures and can therefore be automated by machines or software.

[0685] "Automation methods" refer to the means and technologies implemented to improve efficiency in business processes.

[0686] "Providing proposals as a report" means documenting analysis results and recommendations and communicating them to the user in a report format.

[0687] A "generative AI model" refers to an artificial intelligence program that generates new information or prompts based on data it has learned in advance.

[0688] This invention provides a system for improving the efficiency of processes in the work environment. Specific embodiments for carrying out the invention are described below.

[0689] The terminals are installed in the work environment and collect visual and acoustic information using high-resolution cameras and noise-canceling microphones. This allows for the acquisition of digital information of meetings, actions during work processes, and conversations.

[0690] The collected information is securely transmitted to the server via the network. Data security is ensured during this process using encryption technologies such as SSL / TLS.

[0691] The server utilizes advanced software to analyze the received data. Visual information is divided frame by frame, and machine learning algorithms are used to detect human movements and changes on the screen. For acoustic information, speech recognition technology is applied to convert speech to text, and natural language processing techniques are used to extract important phrases.

[0692] The analysis results are used to identify tasks that can be automated. For the identified processes, the server suggests the most suitable automation method, such as RPA tools or chatbots, and provides this to the user as a report. Based on this report, the user can decide which automation method to implement.

[0693] For example, this could involve automatically accumulating audio data of office meetings and using it to create meeting minutes, or analyzing video footage of manufacturing lines to identify inefficient processes. This would lead to overall streamlining of operations.

[0694] An example of a prompt message given to the AI ​​is, "Analyze meeting audio data in an office environment and suggest ways to streamline meeting minute creation."

[0695] This system provides concrete means to improve productivity by effectively utilizing visual and auditory information to help automate business processes.

[0696] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0697] Step 1:

[0698] The terminal uses a high-resolution camera and noise-canceling microphone installed in the work environment to collect visual and acoustic information. Real-time video and audio are captured as input. The output is video and audio files as digital data. At this time, the terminal automatically starts recording video and audio, saving all actions and conversations during work as digital content.

[0699] Step 2:

[0700] The terminal encrypts the collected video and audio files using a security protocol (e.g., SSL / TLS) and transmits them to the server over the network. The input is the untransmitted digital data collected in step 1, and the output is the secure, encrypted data transfer to the server. Data protection and transfer speed are paramount in this process.

[0701] Step 3:

[0702] The server divides the received visual information frame by frame and uses machine learning algorithms to analyze human actions and changes on the screen. The input is encrypted visual information, and the output is a list of structured behavioral patterns and a record of changes. To efficiently process the large amount of image data, the server performs the analysis using parallel processing.

[0703] Step 4:

[0704] The server converts acoustic information into text data through a speech recognition engine and extracts important phrases using natural language processing techniques. The input is encrypted audio information, and the output is transcribed conversation data and extracted key phrases. This step performs accurate and rapid speech-to-text conversion and analysis.

[0705] Step 5:

[0706] Based on the analysis results, the server identifies tasks that can be automated and proposes the most suitable automation methods. The input is the analysis data obtained in steps 3 and 4, and the output is a list of recommended automation methods. Here, statistical data and past cases are referred to to select the most effective solution.

[0707] Step 6:

[0708] The user receives reports from the server and considers implementing automation measures in their operations. The input is an automation suggestion report from the server, and the output is an action plan based on the user's decision. At this stage, the user develops a feasible strategy and prepares to move on to the next step.

[0709] (Application Example 1)

[0710] 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".

[0711] In manufacturing environments, the difficulty in identifying and proposing efficient work procedures is a challenge that delays the automation of business processes. In particular, a key challenge is how to quickly identify repetitive or inefficient processes performed manually and propose the most suitable automation methods.

[0712] 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.

[0713] In this invention, the server includes means for analyzing visual and acoustic information collected in the work environment, means for identifying tasks that can be automated based on the analysis, means for proposing the most suitable automation means for the tasks, means for identifying inefficient processes by having a monitoring device collect and analyze information in the manufacturing process, and means for supporting the efficiency of work processes based on the proposed automation means. This enables efficient automation of business processes in the manufacturing site.

