system

Abstract

We provide the system. [Solution] An integrated circuit having communication functions, A processing device that performs data analysis using a neural network, connected to the integrated circuit using common connection technology, A control system that automatically makes decisions and generates control signals based on data analyzed by the aforementioned processing device, 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 current communication devices, there are problems that since the communication function and the AI processing function are separated, advanced data analysis and real-time processing are difficult. Also, communication devices are inefficient in the selection and operation of network protocols, and the accompanying increase in operation costs has become a problem. Furthermore, there is room for improvement in the security in their operation, and real-time threat detection and response are required.

Means for Solving the Problems

[0005] To address this challenge, we provide a technology that integrates an integrated circuit with communication capabilities and a processing unit using a neural network to perform data analysis in real time. The processing unit automatically generates control signals based on the analyzed data, enabling efficient device control. Furthermore, when connecting to a network, it selects an optimized protocol and transmits data to an external device for further analysis. This results in improved communication efficiency, device automation, and enhanced security.

[0006] "Communication function" refers to a device's ability to send and receive data over a network.

[0007] An "integrated circuit" is a circuit formed by integrating multiple electronic components, and it performs a specific function.

[0008] "Common connectivity technology" refers to a consistent communication protocol or interface that enables communication between different devices.

[0009] A "neural network" is a form of artificial intelligence built to mimic the neural circuits of the human brain, and is a technology used for data analysis and pattern recognition.

[0010] A "processing device" is a device that receives given data and performs calculations and control.

[0011] A "control signal" is an electrical or electronic signal that instructs a device or system to perform a specific action.

[0012] A "control system" is a set of mechanisms that generate control signals based on inputs and automatically adjust and control devices and processes.

[0013] An "external device" is an electronic device or hardware that is located outside the system but is capable of communicating with the system and exchanging information. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main 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. [[ID=​​​​​​​​​​​ 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled 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.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled 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, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] One embodiment of the present invention is a system that performs efficient data analysis and automatic control by linking an integrated circuit with communication capabilities with a processing unit using a neural network. This system is mounted on an electronic device, collects information in real time, and establishes optimal network connectivity via the integrated circuit. The processing unit analyzes the collected data using a neural network model and generates control signals based on the results to optimally control the device.

[0036] As a concrete example, let's consider an implementation in a smart home environment. A smart sensor, acting as a terminal, collects environmental data such as temperature, humidity, and light intensity, and connects to the home network via an integrated circuit. The terminal sends this information to a processing unit in real time, and through neural network analysis, automatically calculates the optimal air conditioning settings to maintain a comfortable indoor environment. The control signals generated by the terminal automatically adjust the air conditioner, optimizing energy efficiency.

[0037] Furthermore, the present invention is also effective in wearable devices for health monitoring. The device worn by the user measures heart rate and activity levels, and transmits the data to a cloud server in real time via an integrated circuit. The server in the cloud analyzes the data at high speed and sends activity recommendations and abnormality detection results to the terminal. As a result, the user can understand their health status in real time and lead a safe and meaningful life.

[0038] Thus, the system of the present invention efficiently integrates communication and data analysis, providing effective solutions in various application fields.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] When the device is powered on, its built-in integrated circuit starts working, selecting the optimal communication protocol and connecting to the network. This enables real-time communication for the device.

[0042] Step 2:

[0043] The terminal collects necessary data through connected sensors. This data, for example, in the case of a temperature sensor, acquires indoor temperature data and supplies it to the system.

[0044] Step 3:

[0045] The terminal transmits the collected data to its own processing unit, which then uses a neural network to perform initial analysis. This data analysis helps to identify the cause of any abnormal temperature changes, for example.

[0046] Step 4:

[0047] The terminal generates a control signal based on the analyzed results. This signal is sent to the control system, which automatically adjusts devices such as air conditioners to their optimal settings.

[0048] Step 5:

[0049] Analysis results and device status are sent to a server via the network for further analysis. The server performs more advanced data integration and analysis in the cloud and stores the results.

[0050] Step 6:

[0051] Based on the analysis results, the server generates new analysis results and recommendations and sends them to the terminal. This updates the device's control and user notifications.

[0052] Step 7:

[0053] Users receive notifications and feedback from their devices, adjust their settings, and check their health data. This supports a comfortable and healthy lifestyle.

[0054] (Example 1)

[0055] 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."

[0056] Conventional data analysis systems struggle with real-time information processing and automatic equipment optimization, lacking sufficient reliability and adaptability in situations requiring efficient and rapid control. In particular, achieving optimal control adapted to fluctuating environmental conditions is a challenge.

[0057] 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.

[0058] In this invention, the server includes means for collecting environmental information using electronic circuits with communication functions, a computing device for analyzing the information using a machine learning model, and a decision system for generating optimal control signals. This enables real-time information analysis and automatic control of equipment.

[0059] An "electronic circuit with communication capabilities" is a technological element that has a circuit configuration capable of accurately and quickly collecting environmental information and transmitting it via a network.

[0060] A "machine learning model" is a model that incorporates algorithms capable of pattern recognition and prediction by analyzing large amounts of data.

[0061] A "processing unit" is a hardware or software configuration designed for data analysis that can efficiently manage processing load.

[0062] A "control signal" is a signal generated based on analyzed data that contains instructions for automatically adjusting equipment or systems to a desired operating state.

[0063] A "decision-making system" is a system that has logic and algorithms for evaluating analysis results and generating the optimal control signal.

[0064] "Means for transmitting signals to automatically optimize equipment control" refers to technical means that have the function of transmitting instructions so that the equipment reaches an optimal operating state based on predetermined conditions.

[0065] This invention is a system that efficiently analyzes environmental information and automatically controls equipment by linking an electronic circuit with communication capabilities with a machine learning model. Specific embodiments are described below.

[0066] The device has an electronic circuit that includes sensors for collecting environmental data such as temperature, humidity, and light intensity. This electronic circuit is equipped with communication means for efficiently transmitting the collected data to a processing unit, and it is possible to upload the data to a cloud environment using Wi-Fi or other communication protocols.

[0067] The server is a computing device configured in the cloud that uses machine learning models to analyze data in real time. This process utilizes a neural network model to derive optimal control settings for air conditioners and other equipment based on the obtained data. Through this program's processing, the equipment automatically adjusts to a temperature and state suitable for user comfort, resulting in efficient energy use.

[0068] Users can receive feedback from the system through their devices. For example, they can instantly see notifications such as whether the room is being kept at the ideal temperature.

[0069] Examples of specific prompt messages include the following:

[0070] "Please create a program to optimize the management of air conditioning in a smart home."

[0071] "We are looking for an algorithm that analyzes data and automatically sets up a comfortable indoor environment."

[0072] Thus, specific embodiments of the invention provide real-time comfort and efficiency through the collection and analysis of environmental information and automatic equipment control based on the results.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The device collects environmental data. For example, a temperature sensor reads the current room temperature, and a humidity sensor measures the humidity of the air. This collected data is temporarily stored in the device's memory. The input is the analog signal obtained from the sensor, and the output is the environmental data converted into a digital signal.

[0076] Step 2:

[0077] The device collects environmental data and transmits it to the cloud via its communication function. This data transfer uses Wi-Fi or other network protocols. The input is the digital environmental data stored on the device, and the output is the data transmitted to the server via the network.

[0078] Step 3:

[0079] The server receives data in the cloud and analyzes it using a machine learning model. In this analysis step, a neural network is used to estimate the optimal settings for the indoor environment. The input is environmental data received via the network, and the output is information on the optimal control settings.

[0080] Step 4:

[0081] The server generates control signals based on the analysis results. These control signals include instructions for the operation of specific equipment. The input is the analyzed optimal setting information, and the output is the control signal. For example, a specific instruction such as "set the air conditioner to 25 degrees" is generated.

[0082] Step 5:

[0083] The server generates a control signal and sends it to the terminal. The input is the control signal generated by the server, and the output is the signal transmitted via communication to the terminal. This operation starts the configured operation of the environmental equipment.

[0084] Step 6:

[0085] The terminal controls the equipment based on the control signals it receives. For example, the temperature setting of an air conditioner is changed based on the received signal. The input is the control signal received from the server, and the output is the operating state of the adjusted equipment. The equipment is automatically optimized to maintain user comfort.

[0086] (Application Example 1)

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

[0088] Inventory management within factories often relies heavily on physical checks and manual data entry, leading to inefficiencies and human error. Furthermore, insufficient optimization of inventory placement and replenishment processes poses a challenge to improving productivity.

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

[0090] In this invention, the server includes an electronic circuit means having a communication function, a computing device means connected to the electronic circuit means using common connection technology and performing information analysis using artificial intelligence, a management device means that automatically makes decisions and generates control signals based on the information analyzed by the computing device means, a sensing means for reading identification information of items, and a processing means for generating a plan to optimize the placement of items based on the identification information collected by the sensing means. This makes it possible to automate inventory management within a factory and achieve efficient placement and replenishment.

[0091] "Electronic circuit means having communication functions" refers to an electronic circuit structure equipped with communication capabilities for exchanging information with an external system.

[0092] "Common connectivity technology" refers to standardized technologies that enable interconnection between different devices and systems.

[0093] A "computational device that performs information analysis using artificial intelligence" is a computing device that uses artificial intelligence to analyze data and make decisions for a specific purpose.

[0094] A "management device that automatically makes decisions and generates control signals" is a management system that automatically creates signals to control machines and systems based on analysis results.

[0095] "Sensing means for reading identification information of an item" refers to a sensor or reader for collecting identification information associated with a product or item.

[0096] "Processing means for generating a plan to optimize the arrangement of items" refers to a device that has the data processing capability to replan the efficient arrangement and movement of items using collected information.

[0097] In a system that implements an application example of this invention, multiple hardware and software components work together. First, the server is connected to the factory network using electronic circuitry with communication capabilities. This electronic circuitry supports wireless communication protocols such as Wi-Fi and 5G, and transmits and receives information in real time. IoT protocols (e.g., MQTT and HTTP) are used as common connectivity technologies.

[0098] The computing unit utilizes software libraries such as TENSORFLOW® and PyTorch in cloud computing environments such as Google® Cloud Platform and AWS® to perform information analysis using artificial intelligence. The information to be analyzed is data from barcodes and RFID tags attached to items in the factory, which are collected by sensing devices mounted on robots.

[0099] Based on the analyzed data, the control device generates control signals and sends instructions to the robot regarding optimal inventory placement and replenishment. This automated process allows users to efficiently manage inventory within the factory. Users can use their smartphones to check information in real time through a dedicated app and issue replenishment instructions remotely when needed.

[0100] As a concrete example, in one factory, the introduction of this system reduced the time required for inventory checks by half. This significantly improved work efficiency and reduced work errors.

[0101] An example of a prompt is, "Please describe a robot control system that utilizes neural networks as a means to streamline inventory management within a factory." This prompt is used when a generative AI model is employed to provide insights into system usage and effective operational methods.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The terminal reads barcodes and RFID tags attached to items within the factory.

[0105] The input is the identification information associated with each item, and the output is a dataset containing this identification information.

[0106] The device uses its built-in sensors to acquire information about items and collects this data in real time.

[0107] Step 2:

[0108] The terminal transmits the identification information it collects in real time to the server via electronic circuitry.

[0109] The input is the dataset obtained in Step 1, and the output is a database entry used for information analysis on the cloud.