[0714] "Work environment" refers to the place where work is performed, such as manufacturing processes or offices.

[0715] "Visual information" refers to video data collected by cameras and other image acquisition devices.

[0716] "Acoustic information" refers to audio data collected by microphones and other sound acquisition devices.

[0717] "Analysis methods" refer to the process of analyzing collected information to identify trends and specific patterns.

[0718] "Automated tasks" refer to tasks that can be completed mechanically or programmatically without requiring human intervention.

[0719] "Automation means" refers to technologies or devices introduced to perform specific tasks automatically.

[0720] An "inefficient process" refers to a manufacturing stage that requires excessive time or cost and therefore has room for improvement.

[0721] A "surveillance device" refers to a device installed to collect visual and auditory information.

[0722] "Efficiency improvement" refers to reducing time and resources in business processes and improving the productivity of operations.

[0723] The system implementing this invention consists of collecting visual and acoustic information using monitoring devices installed in the work environment, and analyzing this data on a central server. Terminals use cameras and microphones to acquire video and audio data of the work in real time. The collected data is transmitted to the server via the network.

[0724] The server uses Python, the machine learning library TensorFlow, and the image processing library OpenCV to analyze visual information frame by frame and identify human movements and screen changes. For acoustic information, the Google Cloud Speech-to-Text API is used to convert audio data into text data, which is then analyzed using natural language processing techniques to identify important phrases and instructions.

[0725] Based on the analysis results, the server identifies tasks that can be automated and recommends the optimal automation method for those tasks. This process uses a generative AI model to suggest methods for maximizing the efficiency of business processes. As a result, users can implement automation based on the suggested methods and improve work efficiency.

[0726] As a concrete example, in a food manufacturing plant, monitoring devices may patrol the bread production line, collecting and analyzing video and audio of the filling process to identify inefficient work and recommend the introduction of robots in those processes. In this way, successful automation has the potential to dramatically improve production efficiency.

[0727] An example of a prompt message would be: "The robot monitoring the equipment has identified the following inefficient tasks. Please perform a detailed data analysis and suggest the best automation solutions." Through this process, users can gain useful insights to improve their processes and increase productivity.

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The device captures visual information of the work environment using a camera and records audio information using a microphone. The input at this stage is the video and audio of the actual work. The output is a data file of the visual and audio information converted into a digital format. Specifically, camera capture and microphone recording occur simultaneously.

[0731] Step 2:

[0732] The terminal transmits the acquired digital data to the server in real time via the network. The input is the digital data converted in step 1. The output is the data transferred in a format accessible to the server. Specifically, data transfer is performed using a communication protocol (e.g., TCP / IP).

[0733] Step 3:

[0734] The server divides the received visual information into frames and analyzes each frame using OpenCV. The input is the visual data sent from the terminal. The output is the analysis result, which identifies patterns and changes in movement. Specifically, the process involves processing each frame and detecting and identifying movement.

[0735] Step 4:

[0736] The server converts acoustic information into text data using the Google Cloud Speech-to-Text API and extracts important phrases and commands through natural language processing. The input is acoustic data sent from the terminal. The output is the analyzed text data and the extracted important phrases. Specifically, the process involves transcribing audio data into text and extracting keywords.

[0737] Step 5:

[0738] The server combines the frame analysis results and the speech analysis results to identify tasks that can be automated using a machine learning model. The input is the analysis results obtained from steps 3 and 4. The output is a list of tasks that can be automated. Specific actions include analysis and prediction by a generative AI model.

[0739] Step 6:

[0740] The server prompts the user with the optimal automation method for the identified automatable tasks. The input is the task list obtained in step 5. The output is a user-readable suggestion report. Specific actions include suggestion generation and prompt creation based on data analysis.

[0741] 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.