[0110] The device utilizes Wi-Fi and 5G network protocols to transmit data to the cloud server at high speed.

[0111] Step 3:

[0112] The server uses artificial intelligence to analyze the transmitted identification information.

[0113] The input consists of database entries stored on the server, and the output is the analysis results regarding optimal inventory placement and necessary replenishment operations.

[0114] The server uses TensorFlow or PyTorch to run neural network models and extract efficient inventory management methods.

[0115] Step 4:

[0116] The server generates control signals based on the analysis results and sends instructions to the robot.

[0117] The input is the analysis results obtained in step 3, and the output is the specific work procedure that the robot will perform.

[0118] The server uses a control device to generate control signals and automates inventory management through robots.

[0119] Step 5:

[0120] Users can monitor inventory status and robot operations through a smartphone app.

[0121] The input is the latest inventory information retrieved from the server, and the output is a display of a dashboard accessible to the user.

[0122] Users can use the app to check inventory status from home or on the go and issue direct instructions to the system as needed.

[0123] 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.

[0124] One embodiment of the present invention is a system that combines an integrated circuit with communication capabilities, a processing unit using a neural network, and an emotion engine that recognizes the user's emotions. This system is mounted on an electronic device and recognizes emotions in real time using the user's voice and facial expression data. The recognized emotions are analyzed by the processing unit, and the optimal operating method is determined based on the results.

[0125] As a concrete example, let's describe an embodiment in a smart home environment. The smart speaker, acting as a terminal, collects emotions along with the user's voice commands. The emotion engine analyzes the user's emotional state from their voice tone, tempo, and word choices. For example, if the emotion engine determines that the user is tired, the terminal adjusts the room lighting to a comfortable brightness and plays music to support relaxation.

[0126] Furthermore, this system is also effective in smart systems within meeting rooms. The terminal captures participants' facial expressions through a camera, and an emotion engine analyzes the atmosphere of the meeting. Based on the analysis results, the server dynamically adjusts the content and presentation method of materials, contributing to the efficiency of the meeting.

[0127] Thus, the system of the present invention grasps the user's emotional state in real time and performs operations appropriate to it, thereby providing a high level of user experience tailored to individual needs.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] When the terminal is powered on, it connects to the network via an integrated circuit with communication capabilities and begins collecting data from various sensors and microphones.

[0131] Step 2:

[0132] The device acquires voice commands from the user and surrounding environment data, and sends this to the emotion engine. At this stage, the voice data is broken down into parameters such as volume, tone, and pitch.

[0133] Step 3:

[0134] The emotion engine analyzes the received audio data to determine the user's emotions. For example, if the user's voice is calm and spoken at a slow pace, it is recognized as a relaxed emotional state.

[0135] Step 4:

[0136] The terminal sends the results of the emotion engine's analysis to the processing unit, where further data analysis is performed using a neural network. Based on this analysis, specific operation patterns are created.

[0137] Step 5:

[0138] The terminal generates control signals to control the device according to the created operation pattern. For example, if it determines that the user is tired, it generates a control signal to change the lighting to a warmer color.

[0139] Step 6:

[0140] The terminal sends control signals to the device, and the results are reflected in the user's environment. This allows the device to start its intended operation and provide the user with a comfortable environment.

[0141] Step 7:

[0142] The collected sentiment data is sent back to the server for long-term user sentiment analysis. The server uses this information to build a feedback loop that improves the overall system performance.

[0143] (Example 2)

[0144] 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".

[0145] In modern society, there is a growing need for interactive systems that adjust the environment based on the emotions of individual users. However, conventional systems have struggled to accurately recognize users' emotional states in real time and dynamically optimize the environment. Therefore, providing a high-quality user experience tailored to individual needs has been difficult.

[0146] 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.

[0147] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs data analysis using a neural network, a control system that automatically makes decisions and generates control signals based on the individual's emotional state data analyzed by the information processing device, means for collecting the user's voice data and facial expression data in real time and recognizing emotions, means for analyzing voice tone, tempo, and word choice using an emotion engine and determining the emotional state, and means for dynamically adjusting the environment based on the emotions determined by the terminal. This makes it possible to automatically optimize the environment in response to the user's emotions and provide a customized user experience tailored to the individual.

[0148] "Communication function" refers to the ability of a device to send and receive information with other devices or networks.

[0149] An "integrated circuit" refers to an electronic component that integrates a large number of semiconductor elements at high density to form a circuit that realizes a specific function.

[0150] "Common connectivity technology" refers to standardized connection methods for exchanging data and signals between different devices and systems.

[0151] A "neural network" refers to an algorithm or system for processing information that is modeled after the neural circuits of the human brain.

[0152] An "information processing device" refers to a device that receives input data, performs necessary calculations and analyses, and outputs useful results.

[0153] "Individual emotional state data" refers to a collection of data that shows an individual's emotions at a specific time.

[0154] A "control signal" refers to a signal generated within a system to instruct a specific action.

[0155] A "control system" refers to the entire system that generates signals to instruct actions based on the results of emotion analysis.

[0156] "Audio data" refers to digital or analog recordings of human voices that have been recorded or transmitted.

[0157] "Facial expression data" refers to data that contains information about an individual's facial expressions.

[0158] An "emotion engine" refers to an algorithm or process that analyzes input voice data and facial expression data to determine the user's emotional state.

[0159] "Voice tone" refers to the pitch, volume, and other sonic characteristics of a voice.

[0160] "Tempo" refers to the speed or rhythm of speech or music.

[0161] "Means for dynamically adjusting the environment" refers to a method or device for a system to automatically change environmental conditions in response to the user's emotional state.

[0162] "Real-time" refers to information processing that occurs instantly with little to no time delay.

[0163] As an embodiment of the present invention, a system is provided that combines an integrated circuit with communication capabilities, an information processing device using a neural network, and an emotion engine. This system is mounted on an electronic device, and a server collects and analyzes the user's voice data and facial expression data in real time to recognize the user's emotions. Specifically, the server inputs the voice data and facial expression data into the emotion engine, which analyzes the voice tone, tempo, and word choice.

[0164] The device receives the results of an emotion engine analysis based on the user's voice and determines the specific emotional state. This allows it to generate control signals necessary to dynamically optimize the environment according to the user's emotions, automatically adjusting lighting and sound environments. For example, if the user says, "I want to relax today," the device will soften the room lighting and play relaxing music.

[0165] Furthermore, for example, within a meeting room, a terminal can monitor participants' facial expressions, and an emotion engine can analyze the atmosphere of the meeting. The server immediately receives this analysis result and improves the efficiency of the meeting by changing how materials are presented, etc. A concrete example of a prompt message would be, "Set the optimal lighting and music to reduce user stress," which would quickly guide the system to the optimal environment settings.

[0166] Thus, the present invention is an excellent interactive system that can understand the user's emotional state in real time and dynamically create an environment that meets individual needs. This technology makes it possible to provide a higher level of user experience.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user issues a voice command, which is input via the device's microphone. The device collects this voice data in real time and saves it as an audio file. This file serves as the baseline data for emotion recognition.

[0170] Step 2:

[0171] After the user's voice data is input into the device, the device sends that voice data to the emotion engine. The emotion engine analyzes the voice tone, tempo, word choice, etc. This analysis process determines the user's emotional state (for example, whether they are relaxed or stressed).

[0172] Step 3:

[0173] The server automatically generates control signals to determine the optimal environment settings based on the results analyzed by the emotion engine. In this process, a generating AI model creates action scenarios using prompt statements, and then makes specific lighting settings and music selections based on these prompt statements. For example, if a prompt statement such as "the user wants to relax" is used, the environment settings are dynamically optimized.

[0174] Step 4:

[0175] The device uses control signals received from the server to perform actual actions. For example, the device might adjust the brightness and color temperature of the lighting, or play music that helps the user relax. These actions are performed in a way that best suits the user's current emotional state, improving the user experience.

[0176] Step 5:

[0177] Users can provide feedback on the environmental adjustments made by their device. The server collects this feedback and uses it to improve the generated AI model, enabling more appropriate adjustments in subsequent operations.

[0178] (Application Example 2)

[0179] 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 device 14 will be referred to as the "terminal."

[0180] In factories, improving work efficiency and safety requires appropriately understanding the emotional state of workers and dynamically adjusting the work environment accordingly. However, conventional systems have made it difficult to make specific adjustments based on emotional state, and this problem has led to work inefficiency and increased stress.

[0181] 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.

[0182] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs information analysis using a neural network, a control mechanism that recognizes and judges the emotional state and adjusts the operation of the machine based on the information analyzed by the information processing device, and an emotion recognition means that collects voice and facial expression data and estimates the emotional state. This makes it possible to optimize the work environment in real time based on the emotions of the workers.

[0183] An "integrated circuit with communication functions" is an electronic circuit that has the function of sending and receiving data to and from an external network.

[0184] "Common connectivity technology" refers to standard protocols and interfaces used for different devices and equipment to communicate with each other.

[0185] An "information processing device that performs information analysis using a neural network" is a device that uses a neural network, a form of artificial intelligence, to analyze input data and extract patterns and information from it.

[0186] A "control mechanism that recognizes emotional states, makes judgments, and adjusts the operation of mechanical devices" is a system that makes necessary judgments based on analyzed emotional data and adjusts the operation of robots and machines accordingly.

[0187] "An emotion recognition means for collecting voice and facial expression data and estimating emotional state" refers to a method or apparatus for estimating an individual's emotional state by analyzing data obtained from human voice and facial expressions.

[0188] The system designed to realize this application provides an automatically adjustable work environment in a factory based on the recognition of workers' emotions. The server uses an integrated circuit with communication capabilities and is equipped with emotion recognition means that recognizes emotions from the workers' voices and facial expressions. This data is analyzed by an information processing device using a neural network, and based on the analysis results, a control mechanism adjusts the operation of the factory's machinery.

[0189] The server uses smart glasses and microphones for voice data acquisition as hardware. Voice and facial expression data collected through these devices is processed by emotion recognition means. This information processing is carried out in stages such as processing the collected data, extracting features, and estimating emotional states. On the software side, deep learning frameworks such as TensorFlow and PyTorch are used, and emotions are judged using neural networks.

[0190] As a concrete example, when a factory worker feels pressure on the production line, an emotion recognition system detects the stressed state. Based on this information, the server sends instructions to the robot via the control mechanism to reduce the workload, thereby alleviating the worker's stress.

[0191] An example of a prompt for a generative AI model is: "Write a Python program that, when given user voice and facial expression data, recognizes their emotions, generates appropriate instructions, and sends them to a factory robot." This prompt is used to support systems aimed at improving work efficiency based on emotions.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server acquires audio and facial expression data in real time from smart glasses and microphones used within the factory. This data includes the user's voice tone and facial features. The input is audio waveform data and image data, and the output is pre-processed data necessary for emotion recognition. Data processing such as noise reduction and standardization is performed.

[0195] Step 2:

[0196] The device inputs pre-processed data into an emotion recognition system, specifically a neural network model. Calculations are performed to estimate the user's emotional state (e.g., stress, relief, joy). The input consists of pre-processed audio and facial expression data, and the output is the estimated emotional state. Data analysis for feature extraction and classification is performed using the neural network framework.

[0197] Step 3:

[0198] The server receives information about the emotional state obtained from the emotion recognition system and passes this information to the control mechanism. The control mechanism generates instructions to optimize the operation of the factory machinery based on the obtained emotional state. The input is the estimated emotional state, and the output is a specific machine operation instruction. An action decision algorithm is operated according to the emotional state.