[0742] This invention enables more effective automation suggestions by combining a system that uses visual and auditory information for analyzing business processes with an emotion engine that recognizes user emotions. This system is realized by collecting data from both visual and auditory perspectives using devices installed in the work environment.

[0743] First, the terminal collects video and audio in real time during work using cameras and microphones placed in the office environment. This data is temporarily stored and then sent to a server for further processing.

[0744] The server divides the received video data frame by frame and uses image analysis technology to analyze the specific actions involved in the work. Simultaneously, it converts audio data into text using speech recognition technology and analyzes the content using natural language processing technology. This analysis clarifies important instructions and repetitive steps in the business process.

[0745] Furthermore, the server's emotion engine analyzes the user's voice and facial expressions to recognize their emotions. Based on this information, stress points within the work environment and the user's emotional triggers can be identified and reflected in automation suggestions. For example, if it is recognized that a user is experiencing stress while performing a particular task, automating or improving that task will be prioritized.

[0746] The analysis results are presented to the user in the form of a report that includes optimized suggestions for automation. This allows the user to consider adopting specific measures that can help improve their business processes. If the user approves the suggested automation measures, the system will assist with their implementation and support their operation in actual business operations.

[0747] As a concrete example, considering the case of a customer support center, an emotion engine analyzes the voice and video of employees during interactions, and automated tools such as optimizing response scripts and utilizing chatbots are proposed to reduce the burden incurred during conversations with customers.

[0748] Thus, by incorporating an emotion engine, the present invention provides a system that enables personalized automation suggestions that take into account the user's emotional state, thereby achieving further improvements in operational efficiency.

[0749] The following describes the processing flow.

[0750] Step 1:

[0751] The terminal uses cameras and microphones installed within the office to collect visual and auditory information about work activities in real time. The collected data is temporarily stored on the terminal.

[0752] Step 2:

[0753] The device periodically compresses the data it collects and securely uploads it to the server. The uploaded data is encrypted and protected using a secure method.

[0754] Step 3:

[0755] The server divides the visual data frame by frame and uses image recognition technology to analyze the business process. This analysis identifies and digitizes human movements and screen operations.

[0756] Step 4:

[0757] The server converts the audio data into text using speech recognition technology and analyzes it through natural language processing algorithms. This extracts the key points of conversations and instructions related to the work.

[0758] Step 5:

[0759] The server uses an emotion engine to analyze the user's emotional state from audio and video data. It identifies emotions from facial expression analysis and voice tone, and determines the user's stress level and satisfaction level.

[0760] Step 6:

[0761] The server integrates the analysis results and identifies tasks and processes that can and need to be automated. Prioritizing automation targets is selected while considering the user's emotional state.

[0762] Step 7:

[0763] The server selects the most suitable automation method for the identified task and creates a proposal. Based on AI analysis, tools such as RPA tools and chatbots are selected and customized according to the user's emotional state.

[0764] Step 8:

[0765] The server generates automation suggestions in report format and notifies the user. The user reviews the suggestions via email or a dashboard and receives detailed explanations as needed.

[0766] Step 9:

[0767] Once the user reviews the proposal and decides to implement the automation tool, the server provides support for the implementation. This includes installation guides and assistance with initial setup.

[0768] Step 10:

[0769] To measure the effectiveness of the system after the server was implemented, data will be collected and analyzed again. This analysis will confirm improvements in productivity and reductions in workload, and continuous improvement measures will be fed back to the users.

[0770] (Example 2)

[0771] 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".

[0772] In the workplace, a challenge when promoting automation to improve work efficiency is that conventional systems fail to adequately consider the context of the work and the emotions of the users. As a result, proposed automation methods may not lead to improvements in the overall business process. This invention aims to provide more practical and effective automation proposals by comprehensively analyzing visual and auditory information and further taking into account the emotional state of the user.

[0773] 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.

[0774] In this invention, the server includes means for analyzing video and audio data collected in the work environment, means for identifying automatable work procedures and generating optimization suggestions, and means for analyzing the user's voice and facial expressions to recognize their emotional state and reflect the results in the automation suggestions. This makes it possible to provide specific and effective automation suggestions that promote the improvement of business processes.

[0775] "Video data" refers to visual information recorded in a digital format within a work environment.

[0776] "Audio data" refers to acoustic information recorded in a digital format within a work environment.

[0777] "Analysis" refers to processing the obtained data, extracting information related to business processes, and interpreting it.

[0778] "Automable work procedures" are parts of a task that can be automated or made more efficient by having a system intervene to reduce manual work.

[0779] An "optimization suggestion" is a specific method or means that the system suggests to improve the efficiency of operations based on the analysis results.

[0780] "Analysis of voice and facial expressions" involves analyzing characteristics such as tone of sound and facial movements to infer emotions and states of mind.

[0781] "Emotional state" refers to the user's psychological or emotional state, including factors that contribute to workload and stress in their work.

[0782] A "report" is a document that organizes the analysis results and optimization suggestions and provides them to the user.

[0783] This invention is a system that analyzes video and audio data to generate automated suggestions that take into account the user's emotional state, with the aim of improving the efficiency of work in the work environment.

[0784] The terminal uses cameras and microphones installed in the office environment to capture video and audio in real time during work. The cameras are positioned to cover a wide area of ​​the work area, and the microphones are positioned to effectively collect audio. The collected data is encoded and temporarily stored on the terminal.

[0785] The server receives video data transmitted from the terminal and analyzes it frame by frame using image analysis libraries such as "OpenCV". Similarly, audio data is converted into text data using speech recognition software such as "Google Speech-to-Text". The converted text data is then processed using the "NLTK" library to extract important instructions and rules related to the business.

[0786] The server also analyzes the user's voice tone and facial expressions, and uses an emotion engine to recognize the user's emotional state. This analysis utilizes tools such as the "Microsoft Azure Face API" and "IBM Watson Tone Analyzer." Based on this information, automated suggestions are optimized to reduce the user's psychological burden.

[0787] The generated automation suggestions are notified to the user in report format. Based on this, the user can decide whether to adopt the suggested automation methods into their business processes. If the suggestions provided by the system are approved, the system will assist in their implementation and support improvements in business efficiency.

[0788] A concrete example is the automation tools proposed to reduce the burden on employees in customer support centers. This could include optimizing response scripts or introducing chatbots. This reduces the workload on employees and improves the efficiency of customer service.

[0789] An example of an input prompt for the generating AI model is: "To improve the operational efficiency of the customer support center, perform an emotional analysis to determine which tasks employees find stressful and propose solutions for improvement."

[0790] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0791] Step 1:

[0792] The terminal collects video and audio data using cameras and microphones in the work environment. The input is real-time video and audio, which are stored on the terminal as temporary files in digital format. After storage, the data is transmitted to the server via a secure network.

[0793] Step 2:

[0794] The server receives video data transmitted from the terminal and divides it into frames using the "OpenCV" library. The input is the received video data, and the output is the analyzed motion information. It detects specific actions, such as "checking documents" or "computer operation," and generates an action log based on these actions.

[0795] Step 3:

[0796] The server converts audio data into text data using tools such as "Google Speech-to-Text." The input is audio data, and the output is conversation information in text format. This text data is then processed using "NLTK" to extract business procedures and important communication content.

[0797] Step 4:

[0798] The server uses an emotion engine to analyze the user's voice tone and facial expression data. The input is voice tone and facial expression information in the video, and the output is the user's emotional state. This uses "Microsoft Azure Face API" and "IBM Watson Tone Analyzer" to identify the user's emotional trigger points.

[0799] Step 5:

[0800] The server generates automation suggestions based on the analysis results to date. Inputs include action logs, conversation content, and emotional state data, while output is an optimized automation suggestion that includes improvement proposals. Specifically, it suggests methods for automating tasks and the necessary tools.