[0199] Step 4:

[0200] The user experiences the machine's operation, which is regulated by the control mechanism. For example, if the server detects that the user is experiencing stress, it instructs the robot to slow down its work pace. This dynamically adjusts the work environment and reduces the user's stress.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] One embodiment of the present invention is a system that performs efficient data analysis and automatic control by linking an integrated circuit with communication capabilities with a processing unit using a neural network. This system is mounted on an electronic device, collects information in real time, and establishes optimal network connectivity via the integrated circuit. The processing unit analyzes the collected data using a neural network model and generates control signals based on the results to optimally control the device.

[0218] As a concrete example, let's consider an implementation in a smart home environment. A smart sensor, acting as a terminal, collects environmental data such as temperature, humidity, and light intensity, and connects to the home network via an integrated circuit. The terminal sends this information to a processing unit in real time, and through neural network analysis, automatically calculates the optimal air conditioning settings to maintain a comfortable indoor environment. The control signals generated by the terminal automatically adjust the air conditioner, optimizing energy efficiency.

[0219] Furthermore, the present invention is also effective in wearable devices for health monitoring. The device worn by the user measures heart rate and activity levels, and transmits the data to a cloud server in real time via an integrated circuit. The server in the cloud analyzes the data at high speed and sends activity recommendations and abnormality detection results to the terminal. As a result, the user can understand their health status in real time and lead a safe and meaningful life.

[0220] Thus, the system of the present invention efficiently integrates communication and data analysis, providing effective solutions in various application fields.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] When the device is powered on, its built-in integrated circuit starts working, selecting the optimal communication protocol and connecting to the network. This enables real-time communication for the device.

[0224] Step 2:

[0225] The terminal collects necessary data through connected sensors. This data, for example, in the case of a temperature sensor, acquires indoor temperature data and supplies it to the system.

[0226] Step 3:

[0227] The terminal transmits the collected data to its own processing unit, which then uses a neural network to perform initial analysis. This data analysis helps to identify the cause of any abnormal temperature changes, for example.

[0228] Step 4:

[0229] The terminal generates a control signal based on the analyzed results. This signal is sent to the control system, which automatically adjusts devices such as air conditioners to their optimal settings.

[0230] Step 5:

[0231] Analysis results and device status are sent to a server via the network for further analysis. The server performs more advanced data integration and analysis in the cloud and stores the results.

[0232] Step 6:

[0233] Based on the analysis results, the server generates new analysis results and recommendations and sends them to the terminal. This updates the device's control and user notifications.

[0234] Step 7:

[0235] Users receive notifications and feedback from their devices, adjust their settings, and check their health data. This supports a comfortable and healthy lifestyle.

[0236] (Example 1)

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

[0238] Conventional data analysis systems struggle with real-time information processing and automatic equipment optimization, lacking sufficient reliability and adaptability in situations requiring efficient and rapid control. In particular, achieving optimal control adapted to fluctuating environmental conditions is a challenge.

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

[0240] In this invention, the server includes means for collecting environmental information using electronic circuits with communication functions, a computing device for analyzing the information using a machine learning model, and a decision system for generating optimal control signals. This enables real-time information analysis and automatic control of equipment.

[0241] An "electronic circuit with communication capabilities" is a technological element that has a circuit configuration capable of accurately and quickly collecting environmental information and transmitting it via a network.

[0242] A "machine learning model" is a model that incorporates algorithms capable of pattern recognition and prediction by analyzing large amounts of data.

[0243] A "processing unit" is a hardware or software configuration designed for data analysis that can efficiently manage processing load.

[0244] A "control signal" is a signal generated based on analyzed data that contains instructions for automatically adjusting equipment or systems to a desired operating state.

[0245] A "decision-making system" is a system that has logic and algorithms for evaluating analysis results and generating the optimal control signal.

[0246] "Means for transmitting signals to automatically optimize equipment control" refers to technical means that have the function of transmitting instructions so that the equipment reaches an optimal operating state based on predetermined conditions.

[0247] This invention is a system that efficiently analyzes environmental information and automatically controls equipment by linking an electronic circuit with communication capabilities with a machine learning model. Specific embodiments are described below.

[0248] The device has an electronic circuit that includes sensors for collecting environmental data such as temperature, humidity, and light intensity. This electronic circuit is equipped with communication means for efficiently transmitting the collected data to a processing unit, and it is possible to upload the data to a cloud environment using Wi-Fi or other communication protocols.

[0249] The server is a computing device configured in the cloud that uses machine learning models to analyze data in real time. This process utilizes a neural network model to derive optimal control settings for air conditioners and other equipment based on the obtained data. Through this program's processing, the equipment automatically adjusts to a temperature and state suitable for user comfort, resulting in efficient energy use.

[0250] Users can receive feedback from the system through their devices. For example, they can instantly see notifications such as whether the room is being kept at the ideal temperature.

[0251] Examples of specific prompt messages include the following:

[0252] "Please create a program to optimize the management of air conditioning in a smart home."

[0253] "We are looking for an algorithm that analyzes data and automatically sets up a comfortable indoor environment."

[0254] Thus, specific embodiments of the invention provide real-time comfort and efficiency through the collection and analysis of environmental information and automatic equipment control based on the results.

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

[0256] Step 1:

[0257] The device collects environmental data. For example, a temperature sensor reads the current room temperature, and a humidity sensor measures the humidity of the air. This collected data is temporarily stored in the device's memory. The input is the analog signal obtained from the sensor, and the output is the environmental data converted into a digital signal.

[0258] Step 2:

[0259] The device collects environmental data and transmits it to the cloud via its communication function. This data transfer uses Wi-Fi or other network protocols. The input is the digital environmental data stored on the device, and the output is the data transmitted to the server via the network.

[0260] Step 3:

[0261] The server receives data in the cloud and analyzes it using a machine learning model. In this analysis step, a neural network is used to estimate the optimal settings for the indoor environment. The input is environmental data received via the network, and the output is information on the optimal control settings.

[0262] Step 4:

[0263] The server generates control signals based on the analysis results. These control signals include instructions for the operation of specific equipment. The input is the analyzed optimal setting information, and the output is the control signal. For example, a specific instruction such as "set the air conditioner to 25 degrees" is generated.

[0264] Step 5:

[0265] The server generates a control signal and sends it to the terminal. The input is the control signal generated by the server, and the output is the signal transmitted via communication to the terminal. This operation starts the configured operation of the environmental equipment.

[0266] Step 6:

[0267] The terminal controls the equipment based on the control signals it receives. For example, the temperature setting of an air conditioner is changed based on the received signal. The input is the control signal received from the server, and the output is the operating state of the adjusted equipment. The equipment is automatically optimized to maintain user comfort.

[0268] (Application Example 1)

[0269] 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."

[0270] Inventory management within factories often relies heavily on physical checks and manual data entry, leading to inefficiencies and human error. Furthermore, insufficient optimization of inventory placement and replenishment processes poses a challenge to improving productivity.

[0271] 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.

[0272] In this invention, the server includes an electronic circuit means having a communication function, a computing device means connected to the electronic circuit means using common connection technology and performing information analysis using artificial intelligence, a management device means that automatically makes decisions and generates control signals based on the information analyzed by the computing device means, a sensing means for reading identification information of items, and a processing means for generating a plan to optimize the placement of items based on the identification information collected by the sensing means. This makes it possible to automate inventory management within a factory and achieve efficient placement and replenishment.

[0273] "Electronic circuit means having communication functions" refers to an electronic circuit structure equipped with communication capabilities for exchanging information with an external system.

[0274] "Common connectivity technology" refers to standardized technologies that enable interconnection between different devices and systems.

[0275] A "computational device that performs information analysis using artificial intelligence" is a computing device that uses artificial intelligence to analyze data and make decisions for a specific purpose.

[0276] A "management device that automatically makes decisions and generates control signals" is a management system that automatically creates signals to control machines and systems based on analysis results.

[0277] "Sensing means for reading identification information of an item" refers to a sensor or reader for collecting identification information associated with a product or item.

[0278] "Processing means for generating a plan to optimize the arrangement of items" refers to a device that has the data processing capability to replan the efficient arrangement and movement of items using collected information.

[0279] In a system that implements an application example of this invention, multiple hardware and software components work together. First, the server is connected to the factory network using electronic circuitry with communication capabilities. This electronic circuitry supports wireless communication protocols such as Wi-Fi and 5G, and transmits and receives information in real time. IoT protocols (e.g., MQTT and HTTP) are used as common connectivity technologies.

[0280] The computing device utilizes software libraries such as TensorFlow and PyTorch in a cloud computing environment like Google Cloud Platform or AWS to perform information analysis using artificial intelligence. The information to be analyzed is the data of barcodes or RFID tags attached to the items in the factory, and these are collected by the sensing means mounted on the robots.

[0281] Based on the analyzed data, the management device generates control signals and sends instructions regarding the optimal placement and replenishment of inventory to the robots. Through this automated process, the user can efficiently manage the inventory in the factory. The user can use their smartphone to check the information in real-time through a dedicated app and issue replenishment instructions remotely when necessary.

[0282] As a specific example, in a certain factory, the introduction of this system shortened the time for inventory checking work to half of the previous time. As a result, the work efficiency was greatly improved and work errors were also reduced.

[0283] As an example of a prompt sentence, "Please explain a robot control system that utilizes a neural network as a means to improve the efficiency of inventory management in a factory." can be cited. This prompt is used when leveraging a generative AI model to provide insights on the usage status and effective operation methods of the system.

[0284] The flow of a specific process in Application Example 1 will be described using Figure 12.

[0285] Step 1:

[0286] The terminal reads the barcodes or RFID tags attached to the items in the factory.

[0287] The input is the identification information associated with each item, and the output is a dataset containing this identification information.

[0288] The device uses its built-in sensors to acquire information about items and collects this data in real time.

[0289] Step 2:

[0290] The terminal transmits the identification information it collects in real time to the server via electronic circuitry.

[0291] The input is the dataset obtained in Step 1, and the output is a database entry used for information analysis on the cloud.

[0292] The device utilizes Wi-Fi and 5G network protocols to transmit data to the cloud server at high speed.

[0293] Step 3:

[0294] The server uses artificial intelligence to analyze the transmitted identification information.

[0295] The input consists of database entries stored on the server, and the output is the analysis results regarding optimal inventory placement and necessary replenishment operations.

[0296] The server uses TensorFlow or PyTorch to run neural network models and extract efficient inventory management methods.

[0297] Step 4:

[0298] The server generates control signals based on the analysis results and sends instructions to the robot.

[0299] The input is the analysis results obtained in step 3, and the output is the specific work procedure that the robot will perform.

[0300] The server uses a control device to generate control signals and automates inventory management through robots.

[0301] Step 5:

[0302] The user monitors the inventory status and the operations of the robots through the smartphone application.

[0303] The input is the latest inventory information obtained from the server, and the output is the display on the dashboard accessible to the user.

[0304] The user can check the inventory status from home or outside using the application and directly give instructions to the system as needed.

[0305] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0306] As a form for implementing the present invention, a system that combines an integrated circuit having a communication function and a processing device using a neural network with an emotion engine for recognizing the user's emotions can be mentioned. This system is mounted on an electronic device and recognizes emotions in real time using the user's voice and facial expression data. The recognized emotions are analyzed by the processing device, and an optimal operation method is determined based on the results.