[0801] Step 6:

[0802] The server compiles the generated automation suggestions into a report and notifies the user. The input is the generated report data, and the output is a document sent to the user. Based on the report, the user can consider specific measures for improving their business processes.

[0803] (Application Example 2)

[0804] 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".

[0805] In the workplace, there is a growing need to improve work efficiency by considering the emotional state of workers. However, conventional automation systems are insufficient for providing real-time guidance based on workers' emotions. Therefore, a new method is needed that reduces the burden on workers while further increasing the overall efficiency of operations.

[0806] 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.

[0807] In this invention, the server includes means for analyzing visual and auditory information collected in the work environment, means for identifying tasks that can be automated based on the analysis, means for recognizing the emotional state of the worker, means for providing real-time advice to the worker based on the emotional state, and means for proposing the most suitable automation means for the task. This makes it possible to optimize work processes while taking into account the emotional state of the worker.

[0808] "Work environment" refers to the space or system in which daily work is carried out within a company or organization.

[0809] "Visual information" refers to images and video data obtained through cameras and other image acquisition devices.

[0810] "Acoustic information" refers to audio data obtained through microphones and other sound acquisition devices.

[0811] "Means of analysis" refers to the processes and techniques used to process input data and extract or understand specific information.

[0812] "Automated tasks" refer to business processes that can be performed by machines or software without human intervention.

[0813] A "worker" refers to a person who is engaged in a business process and performs a specific task.

[0814] "Emotional state" refers to information that indicates the psychological and emotional state of a worker.

[0815] "Means of providing real-time advice" refers to processes and technologies for providing immediate advice and guidance to workers.

[0816] "Suggested means" refers to a method or process for indicating the optimal course of action or options based on the analysis results.

[0817] This invention is a system for recognizing the emotional state of workers by analyzing visual and auditory information in the work environment, and then providing real-time automated advice. This system collects data from the work environment using a terminal equipped with a camera and microphone. The data collected by the terminal is transmitted to a server, which analyzes the data to recognize the emotional state of the workers.

[0818] Specifically, the server uses the OpenCV library to process visual information and analyze the worker's facial expressions. It also uses the speech_recognition library to convert acoustic information into text data and recognizes emotions from the spoken content through natural language processing. Based on the emotional information obtained in this way, the server identifies areas where the worker is experiencing stress and provides real-time advice on the identified problems. This advice includes suggestions for improving work procedures or automating them.

[0819] For example, if a worker on a manufacturing line is taking too long on a particular operation, the system analyzes that operation and proposes an efficient work procedure, taking into account the worker's stress level. The program in this system utilizes a generative AI model to generate optimal advice tailored to specific situations.

[0820] An example of a prompt message for the generated AI model would be, "Please tell us the points where the worker feels stressed during the current task, and then provide the best automation suggestions to address them." This allows the server to provide quick and appropriate support to the worker, thereby improving the overall efficiency of the work.

[0821] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0822] Step 1:

[0823] The terminal uses its camera and microphone to collect visual and auditory information in real time within the work environment. This collected data is then transferred to a server.

[0824] Step 2:

[0825] The server divides the received visual information into frames and analyzes the worker's facial expressions using the OpenCV library. Here, image data is used as input, and the characteristic points and changes in facial expressions are analyzed to output the emotional state.

[0826] Step 3:

[0827] The server converts acoustic information into text data via speech recognition technology. Using the Speech_recognition library, it converts the audio data into text, and then uses that text as input for natural language processing to output the content and emotion of the speech.

[0828] Step 4:

[0829] The server's emotion engine recognizes the worker's emotional state from frame analysis and spoken content. Using the results of facial expression analysis and text analysis already obtained, it identifies situations and points where the worker is experiencing stress, and this is output as a recognition of the worker's emotional state.

[0830] Step 5:

[0831] The server uses a generative AI model to generate real-time advice for the worker based on their emotional state. The prompt used for the generative AI model is, "Please tell us what points in the current task cause the worker stress, and provide the best automation suggestions to address them." As a result, specific suggestions for improving the work procedure are output.