[0307] As a specific example, an embodiment in a smart home environment will be described. A smart speaker, which is a terminal, collects emotions together with the user's voice commands. The emotion engine analyzes the user's emotional state from the voice tone, tempo, and word selection. For example, when the emotion engine determines that the user is tired, the terminal adjusts the room lighting to a comfortable brightness and plays music to support a relaxed state.

[0308] Furthermore, this system is also effective in a smart system in a conference room. The terminal captures the expressions of the participants through a camera, and the emotion engine analyzes the atmosphere of the meeting. Based on the analysis results, the server dynamically adjusts the content and presentation method of the materials, contributing to the improvement of the meeting efficiency.

[0309] Thus, the system of the present invention grasps the user's emotional state in real time and performs operations appropriate to it, thereby providing a high level of user experience tailored to individual needs.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] When the terminal is powered on, it connects to the network via an integrated circuit with communication capabilities and begins collecting data from various sensors and microphones.

[0313] Step 2:

[0314] The device acquires voice commands from the user and surrounding environment data, and sends this to the emotion engine. At this stage, the voice data is broken down into parameters such as volume, tone, and pitch.

[0315] Step 3:

[0316] The emotion engine analyzes the received audio data to determine the user's emotions. For example, if the user's voice is calm and spoken at a slow pace, it is recognized as a relaxed emotional state.

[0317] Step 4:

[0318] The terminal sends the results of the emotion engine's analysis to the processing unit, where further data analysis is performed using a neural network. Based on this analysis, specific operation patterns are created.

[0319] Step 5:

[0320] The terminal generates control signals to control the device according to the created operation pattern. For example, if it determines that the user is tired, it generates a control signal to change the lighting to a warmer color.

[0321] Step 6:

[0322] The terminal sends control signals to the device, and the results are reflected in the user's environment. This allows the device to start its intended operation and provide the user with a comfortable environment.

[0323] Step 7:

[0324] The collected sentiment data is sent back to the server for long-term user sentiment analysis. The server uses this information to build a feedback loop that improves the overall system performance.

[0325] (Example 2)

[0326] 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".

[0327] In modern society, there is a growing need for interactive systems that adjust the environment based on the emotions of individual users. However, conventional systems have struggled to accurately recognize users' emotional states in real time and dynamically optimize the environment. Therefore, providing a high-quality user experience tailored to individual needs has been difficult.

[0328] 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.

[0329] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs data analysis using a neural network, a control system that automatically makes decisions and generates control signals based on the individual's emotional state data analyzed by the information processing device, means for collecting the user's voice data and facial expression data in real time and recognizing emotions, means for analyzing voice tone, tempo, and word choice using an emotion engine and determining the emotional state, and means for dynamically adjusting the environment based on the emotions determined by the terminal. This makes it possible to automatically optimize the environment in response to the user's emotions and provide a customized user experience tailored to the individual.

[0330] "Communication function" refers to the ability of a device to send and receive information with other devices or networks.

[0331] An "integrated circuit" refers to an electronic component that integrates a large number of semiconductor elements at high density to form a circuit that realizes a specific function.

[0332] "Common connectivity technology" refers to standardized connection methods for exchanging data and signals between different devices and systems.

[0333] A "neural network" refers to an algorithm or system for processing information that is modeled after the neural circuits of the human brain.

[0334] An "information processing device" refers to a device that receives input data, performs necessary calculations and analyses, and outputs useful results.

[0335] "Individual emotional state data" refers to a collection of data that shows an individual's emotions at a specific time.

[0336] A "control signal" refers to a signal generated within a system to instruct a specific action.

[0337] A "control system" refers to the entire system that generates signals to instruct actions based on the results of emotion analysis.

[0338] "Audio data" refers to digital or analog recordings of human voices that have been recorded or transmitted.

[0339] "Facial expression data" refers to data that contains information about an individual's facial expressions.

[0340] An "emotion engine" refers to an algorithm or process that analyzes input voice data and facial expression data to determine the user's emotional state.

[0341] "Voice tone" refers to the pitch, volume, and other sonic characteristics of a voice.

[0342] "Tempo" refers to the speed or rhythm of speech or music.

[0343] "Means for dynamically adjusting the environment" refers to a method or device for a system to automatically change environmental conditions in response to the user's emotional state.

[0344] "Real-time" refers to information processing that occurs instantly with little to no time delay.

[0345] As an embodiment of the present invention, a system is provided that combines an integrated circuit with communication capabilities, an information processing device using a neural network, and an emotion engine. This system is mounted on an electronic device, and a server collects and analyzes the user's voice data and facial expression data in real time to recognize the user's emotions. Specifically, the server inputs the voice data and facial expression data into the emotion engine, which analyzes the voice tone, tempo, and word choice.

[0346] The device receives the results of an emotion engine analysis based on the user's voice and determines the specific emotional state. This allows it to generate control signals necessary to dynamically optimize the environment according to the user's emotions, automatically adjusting lighting and sound environments. For example, if the user says, "I want to relax today," the device will soften the room lighting and play relaxing music.

[0347] Furthermore, for example, within a meeting room, a terminal can monitor participants' facial expressions, and an emotion engine can analyze the atmosphere of the meeting. The server immediately receives this analysis result and improves the efficiency of the meeting by changing how materials are presented, etc. A concrete example of a prompt message would be, "Set the optimal lighting and music to reduce user stress," which would quickly guide the system to the optimal environment settings.

[0348] Thus, the present invention is an excellent interactive system that can understand the user's emotional state in real time and dynamically create an environment that meets individual needs. This technology makes it possible to provide a higher level of user experience.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The user issues a voice command, which is input via the device's microphone. The device collects this voice data in real time and saves it as an audio file. This file serves as the baseline data for emotion recognition.

[0352] Step 2:

[0353] After the user's voice data is input into the device, the device sends that voice data to the emotion engine. The emotion engine analyzes the voice tone, tempo, word choice, etc. This analysis process determines the user's emotional state (for example, whether they are relaxed or stressed).

[0354] Step 3:

[0355] The server automatically generates control signals to determine the optimal environment settings based on the results analyzed by the emotion engine. In this process, a generating AI model creates action scenarios using prompt statements, and then makes specific lighting settings and music selections based on these prompt statements. For example, if a prompt statement such as "the user wants to relax" is used, the environment settings are dynamically optimized.

[0356] Step 4:

[0357] The device uses control signals received from the server to perform actual actions. For example, the device might adjust the brightness and color temperature of the lighting, or play music that helps the user relax. These actions are performed in a way that best suits the user's current emotional state, improving the user experience.

[0358] Step 5:

[0359] Users can provide feedback on the environmental adjustments made by their device. The server collects this feedback and uses it to improve the generated AI model, enabling more appropriate adjustments in subsequent operations.

[0360] (Application Example 2)

[0361] 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 as the "terminal".

[0362] In factories, improving work efficiency and safety requires appropriately understanding the emotional state of workers and dynamically adjusting the work environment accordingly. However, conventional systems have made it difficult to make specific adjustments based on emotional state, and this problem has led to work inefficiency and increased stress.

[0363] 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.

[0364] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs information analysis using a neural network, a control mechanism that recognizes and judges the emotional state and adjusts the operation of the machine based on the information analyzed by the information processing device, and an emotion recognition means that collects voice and facial expression data and estimates the emotional state. This makes it possible to optimize the work environment in real time based on the emotions of the workers.

[0365] An "integrated circuit with communication functions" is an electronic circuit that has the function of sending and receiving data to and from an external network.

[0366] "Common connectivity technology" refers to standard protocols and interfaces used for different devices and equipment to communicate with each other.

[0367] An "information processing device that performs information analysis using a neural network" is a device that uses a neural network, a form of artificial intelligence, to analyze input data and extract patterns and information from it.

[0368] A "control mechanism that recognizes emotional states, makes judgments, and adjusts the operation of mechanical devices" is a system that makes necessary judgments based on analyzed emotional data and adjusts the operation of robots and machines accordingly.

[0369] "An emotion recognition means for collecting voice and facial expression data and estimating emotional state" refers to a method or apparatus for estimating an individual's emotional state by analyzing data obtained from human voice and facial expressions.

[0370] The system designed to realize this application provides an automatically adjustable work environment in a factory based on the recognition of workers' emotions. The server uses an integrated circuit with communication capabilities and is equipped with emotion recognition means that recognizes emotions from the workers' voices and facial expressions. This data is analyzed by an information processing device using a neural network, and based on the analysis results, a control mechanism adjusts the operation of the factory's machinery.

[0371] The server uses smart glasses and microphones for voice data acquisition as hardware. Voice and facial expression data collected through these devices is processed by emotion recognition means. This information processing is carried out in stages such as processing the collected data, extracting features, and estimating emotional states. On the software side, deep learning frameworks such as TensorFlow and PyTorch are used, and emotions are judged using neural networks.

[0372] As a concrete example, when a factory worker feels pressure on the production line, an emotion recognition system detects the stressed state. Based on this information, the server sends instructions to the robot via the control mechanism to reduce the workload, thereby alleviating the worker's stress.

[0373] An example of a prompt for a generative AI model is: "Write a Python program that, when given user voice and facial expression data, recognizes their emotions, generates appropriate instructions, and sends them to a factory robot." This prompt is used to support systems aimed at improving work efficiency based on emotions.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server acquires audio and facial expression data in real time from smart glasses and microphones used within the factory. This data includes the user's voice tone and facial features. The input is audio waveform data and image data, and the output is pre-processed data necessary for emotion recognition. Data processing such as noise reduction and standardization is performed.

[0377] Step 2:

[0378] The device inputs pre-processed data into an emotion recognition system, specifically a neural network model. Calculations are performed to estimate the user's emotional state (e.g., stress, relief, joy). The input consists of pre-processed audio and facial expression data, and the output is the estimated emotional state. Data analysis for feature extraction and classification is performed using the neural network framework.

[0379] Step 3:

[0380] The server receives information about the emotional state obtained from the emotion recognition system and passes this information to the control mechanism. The control mechanism generates instructions to optimize the operation of the factory machinery based on the obtained emotional state. The input is the estimated emotional state, and the output is a specific machine operation instruction. An action decision algorithm is operated according to the emotional state.

[0381] Step 4:

[0382] The user experiences the machine's operation, which is regulated by the control mechanism. For example, if the server detects that the user is experiencing stress, it instructs the robot to slow down its work pace. This dynamically adjusts the work environment and reduces the user's stress.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] One embodiment of the present invention is a system that performs efficient data analysis and automatic control by linking an integrated circuit with communication capabilities with a processing unit using a neural network. This system is mounted on an electronic device, collects information in real time, and establishes optimal network connectivity via the integrated circuit. The processing unit analyzes the collected data using a neural network model and generates control signals based on the results to optimally control the device.

[0400] As a concrete example, let's consider an implementation in a smart home environment. A smart sensor, acting as a terminal, collects environmental data such as temperature, humidity, and light intensity, and connects to the home network via an integrated circuit. The terminal sends this information to a processing unit in real time, and through neural network analysis, automatically calculates the optimal air conditioning settings to maintain a comfortable indoor environment. The control signals generated by the terminal automatically adjust the air conditioner, optimizing energy efficiency.

[0401] Furthermore, the present invention is also effective in wearable devices for health monitoring. The device worn by the user measures heart rate and activity levels, and transmits the data to a cloud server in real time via an integrated circuit. The server in the cloud analyzes the data at high speed and sends activity recommendations and abnormality detection results to the terminal. As a result, the user can understand their health status in real time and lead a safe and meaningful life.

[0402] Thus, the system of the present invention efficiently integrates communication and data analysis, providing effective solutions in various application fields.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] When the device is powered on, its built-in integrated circuit starts working, selecting the optimal communication protocol and connecting to the network. This enables real-time communication for the device.

[0406] Step 2:

[0407] The terminal collects necessary data through connected sensors. This data, for example, in the case of a temperature sensor, acquires indoor temperature data and supplies it to the system.

[0408] Step 3:

[0409] The terminal transmits the collected data to its own processing unit, which then uses a neural network to perform initial analysis. This data analysis helps to identify the cause of any abnormal temperature changes, for example.

[0410] Step 4:

[0411] The terminal generates a control signal based on the analyzed results. This signal is sent to the control system, which automatically adjusts devices such as air conditioners to their optimal settings.

[0412] Step 5:

[0413] Analysis results and device status are sent to a server via the network for further analysis. The server performs more advanced data integration and analysis in the cloud and stores the results.

[0414] Step 6:

[0415] Based on the analysis results, the server generates new analysis results and recommendations and sends them to the terminal. This updates the device's control and user notifications.

[0416] Step 7:

[0417] Users receive notifications and feedback from their devices, adjust their settings, and check their health data. This supports a comfortable and healthy lifestyle.

[0418] (Example 1)

[0419] 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."

[0420] Conventional data analysis systems struggle with real-time information processing and automatic equipment optimization, lacking sufficient reliability and adaptability in situations requiring efficient and rapid control. In particular, achieving optimal control adapted to fluctuating environmental conditions is a challenge.

[0421] 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.

[0422] In this invention, the server includes means for collecting environmental information using electronic circuits with communication functions, a computing device for analyzing the information using a machine learning model, and a decision system for generating optimal control signals. This enables real-time information analysis and automatic control of equipment.

[0423] An "electronic circuit with communication capabilities" is a technological element that has a circuit configuration capable of accurately and quickly collecting environmental information and transmitting it via a network.

[0424] A "machine learning model" is a model that incorporates algorithms capable of pattern recognition and prediction by analyzing large amounts of data.

[0425] A "processing unit" is a hardware or software configuration designed for data analysis that can efficiently manage processing load.

[0426] A "control signal" is a signal generated based on analyzed data that contains instructions for automatically adjusting equipment or systems to a desired operating state.

[0427] A "decision-making system" is a system that has logic and algorithms for evaluating analysis results and generating the optimal control signal.

[0428] "Means for transmitting signals to automatically optimize equipment control" refers to technical means that have the function of transmitting instructions so that the equipment reaches an optimal operating state based on predetermined conditions.

[0429] This invention is a system that efficiently analyzes environmental information and automatically controls equipment by linking an electronic circuit with communication capabilities with a machine learning model. Specific embodiments are described below.

[0430] The device has an electronic circuit that includes sensors for collecting environmental data such as temperature, humidity, and light intensity. This electronic circuit is equipped with communication means for efficiently transmitting the collected data to a processing unit, and it is possible to upload the data to a cloud environment using Wi-Fi or other communication protocols.

[0431] The server is a computing device configured in the cloud that uses machine learning models to analyze data in real time. This process utilizes a neural network model to derive optimal control settings for air conditioners and other equipment based on the obtained data. Through this program's processing, the equipment automatically adjusts to a temperature and state suitable for user comfort, resulting in efficient energy use.

[0432] Users can receive feedback from the system through their devices. For example, they can instantly see notifications such as whether the room is being kept at the ideal temperature.

[0433] Examples of specific prompt messages include the following:

[0434] "Please create a program to optimize the management of air conditioning in a smart home."

[0435] "We are looking for an algorithm that analyzes data and automatically sets up a comfortable indoor environment."

[0436] Thus, specific embodiments of the invention provide real-time comfort and efficiency through the collection and analysis of environmental information and automatic equipment control based on the results.

[0437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0438] Step 1:

[0439] The device collects environmental data. For example, a temperature sensor reads the current room temperature, and a humidity sensor measures the humidity of the air. This collected data is temporarily stored in the device's memory. The input is the analog signal obtained from the sensor, and the output is the environmental data converted into a digital signal.

[0440] Step 2:

[0441] The device collects environmental data and transmits it to the cloud via its communication function. This data transfer uses Wi-Fi or other network protocols. The input is the digital environmental data stored on the device, and the output is the data transmitted to the server via the network.

[0442] Step 3:

[0443] The server receives data in the cloud and analyzes it using a machine learning model. In this analysis step, a neural network is used to estimate the optimal settings for the indoor environment. The input is environmental data received via the network, and the output is information on the optimal control settings.

[0444] Step 4:

[0445] The server generates control signals based on the analysis results. These control signals include instructions for the operation of specific equipment. The input is the analyzed optimal setting information, and the output is the control signal. For example, a specific instruction such as "set the air conditioner to 25 degrees" is generated.

[0446] Step 5:

[0447] The server generates a control signal and sends it to the terminal. The input is the control signal generated by the server, and the output is the signal transmitted via communication to the terminal. This operation starts the configured operation of the environmental equipment.

[0448] Step 6:

[0449] The terminal controls the equipment based on the control signals it receives. For example, the temperature setting of an air conditioner is changed based on the received signal. The input is the control signal received from the server, and the output is the operating state of the adjusted equipment. The equipment is automatically optimized to maintain user comfort.

[0450] (Application Example 1)

[0451] 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."

[0452] Inventory management within factories often relies heavily on physical checks and manual data entry, leading to inefficiencies and human error. Furthermore, insufficient optimization of inventory placement and replenishment processes poses a challenge to improving productivity.

[0453] 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.

[0454] In this invention, the server includes an electronic circuit means having a communication function, a computing device means connected to the electronic circuit means using common connection technology and performing information analysis using artificial intelligence, a management device means that automatically makes decisions and generates control signals based on the information analyzed by the computing device means, a sensing means for reading identification information of items, and a processing means for generating a plan to optimize the placement of items based on the identification information collected by the sensing means. This makes it possible to automate inventory management within a factory and achieve efficient placement and replenishment.

[0455] "Electronic circuit means having communication functions" refers to an electronic circuit structure equipped with communication capabilities for exchanging information with an external system.

[0456] "Common connectivity technology" refers to standardized technologies that enable interconnection between different devices and systems.

[0457] A "computational device that performs information analysis using artificial intelligence" is a computing device that uses artificial intelligence to analyze data and make decisions for a specific purpose.

[0458] A "management device that automatically makes decisions and generates control signals" is a management system that automatically creates signals to control machines and systems based on analysis results.

[0459] "Sensing means for reading identification information of an item" refers to a sensor or reader for collecting identification information associated with a product or item.

[0460] "Processing means for generating a plan to optimize the arrangement of items" refers to a device that has the data processing capability to replan the efficient arrangement and movement of items using collected information.

[0461] In a system that implements an application example of this invention, multiple hardware and software components work together. First, the server is connected to the factory network using electronic circuitry with communication capabilities. This electronic circuitry supports wireless communication protocols such as Wi-Fi and 5G, and transmits and receives information in real time. IoT protocols (e.g., MQTT and HTTP) are used as common connectivity technologies.

[0462] The computing unit utilizes software libraries such as TensorFlow and PyTorch in cloud computing environments such as Google Cloud Platform and AWS to perform information analysis using artificial intelligence. The information to be analyzed is data from barcodes and RFID tags attached to items in the factory, which are collected by sensing devices mounted on robots.

[0463] Based on the analyzed data, the control device generates control signals and sends instructions to the robot regarding optimal inventory placement and replenishment. This automated process allows users to efficiently manage inventory within the factory. Users can use their smartphones to check information in real time through a dedicated app and issue replenishment instructions remotely when needed.

[0464] As a concrete example, in one factory, the introduction of this system reduced the time required for inventory checks by half. This significantly improved work efficiency and reduced work errors.

[0465] An example of a prompt is, "Please describe a robot control system that utilizes neural networks as a means to streamline inventory management within a factory." This prompt is used when a generative AI model is employed to provide insights into system usage and effective operational methods.

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The terminal reads barcodes and RFID tags attached to items within the factory.

[0469] The input is the identification information associated with each item, and the output is a dataset containing this identification information.

[0470] The device uses its built-in sensors to acquire information about items and collects this data in real time.

[0471] Step 2:

[0472] The terminal transmits the identification information it collects in real time to the server via electronic circuitry.

[0473] The input is the dataset obtained in Step 1, and the output is a database entry used for information analysis on the cloud.

[0474] The device utilizes Wi-Fi and 5G network protocols to transmit data to the cloud server at high speed.

[0475] Step 3:

[0476] The server uses artificial intelligence to analyze the transmitted identification information.

[0477] The input consists of database entries stored on the server, and the output is the analysis results regarding optimal inventory placement and necessary replenishment operations.

[0478] The server uses TensorFlow or PyTorch to run neural network models and extract efficient inventory management methods.

[0479] Step 4:

[0480] The server generates control signals based on the analysis results and sends instructions to the robot.

[0481] The input is the analysis results obtained in step 3, and the output is the specific work procedure that the robot will perform.

[0482] The server uses a control device to generate control signals and automates inventory management through robots.

[0483] Step 5:

[0484] Users can monitor inventory status and robot operations through a smartphone app.

[0485] The input is the latest inventory information retrieved from the server, and the output is a display of a dashboard accessible to the user.

[0486] Users can use the app to check inventory status from home or on the go and issue direct instructions to the system as needed.

[0487] 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.

[0488] One embodiment of the present invention is a system that combines an integrated circuit with communication capabilities, a processing unit using a neural network, and an emotion engine that recognizes the user's emotions. This system is mounted on an electronic device and recognizes emotions in real time using the user's voice and facial expression data. The recognized emotions are analyzed by the processing unit, and the optimal operating method is determined based on the results.

[0489] As a concrete example, let's describe an embodiment in a smart home environment. The smart speaker, acting as a terminal, collects emotions along with the user's voice commands. The emotion engine analyzes the user's emotional state from their voice tone, tempo, and word choices. For example, if the emotion engine determines that the user is tired, the terminal adjusts the room lighting to a comfortable brightness and plays music to support relaxation.

[0490] Furthermore, this system is also effective in smart systems within meeting rooms. The terminal captures participants' facial expressions through a camera, and an emotion engine analyzes the atmosphere of the meeting. Based on the analysis results, the server dynamically adjusts the content and presentation method of materials, contributing to the efficiency of the meeting.

[0491] Thus, the system of the present invention grasps the user's emotional state in real time and performs operations appropriate to it, thereby providing a high level of user experience tailored to individual needs.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] When the terminal is powered on, it connects to the network via an integrated circuit with communication capabilities and begins collecting data from various sensors and microphones.

[0495] Step 2:

[0496] The device acquires voice commands from the user and surrounding environment data, and sends this to the emotion engine. At this stage, the voice data is broken down into parameters such as volume, tone, and pitch.

[0497] Step 3:

[0498] The emotion engine analyzes the received audio data to determine the user's emotions. For example, if the user's voice is calm and spoken at a slow pace, it is recognized as a relaxed emotional state.

[0499] Step 4:

[0500] The terminal sends the results of the emotion engine's analysis to the processing unit, where further data analysis is performed using a neural network. Based on this analysis, specific operation patterns are created.