[0832] Step 6:

[0833] The system notifies the user of advice, allowing them to review the suggested improvements and use them to enhance efficiency in their actual work. Here, the server generates advice and notifies the user, and the content of that notification is output as work support information for the user.

[0834] 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.

[0835] 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.

[0836] 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 robot 414.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] The following is further disclosed regarding the embodiments described above.

[0856] (Claim 1)

[0857] A device for analyzing visual and auditory information collected in the work environment,

[0858] A device that identifies tasks that can be automated based on the aforementioned analysis,

[0859] A device that proposes an optimal automation method for the aforementioned task,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, further comprising means for dividing and processing the aforementioned visual information frame by frame.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising means for converting the aforementioned acoustic information into text data and performing natural language processing.

[0865] "Example 1"

[0866] (Claim 1)

[0867] A device for collecting information in the work environment,

[0868] In order to analyze the aforementioned information, means for dividing the visual information into frame units,

[0869] A means of converting acoustic information into text data using speech recognition technology,

[0870] A method for extracting important data using machine learning algorithms,

[0871] A device that identifies tasks that can be automated based on the aforementioned analysis,

[0872] A means of proposing the optimal automation method based on past data,

[0873] A device that provides users with a report containing recommendations, allowing users to decide whether to implement automation tools,

[0874] Means for configuring the aforementioned tools and providing technical support,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, further comprising a function for performing natural language processing with respect to the aforementioned acoustic information.

[0878] (Claim 3)

[0879] The system according to claim 1, comprising a generative AI model that generates prompt sentences based on an automateable process identified from the analyzed data.

[0880] "Application Example 1"

[0881] (Claim 1)

[0882] A means for analyzing visual and auditory information collected in the work environment,

[0883] A means for identifying tasks that can be automated based on the aforementioned analysis,

[0884] A means of proposing an automation method best suited to the aforementioned task,

[0885] A means of identifying inefficient processes in the manufacturing process by having monitoring devices collect and analyze information,

[0886] A means to support the efficiency of work processes based on the proposed automation means,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, further comprising means for dividing and processing the aforementioned visual information frame by frame.

[0890] (Claim 3)

[0891] The system according to claim 1, further comprising means for converting the aforementioned acoustic information into text data and performing natural language processing.

[0892] "Example 2 of combining an emotion engine"

[0893] (Claim 1)

[0894] A means for analyzing video and audio data collected in the work environment,

[0895] Based on the aforementioned analysis, a means for identifying automatable business procedures and generating optimization proposals,

[0896] A means of analyzing the user's voice and facial expressions to recognize their emotional state and reflecting the results in automated suggestions,

[0897] A means of notifying the user of the generated automation suggestions as a report,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, further comprising means for dividing the aforementioned video data into frames and performing image analysis.

[0901] (Claim 3)

[0902] The system according to claim 1, further comprising means for converting the aforementioned audio data into text information and performing natural language processing.

[0903] "Application example 2 when combining with an emotional engine"

[0904] (Claim 1)

[0905] A means for analyzing visual and auditory information collected in the work environment,

[0906] A means for identifying tasks that can be automated based on the aforementioned analysis,

[0907] Means for recognizing the emotional state of workers,

[0908] A means for providing real-time advice to the worker based on the aforementioned emotional state,

[0909] A means of proposing an automation method best suited to the aforementioned task,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for dividing and processing the aforementioned visual information frame by frame and analyzing the facial expressions of the worker.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for converting the aforementioned acoustic information into text data and performing natural language processing to analyze emotions from the worker's speech. [Explanation of Symbols]

[0915] 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. A device for analyzing visual and auditory information collected in the work environment, A device that identifies tasks that can be automated based on the aforementioned analysis, A device that proposes an optimal automation method for the aforementioned task, A system that includes this.

2. The system according to claim 1, further comprising means for dividing and processing the aforementioned visual information frame by frame.

3. The system according to claim 1, further comprising means for converting the aforementioned acoustic information into text data and performing natural language processing.

Images