[0501] Step 5:

[0502] The terminal generates control signals to control the device according to the created operation pattern. For example, if it determines that the user is tired, it generates a control signal to change the lighting to a warmer color.

[0503] Step 6:

[0504] The terminal sends control signals to the device, and the results are reflected in the user's environment. This allows the device to start its intended operation and provide the user with a comfortable environment.

[0505] Step 7:

[0506] The collected sentiment data is sent back to the server for long-term user sentiment analysis. The server uses this information to build a feedback loop that improves the overall system performance.

[0507] (Example 2)

[0508] 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."

[0509] In modern society, there is a growing need for interactive systems that adjust the environment based on the emotions of individual users. However, conventional systems have struggled to accurately recognize users' emotional states in real time and dynamically optimize the environment. Therefore, providing a high-quality user experience tailored to individual needs has been difficult.

[0510] 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.

[0511] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs data analysis using a neural network, a control system that automatically makes decisions and generates control signals based on the individual's emotional state data analyzed by the information processing device, means for collecting the user's voice data and facial expression data in real time and recognizing emotions, means for analyzing voice tone, tempo, and word choice using an emotion engine and determining the emotional state, and means for dynamically adjusting the environment based on the emotions determined by the terminal. This makes it possible to automatically optimize the environment in response to the user's emotions and provide a customized user experience tailored to the individual.

[0512] "Communication function" refers to the ability of a device to send and receive information with other devices or networks.

[0513] An "integrated circuit" refers to an electronic component that integrates a large number of semiconductor elements at high density to form a circuit that realizes a specific function.

[0514] "Common connectivity technology" refers to standardized connection methods for exchanging data and signals between different devices and systems.

[0515] A "neural network" refers to an algorithm or system for processing information that is modeled after the neural circuits of the human brain.

[0516] An "information processing device" refers to a device that receives input data, performs necessary calculations and analyses, and outputs useful results.

[0517] "Individual emotional state data" refers to a collection of data that shows an individual's emotions at a specific time.

[0518] A "control signal" refers to a signal generated within a system to instruct a specific action.

[0519] A "control system" refers to the entire system that generates signals to instruct actions based on the results of emotion analysis.

[0520] "Audio data" refers to digital or analog recordings of human voices that have been recorded or transmitted.

[0521] "Facial expression data" refers to data that contains information about an individual's facial expressions.

[0522] An "emotion engine" refers to an algorithm or process that analyzes input voice data and facial expression data to determine the user's emotional state.

[0523] "Voice tone" refers to the pitch, volume, and other sonic characteristics of a voice.

[0524] "Tempo" refers to the speed or rhythm of speech or music.

[0525] "Means for dynamically adjusting the environment" refers to a method or device for a system to automatically change environmental conditions in response to the user's emotional state.

[0526] "Real-time" refers to information processing that occurs instantly with little to no time delay.

[0527] As an embodiment of the present invention, a system is provided that combines an integrated circuit with communication capabilities, an information processing device using a neural network, and an emotion engine. This system is mounted on an electronic device, and a server collects and analyzes the user's voice data and facial expression data in real time to recognize the user's emotions. Specifically, the server inputs the voice data and facial expression data into the emotion engine, which analyzes the voice tone, tempo, and word choice.

[0528] The device receives the results of an emotion engine analysis based on the user's voice and determines the specific emotional state. This allows it to generate control signals necessary to dynamically optimize the environment according to the user's emotions, automatically adjusting lighting and sound environments. For example, if the user says, "I want to relax today," the device will soften the room lighting and play relaxing music.

[0529] Furthermore, for example, within a meeting room, a terminal can monitor participants' facial expressions, and an emotion engine can analyze the atmosphere of the meeting. The server immediately receives this analysis result and improves the efficiency of the meeting by changing how materials are presented, etc. A concrete example of a prompt message would be, "Set the optimal lighting and music to reduce user stress," which would quickly guide the system to the optimal environment settings.

[0530] Thus, the present invention is an excellent interactive system that can understand the user's emotional state in real time and dynamically create an environment that meets individual needs. This technology makes it possible to provide a higher level of user experience.

[0531] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0532] Step 1:

[0533] The user issues a voice command, which is input via the device's microphone. The device collects this voice data in real time and saves it as an audio file. This file serves as the baseline data for emotion recognition.

[0534] Step 2:

[0535] After the user's voice data is input into the device, the device sends that voice data to the emotion engine. The emotion engine analyzes the voice tone, tempo, word choice, etc. This analysis process determines the user's emotional state (for example, whether they are relaxed or stressed).

[0536] Step 3:

[0537] The server automatically generates control signals to determine the optimal environment settings based on the results analyzed by the emotion engine. In this process, a generating AI model creates action scenarios using prompt statements, and then makes specific lighting settings and music selections based on these prompt statements. For example, if a prompt statement such as "the user wants to relax" is used, the environment settings are dynamically optimized.

[0538] Step 4:

[0539] The device uses control signals received from the server to perform actual actions. For example, the device might adjust the brightness and color temperature of the lighting, or play music that helps the user relax. These actions are performed in a way that best suits the user's current emotional state, improving the user experience.

[0540] Step 5:

[0541] Users can provide feedback on the environmental adjustments made by their device. The server collects this feedback and uses it to improve the generated AI model, enabling more appropriate adjustments in subsequent operations.

[0542] (Application Example 2)

[0543] 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."

[0544] In factories, improving work efficiency and safety requires appropriately understanding the emotional state of workers and dynamically adjusting the work environment accordingly. However, conventional systems have made it difficult to make specific adjustments based on emotional state, and this problem has led to work inefficiency and increased stress.

[0545] 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.

[0546] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs information analysis using a neural network, a control mechanism that recognizes and judges the emotional state and adjusts the operation of the machine based on the information analyzed by the information processing device, and an emotion recognition means that collects voice and facial expression data and estimates the emotional state. This makes it possible to optimize the work environment in real time based on the emotions of the workers.

[0547] An "integrated circuit with communication functions" is an electronic circuit that has the function of sending and receiving data to and from an external network.

[0548] "Common connectivity technology" refers to standard protocols and interfaces used for different devices and equipment to communicate with each other.

[0549] An "information processing device that performs information analysis using a neural network" is a device that uses a neural network, a form of artificial intelligence, to analyze input data and extract patterns and information from it.

[0550] A "control mechanism that recognizes emotional states, makes judgments, and adjusts the operation of mechanical devices" is a system that makes necessary judgments based on analyzed emotional data and adjusts the operation of robots and machines accordingly.

[0551] "An emotion recognition means for collecting voice and facial expression data and estimating emotional state" refers to a method or apparatus for estimating an individual's emotional state by analyzing data obtained from human voice and facial expressions.

[0552] The system designed to realize this application provides an automatically adjustable work environment in a factory based on the recognition of workers' emotions. The server uses an integrated circuit with communication capabilities and is equipped with emotion recognition means that recognizes emotions from the workers' voices and facial expressions. This data is analyzed by an information processing device using a neural network, and based on the analysis results, a control mechanism adjusts the operation of the factory's machinery.

[0553] The server uses smart glasses and microphones for voice data acquisition as hardware. Voice and facial expression data collected through these devices is processed by emotion recognition means. This information processing is carried out in stages such as processing the collected data, extracting features, and estimating emotional states. On the software side, deep learning frameworks such as TensorFlow and PyTorch are used, and emotions are judged using neural networks.

[0554] As a concrete example, when a factory worker feels pressure on the production line, an emotion recognition system detects the stressed state. Based on this information, the server sends instructions to the robot via the control mechanism to reduce the workload, thereby alleviating the worker's stress.

[0555] An example of a prompt for a generative AI model is: "Write a Python program that, when given user voice and facial expression data, recognizes their emotions, generates appropriate instructions, and sends them to a factory robot." This prompt is used to support systems aimed at improving work efficiency based on emotions.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server acquires audio and facial expression data in real time from smart glasses and microphones used within the factory. This data includes the user's voice tone and facial features. The input is audio waveform data and image data, and the output is pre-processed data necessary for emotion recognition. Data processing such as noise reduction and standardization is performed.

[0559] Step 2:

[0560] The device inputs pre-processed data into an emotion recognition system, specifically a neural network model. Calculations are performed to estimate the user's emotional state (e.g., stress, relief, joy). The input consists of pre-processed audio and facial expression data, and the output is the estimated emotional state. Data analysis for feature extraction and classification is performed using the neural network framework.

[0561] Step 3:

[0562] The server receives information about the emotional state obtained from the emotion recognition system and passes this information to the control mechanism. The control mechanism generates instructions to optimize the operation of the factory machinery based on the obtained emotional state. The input is the estimated emotional state, and the output is a specific machine operation instruction. An action decision algorithm is operated according to the emotional state.

[0563] Step 4:

[0564] The user experiences the machine's operation, which is regulated by the control mechanism. For example, if the server detects that the user is experiencing stress, it instructs the robot to slow down its work pace. This dynamically adjusts the work environment and reduces the user's stress.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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".

[0582] One embodiment of the present invention is a system that performs efficient data analysis and automatic control by linking an integrated circuit with communication capabilities with a processing unit using a neural network. This system is mounted on an electronic device, collects information in real time, and establishes optimal network connectivity via the integrated circuit. The processing unit analyzes the collected data using a neural network model and generates control signals based on the results to optimally control the device.

[0583] As a concrete example, let's consider an implementation in a smart home environment. A smart sensor, acting as a terminal, collects environmental data such as temperature, humidity, and light intensity, and connects to the home network via an integrated circuit. The terminal sends this information to a processing unit in real time, and through neural network analysis, automatically calculates the optimal air conditioning settings to maintain a comfortable indoor environment. The control signals generated by the terminal automatically adjust the air conditioner, optimizing energy efficiency.

[0584] Furthermore, the present invention is also effective in wearable devices for health monitoring. The device worn by the user measures heart rate and activity levels, and transmits the data to a cloud server in real time via an integrated circuit. The server in the cloud analyzes the data at high speed and sends activity recommendations and abnormality detection results to the terminal. As a result, the user can understand their health status in real time and lead a safe and meaningful life.

[0585] Thus, the system of the present invention efficiently integrates communication and data analysis, providing effective solutions in various application fields.

[0586] The following describes the processing flow.

[0587] Step 1:

[0588] When the device is powered on, its built-in integrated circuit starts working, selecting the optimal communication protocol and connecting to the network. This enables real-time communication for the device.

[0589] Step 2:

[0590] The terminal collects necessary data through connected sensors. This data, for example, in the case of a temperature sensor, acquires indoor temperature data and supplies it to the system.

[0591] Step 3:

[0592] The terminal transmits the collected data to its own processing unit, which then uses a neural network to perform initial analysis. This data analysis helps to identify the cause of any abnormal temperature changes, for example.

[0593] Step 4:

[0594] The terminal generates a control signal based on the analyzed results. This signal is sent to the control system, which automatically adjusts devices such as air conditioners to their optimal settings.

[0595] Step 5:

[0596] Analysis results and device status are sent to a server via the network for further analysis. The server performs more advanced data integration and analysis in the cloud and stores the results.

[0597] Step 6:

[0598] Based on the analysis results, the server generates new analysis results and recommendations and sends them to the terminal. This updates the device's control and user notifications.

[0599] Step 7:

[0600] Users receive notifications and feedback from their devices, adjust their settings, and check their health data. This supports a comfortable and healthy lifestyle.

[0601] (Example 1)

[0602] 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".

[0603] Conventional data analysis systems struggle with real-time information processing and automatic equipment optimization, lacking sufficient reliability and adaptability in situations requiring efficient and rapid control. In particular, achieving optimal control adapted to fluctuating environmental conditions is a challenge.

[0604] 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.

[0605] In this invention, the server includes means for collecting environmental information using electronic circuits with communication functions, a computing device for analyzing the information using a machine learning model, and a decision system for generating optimal control signals. This enables real-time information analysis and automatic control of equipment.

[0606] An "electronic circuit with communication capabilities" is a technological element that has a circuit configuration capable of accurately and quickly collecting environmental information and transmitting it via a network.

[0607] A "machine learning model" is a model that incorporates algorithms capable of pattern recognition and prediction by analyzing large amounts of data.

[0608] A "processing unit" is a hardware or software configuration designed for data analysis that can efficiently manage processing load.

[0609] A "control signal" is a signal generated based on analyzed data that contains instructions for automatically adjusting equipment or systems to a desired operating state.

[0610] A "decision-making system" is a system that has logic and algorithms for evaluating analysis results and generating the optimal control signal.

[0611] "Means for transmitting signals to automatically optimize equipment control" refers to technical means that have the function of transmitting instructions so that the equipment reaches an optimal operating state based on predetermined conditions.

[0612] This invention is a system that efficiently analyzes environmental information and automatically controls equipment by linking an electronic circuit with communication capabilities with a machine learning model. Specific embodiments are described below.

[0613] The device has an electronic circuit that includes sensors for collecting environmental data such as temperature, humidity, and light intensity. This electronic circuit is equipped with communication means for efficiently transmitting the collected data to a processing unit, and it is possible to upload the data to a cloud environment using Wi-Fi or other communication protocols.

[0614] The server is a computing device configured in the cloud that uses machine learning models to analyze data in real time. This process utilizes a neural network model to derive optimal control settings for air conditioners and other equipment based on the obtained data. Through this program's processing, the equipment automatically adjusts to a temperature and state suitable for user comfort, resulting in efficient energy use.

[0615] Users can receive feedback from the system through their devices. For example, they can instantly see notifications such as whether the room is being kept at the ideal temperature.

[0616] Examples of specific prompt messages include the following:

[0617] "Please create a program to optimize the management of air conditioning in a smart home."

[0618] "We are looking for an algorithm that analyzes data and automatically sets up a comfortable indoor environment."

[0619] Thus, specific embodiments of the invention provide real-time comfort and efficiency through the collection and analysis of environmental information and automatic equipment control based on the results.

[0620] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0621] Step 1:

[0622] The device collects environmental data. For example, a temperature sensor reads the current room temperature, and a humidity sensor measures the humidity of the air. This collected data is temporarily stored in the device's memory. The input is the analog signal obtained from the sensor, and the output is the environmental data converted into a digital signal.

[0623] Step 2:

[0624] The device collects environmental data and transmits it to the cloud via its communication function. This data transfer uses Wi-Fi or other network protocols. The input is the digital environmental data stored on the device, and the output is the data transmitted to the server via the network.

[0625] Step 3:

[0626] The server receives data in the cloud and analyzes it using a machine learning model. In this analysis step, a neural network is used to estimate the optimal settings for the indoor environment. The input is environmental data received via the network, and the output is information on the optimal control settings.

[0627] Step 4:

[0628] The server generates control signals based on the analysis results. These control signals include instructions for the operation of specific equipment. The input is the analyzed optimal setting information, and the output is the control signal. For example, a specific instruction such as "set the air conditioner to 25 degrees" is generated.

[0629] Step 5:

[0630] The server generates a control signal and sends it to the terminal. The input is the control signal generated by the server, and the output is the signal transmitted via communication to the terminal. This operation starts the configured operation of the environmental equipment.

[0631] Step 6:

[0632] The terminal controls the equipment based on the control signals it receives. For example, the temperature setting of an air conditioner is changed based on the received signal. The input is the control signal received from the server, and the output is the operating state of the adjusted equipment. The equipment is automatically optimized to maintain user comfort.

[0633] (Application Example 1)

[0634] 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".

[0635] Inventory management within factories often relies heavily on physical checks and manual data entry, leading to inefficiencies and human error. Furthermore, insufficient optimization of inventory placement and replenishment processes poses a challenge to improving productivity.

[0636] 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.

[0637] In this invention, the server includes an electronic circuit means having a communication function, a computing device means connected to the electronic circuit means using common connection technology and performing information analysis using artificial intelligence, a management device means that automatically makes decisions and generates control signals based on the information analyzed by the computing device means, a sensing means for reading identification information of items, and a processing means for generating a plan to optimize the placement of items based on the identification information collected by the sensing means. This makes it possible to automate inventory management within a factory and achieve efficient placement and replenishment.

[0638] "Electronic circuit means having communication functions" refers to an electronic circuit structure equipped with communication capabilities for exchanging information with an external system.

[0639] "Common connectivity technology" refers to standardized technologies that enable interconnection between different devices and systems.

[0640] A "computational device that performs information analysis using artificial intelligence" is a computing device that uses artificial intelligence to analyze data and make decisions for a specific purpose.

[0641] A "management device that automatically makes decisions and generates control signals" is a management system that automatically creates signals to control machines and systems based on analysis results.

[0642] "Sensing means for reading identification information of an item" refers to a sensor or reader for collecting identification information associated with a product or item.

[0643] "Processing means for generating a plan to optimize the arrangement of items" refers to a device that has the data processing capability to replan the efficient arrangement and movement of items using collected information.

[0644] In a system that implements an application example of this invention, multiple hardware and software components work together. First, the server is connected to the factory network using electronic circuitry with communication capabilities. This electronic circuitry supports wireless communication protocols such as Wi-Fi and 5G, and transmits and receives information in real time. IoT protocols (e.g., MQTT and HTTP) are used as common connectivity technologies.

[0645] The computing unit utilizes software libraries such as TensorFlow and PyTorch in cloud computing environments such as Google Cloud Platform and AWS to perform information analysis using artificial intelligence. The information to be analyzed is data from barcodes and RFID tags attached to items in the factory, which are collected by sensing devices mounted on robots.

[0646] Based on the analyzed data, the control device generates control signals and sends instructions to the robot regarding optimal inventory placement and replenishment. This automated process allows users to efficiently manage inventory within the factory. Users can use their smartphones to check information in real time through a dedicated app and issue replenishment instructions remotely when needed.

[0647] As a concrete example, in one factory, the introduction of this system reduced the time required for inventory checks by half. This significantly improved work efficiency and reduced work errors.

[0648] An example of a prompt is, "Please describe a robot control system that utilizes neural networks as a means to streamline inventory management within a factory." This prompt is used when a generative AI model is employed to provide insights into system usage and effective operational methods.

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The terminal reads barcodes and RFID tags attached to items within the factory.

[0652] The input is the identification information associated with each item, and the output is a dataset containing this identification information.

[0653] The device uses its built-in sensors to acquire information about items and collects this data in real time.

[0654] Step 2:

[0655] The terminal transmits the identification information it collects in real time to the server via electronic circuitry.

[0656] The input is the dataset obtained in Step 1, and the output is a database entry used for information analysis on the cloud.

[0657] The device utilizes Wi-Fi and 5G network protocols to transmit data to the cloud server at high speed.

[0658] Step 3:

[0659] The server uses artificial intelligence to analyze the transmitted identification information.

[0660] The input consists of database entries stored on the server, and the output is the analysis results regarding optimal inventory placement and necessary replenishment operations.

[0661] The server uses TensorFlow or PyTorch to run neural network models and extract efficient inventory management methods.

[0662] Step 4:

[0663] The server generates control signals based on the analysis results and sends instructions to the robot.

[0664] The input is the analysis results obtained in step 3, and the output is the specific work procedure that the robot will perform.

[0665] The server uses a control device to generate control signals and automates inventory management through robots.

[0666] Step 5:

[0667] Users can monitor inventory status and robot operations through a smartphone app.

[0668] The input is the latest inventory information retrieved from the server, and the output is a display of a dashboard accessible to the user.

[0669] Users can use the app to check inventory status from home or on the go and issue direct instructions to the system as needed.

[0670] 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.

[0671] One embodiment of the present invention is a system that combines an integrated circuit with communication capabilities, a processing unit using a neural network, and an emotion engine that recognizes the user's emotions. This system is mounted on an electronic device and recognizes emotions in real time using the user's voice and facial expression data. The recognized emotions are analyzed by the processing unit, and the optimal operating method is determined based on the results.

[0672] As a concrete example, let's describe an embodiment in a smart home environment. The smart speaker, acting as a terminal, collects emotions along with the user's voice commands. The emotion engine analyzes the user's emotional state from their voice tone, tempo, and word choices. For example, if the emotion engine determines that the user is tired, the terminal adjusts the room lighting to a comfortable brightness and plays music to support relaxation.

[0673] Furthermore, this system is also effective in smart systems within meeting rooms. The terminal captures participants' facial expressions through a camera, and an emotion engine analyzes the atmosphere of the meeting. Based on the analysis results, the server dynamically adjusts the content and presentation method of materials, contributing to the efficiency of the meeting.

[0674] Thus, the system of the present invention grasps the user's emotional state in real time and performs operations appropriate to it, thereby providing a high level of user experience tailored to individual needs.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] When the terminal is powered on, it connects to the network via an integrated circuit with communication capabilities and begins collecting data from various sensors and microphones.

[0678] Step 2:

[0679] The device acquires voice commands from the user and surrounding environment data, and sends this to the emotion engine. At this stage, the voice data is broken down into parameters such as volume, tone, and pitch.

[0680] Step 3:

[0681] The emotion engine analyzes the received audio data to determine the user's emotions. For example, if the user's voice is calm and spoken at a slow pace, it is recognized as a relaxed emotional state.

[0682] Step 4:

[0683] The terminal sends the results of the emotion engine's analysis to the processing unit, where further data analysis is performed using a neural network. Based on this analysis, specific operation patterns are created.

[0684] Step 5:

[0685] The terminal generates control signals to control the device according to the created operation pattern. For example, if it determines that the user is tired, it generates a control signal to change the lighting to a warmer color.

[0686] Step 6:

[0687] The terminal sends control signals to the device, and the results are reflected in the user's environment. This allows the device to start its intended operation and provide the user with a comfortable environment.

[0688] Step 7:

[0689] The collected sentiment data is sent back to the server for long-term user sentiment analysis. The server uses this information to build a feedback loop that improves the overall system performance.

[0690] (Example 2)

[0691] 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".

[0692] In modern society, there is a growing need for interactive systems that adjust the environment based on the emotions of individual users. However, conventional systems have struggled to accurately recognize users' emotional states in real time and dynamically optimize the environment. Therefore, providing a high-quality user experience tailored to individual needs has been difficult.

[0693] 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.

[0694] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs data analysis using a neural network, a control system that automatically makes decisions and generates control signals based on the individual's emotional state data analyzed by the information processing device, means for collecting the user's voice data and facial expression data in real time and recognizing emotions, means for analyzing voice tone, tempo, and word choice using an emotion engine and determining the emotional state, and means for dynamically adjusting the environment based on the emotions determined by the terminal. This makes it possible to automatically optimize the environment in response to the user's emotions and provide a customized user experience tailored to the individual.

[0695] "Communication function" refers to the ability of a device to send and receive information with other devices or networks.

[0696] An "integrated circuit" refers to an electronic component that integrates a large number of semiconductor elements at high density to form a circuit that realizes a specific function.

[0697] "Common connectivity technology" refers to standardized connection methods for exchanging data and signals between different devices and systems.

[0698] A "neural network" refers to an algorithm or system for processing information that is modeled after the neural circuits of the human brain.

[0699] An "information processing device" refers to a device that receives input data, performs necessary calculations and analyses, and outputs useful results.

[0700] "Individual emotional state data" refers to a collection of data that shows an individual's emotions at a specific time.

[0701] A "control signal" refers to a signal generated within a system to instruct a specific action.

[0702] A "control system" refers to the entire system that generates signals to instruct actions based on the results of emotion analysis.

[0703] "Audio data" refers to digital or analog recordings of human voices that have been recorded or transmitted.

[0704] "Facial expression data" refers to data that contains information about an individual's facial expressions.

[0705] An "emotion engine" refers to an algorithm or process that analyzes input voice data and facial expression data to determine the user's emotional state.

[0706] "Voice tone" refers to the pitch, volume, and other sonic characteristics of a voice.

[0707] "Tempo" refers to the speed or rhythm of speech or music.

[0708] "Means for dynamically adjusting the environment" refers to a method or device for a system to automatically change environmental conditions in response to the user's emotional state.

[0709] "Real-time" refers to information processing that occurs instantly with little to no time delay.

[0710] As an embodiment of the present invention, a system is provided that combines an integrated circuit with communication capabilities, an information processing device using a neural network, and an emotion engine. This system is mounted on an electronic device, and a server collects and analyzes the user's voice data and facial expression data in real time to recognize the user's emotions. Specifically, the server inputs the voice data and facial expression data into the emotion engine, which analyzes the voice tone, tempo, and word choice.

[0711] The device receives the results of an emotion engine analysis based on the user's voice and determines the specific emotional state. This allows it to generate control signals necessary to dynamically optimize the environment according to the user's emotions, automatically adjusting lighting and sound environments. For example, if the user says, "I want to relax today," the device will soften the room lighting and play relaxing music.

[0712] Furthermore, for example, within a meeting room, a terminal can monitor participants' facial expressions, and an emotion engine can analyze the atmosphere of the meeting. The server immediately receives this analysis result and improves the efficiency of the meeting by changing how materials are presented, etc. A concrete example of a prompt message would be, "Set the optimal lighting and music to reduce user stress," which would quickly guide the system to the optimal environment settings.

[0713] Thus, the present invention is an excellent interactive system that can understand the user's emotional state in real time and dynamically create an environment that meets individual needs. This technology makes it possible to provide a higher level of user experience.

[0714] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0715] Step 1:

[0716] The user issues a voice command, which is input via the device's microphone. The device collects this voice data in real time and saves it as an audio file. This file serves as the baseline data for emotion recognition.

[0717] Step 2:

[0718] After the user's voice data is input into the device, the device sends that voice data to the emotion engine. The emotion engine analyzes the voice tone, tempo, word choice, etc. This analysis process determines the user's emotional state (for example, whether they are relaxed or stressed).

[0719] Step 3:

[0720] The server automatically generates control signals to determine the optimal environment settings based on the results analyzed by the emotion engine. In this process, a generating AI model creates action scenarios using prompt statements, and then makes specific lighting settings and music selections based on these prompt statements. For example, if a prompt statement such as "the user wants to relax" is used, the environment settings are dynamically optimized.

[0721] Step 4:

[0722] The device uses control signals received from the server to perform actual actions. For example, the device might adjust the brightness and color temperature of the lighting, or play music that helps the user relax. These actions are performed in a way that best suits the user's current emotional state, improving the user experience.

[0723] Step 5:

[0724] Users can provide feedback on the environmental adjustments made by their device. The server collects this feedback and uses it to improve the generated AI model, enabling more appropriate adjustments in subsequent operations.

[0725] (Application Example 2)

[0726] 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".

[0727] In factories, improving work efficiency and safety requires appropriately understanding the emotional state of workers and dynamically adjusting the work environment accordingly. However, conventional systems have made it difficult to make specific adjustments based on emotional state, and this problem has led to work inefficiency and increased stress.

[0728] 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.

[0729] In this invention, the server includes an integrated circuit having a communication function, an information processing device connected to the integrated circuit using common connection technology that performs information analysis using a neural network, a control mechanism that recognizes and judges the emotional state and adjusts the operation of the machine based on the information analyzed by the information processing device, and an emotion recognition means that collects voice and facial expression data and estimates the emotional state. This makes it possible to optimize the work environment in real time based on the emotions of the workers.

[0730] An "integrated circuit with communication functions" is an electronic circuit that has the function of sending and receiving data to and from an external network.

[0731] "Common connectivity technology" refers to standard protocols and interfaces used for different devices and equipment to communicate with each other.

[0732] An "information processing device that performs information analysis using a neural network" is a device that uses a neural network, a form of artificial intelligence, to analyze input data and extract patterns and information from it.

[0733] A "control mechanism that recognizes emotional states, makes judgments, and adjusts the operation of mechanical devices" is a system that makes necessary judgments based on analyzed emotional data and adjusts the operation of robots and machines accordingly.

[0734] "An emotion recognition means for collecting voice and facial expression data and estimating emotional state" refers to a method or apparatus for estimating an individual's emotional state by analyzing data obtained from human voice and facial expressions.

[0735] The system designed to realize this application provides an automatically adjustable work environment in a factory based on the recognition of workers' emotions. The server uses an integrated circuit with communication capabilities and is equipped with emotion recognition means that recognizes emotions from the workers' voices and facial expressions. This data is analyzed by an information processing device using a neural network, and based on the analysis results, a control mechanism adjusts the operation of the factory's machinery.

[0736] The server uses smart glasses and microphones for voice data acquisition as hardware. Voice and facial expression data collected through these devices is processed by emotion recognition means. This information processing is carried out in stages such as processing the collected data, extracting features, and estimating emotional states. On the software side, deep learning frameworks such as TensorFlow and PyTorch are used, and emotions are judged using neural networks.

[0737] As a concrete example, when a factory worker feels pressure on the production line, an emotion recognition system detects the stressed state. Based on this information, the server sends instructions to the robot via the control mechanism to reduce the workload, thereby alleviating the worker's stress.

[0738] An example of a prompt for a generative AI model is: "Write a Python program that, when given user voice and facial expression data, recognizes their emotions, generates appropriate instructions, and sends them to a factory robot." This prompt is used to support systems aimed at improving work efficiency based on emotions.

[0739] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0740] Step 1:

[0741] The server acquires audio and facial expression data in real time from smart glasses and microphones used within the factory. This data includes the user's voice tone and facial features. The input is audio waveform data and image data, and the output is pre-processed data necessary for emotion recognition. Data processing such as noise reduction and standardization is performed.

[0742] Step 2:

[0743] The device inputs pre-processed data into an emotion recognition system, specifically a neural network model. Calculations are performed to estimate the user's emotional state (e.g., stress, relief, joy). The input consists of pre-processed audio and facial expression data, and the output is the estimated emotional state. Data analysis for feature extraction and classification is performed using the neural network framework.

[0744] Step 3:

[0745] The server receives information about the emotional state obtained from the emotion recognition system and passes this information to the control mechanism. The control mechanism generates instructions to optimize the operation of the factory machinery based on the obtained emotional state. The input is the estimated emotional state, and the output is a specific machine operation instruction. An action decision algorithm is operated according to the emotional state.

[0746] Step 4:

[0747] The user experiences the machine's operation, which is regulated by the control mechanism. For example, if the server detects that the user is experiencing stress, it instructs the robot to slow down its work pace. This dynamically adjusts the work environment and reduces the user's stress.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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."

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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 as being incorporated by reference.

[0769] The following is further disclosed regarding the embodiments described above.

[0770] (Claim 1)

[0771] An integrated circuit having communication functions,

[0772] A processing device that performs data analysis using a neural network, connected to the integrated circuit using common connection technology,

[0773] A control system that automatically makes decisions and generates control signals based on data analyzed by the aforementioned processing device,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The system according to claim 1, wherein the integrated circuit includes means for selecting an optimized protocol for the connected network.

[0777] (Claim 3)

[0778] The system according to claim 1, wherein the analyzed data is transmitted in real time to an external device and used for further analysis.

[0779] "Example 1"

[0780] (Claim 1)

[0781] A means for collecting environmental information using an electronic circuit with communication capabilities,

[0782] A computing device connected to the aforementioned electronic circuit analyzes information using a machine learning model,

[0783] A decision system that generates the optimal control signal based on the information analyzed by the aforementioned computing device,

[0784] A means for transmitting signals to automatically optimize the control of the equipment,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, wherein the electronic circuit has a function to select an optimized method for the connected communication network.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the analyzed information is transferred in real time to an external technology and used for further information processing.

[0790] "Application Example 1"

[0791] (Claim 1)

[0792] Electronic circuit means having a communication function,

[0793] A computing device means that performs information analysis using artificial intelligence, connected to the electronic circuit means using common connection technology,

[0794] A management device means that automatically makes decisions and generates control signals based on the information analyzed by the aforementioned computing device means,

[0795] A sensing means for reading the identification information of an item,

[0796] A processing means that generates a plan for optimizing the arrangement of items based on the identification information collected by the sensing means,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the electronic circuit means has a function to select an optimal communication protocol.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the analyzed information is transmitted in real time to an external system and used as a basis for further analysis.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] An integrated circuit having communication functions,

[0805] An information processing device that performs data analysis using a neural network, connected to the integrated circuit using common connection technology,

[0806] A control system that automatically makes decisions and generates control signals based on individual emotional state data analyzed by the aforementioned information processing device,

[0807] A means of collecting user voice data and facial expression data in real time and recognizing emotions,

[0808] A means of determining an emotional state by analyzing voice tone, tempo, and word choice using an emotion engine,

[0809] A means of dynamically adjusting the environment based on emotions determined by the device,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, wherein the integrated circuit includes means for selecting an optimized protocol for the connected data network.

[0813] (Claim 3)

[0814] The system according to claim 1, wherein the analyzed personal emotional data is transmitted in real time to an external device and used for further analysis.

[0815] "Application example 2 of combining emotional engines"

[0816] (Claim 1)

[0817] An integrated circuit having communication functions,

[0818] An information processing device that performs information analysis using a neural network, connected to the integrated circuit using common connection technology,

[0819] A control mechanism that recognizes and judges the emotional state based on the information analyzed by the aforementioned information processing device and adjusts the operation of the mechanical device,

[0820] An emotion recognition means for collecting voice and facial expression data and estimating the aforementioned emotional state,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the integrated circuit includes means for selecting an optimized protocol for the connected communication network.

[0824] (Claim 3)

[0825] The system according to claim 1, wherein the analyzed information is transmitted in real time to an external device and used for further analysis. [Explanation of Symbols]

[0826] 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. An integrated circuit having communication functions, A processing device that performs data analysis using a neural network, connected to the integrated circuit using common connection technology, A control system that automatically makes decisions and generates control signals based on data analyzed by the aforementioned processing device, A system that includes this.

2. The system according to claim 1, wherein the integrated circuit includes means for selecting an optimized protocol for the connected network.

3. The system according to claim 1, wherein the analyzed data is transmitted in real time to an external device and used for further analysis.

Images