system

Abstract

We provide the system. [Solution] A sensor means that detects user behavior and acquires data, A processing means for inferring the user's intent based on the analysis of the aforementioned data, A means of providing advice to users, A household task support 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When performing tasks at home, it is often interrupted, the next action to be taken is forgotten, and the location of things becomes unknown. The purpose is to solve these problems. Such problems particularly reduce efficiency and cause stress in busy daily life. With the conventional mechanism, it has been difficult to comprehensively and real-time respond to these problems.

Means for Solving the Problems

[0005] This invention includes sensor means for detecting user movements, processing means for analyzing the acquired data, and a function for inferring the user's intentions. Furthermore, by combining voice processing means for converting and analyzing voice input into text, and response means for providing optimal advice to the user, it becomes possible to provide support for users to perform household tasks without interruption. As a result, users can proceed with tasks efficiently and prevent forgetting things or making mistakes.

[0006] "User" refers to an individual who uses the system within their home to perform tasks.

[0007] "Action detection" refers to using sensors to observe the user's movements and changes in their surroundings in real time and acquiring that data.

[0008] "Sensing means" refers to devices or equipment installed to detect the physical movements of users or changes in the environment.

[0009] "Data analysis" refers to the process of processing raw data using various methods to perform calculations and evaluations, transforming it into information that helps understand user behavior and intentions.

[0010] "Inferring intent" refers to predicting the next action a user should take or the advice they should receive, based on their past behavioral data and current situation.

[0011] "Processing means" refers to computing devices and software used to analyze and calculate acquired data and generate necessary information.

[0012] "Response means" refers to devices or functions that provide users with appropriate information and advice based on the analysis results.

[0013] "Voice input" refers to the process of inputting the user's voice as a digital signal into the system.

[0014] "Text data" refers to character information converted from voice input and is used for analysis processing.

[0015] "Voice processing means" refers to a device or software function for processing voice input and converting it into text data.

[0016] "In-home task support system" refers to a general term for devices and methods designed to support a user's task execution within a home.

Brief Description of Drawings

[0017] [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. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] To implement this invention, it is necessary to appropriately place multiple sensor devices, wearable devices, and devices with voice recognition capabilities within the home. The sensor means record the user's physical movements and environmental conditions in real time. Based on this information, the process primarily involves inferring the user's actions and intentions.

[0039] Data is first collected by the terminal and transmitted wirelessly to the server. The server analyzes the received data using natural language processing and machine learning algorithms. For example, if a user says, "What did I come here for?", the server refers to relevant past behavioral data and infers that the next task, "hanging up the laundry," has not yet been performed. To communicate this to the user, the terminal provides advice via voice or text.

[0040] As a concrete example, consider a scenario where a user asks in the kitchen, "When is garbage day for this?" A sensor identifies the type of object, and a voice processing device converts the question into text. The server then consults a local database of garbage information and answers, "Friday." The terminal then forwards the answer to the user via its speaker, providing them with the information. Other specific applications include integration with devices such as smartphones and smart speakers, allowing for a user interface that visually displays the progress of household chores.

[0041] In this way, a concrete embodiment of the household co-pilot system is realized to support the user's daily life and improve efficiency.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects data in real time about the user's movements and environmental conditions from sensor devices placed throughout the home. For example, a motion sensor detects when the user stands up, and a temperature and humidity sensor records the room's conditions.

[0045] Step 2:

[0046] The device transmits the collected data to the server via wireless communication. Here, the data is typically encrypted for privacy reasons.

[0047] Step 3:

[0048] The server analyzes the received data and uses natural language processing algorithms to infer the user's intent behind their speech and actions. For example, it might refer to data converted from speech input to text to analyze the user's question, "What should I do next?"

[0049] Step 4:

[0050] The server generates recommended tasks based on the analysis results and the user's past activity history. Machine learning models are used to create advice that takes into account the user's habits and current context.

[0051] Step 5:

[0052] The server sends the generated advice (for example, "Please hang up the laundry") to the terminal. The terminal receives this message and communicates it to the user using voice or display.

[0053] Step 6:

[0054] Based on the advice provided, users will take the next steps. If necessary, they can ask additional questions to the device to seek further advice.

[0055] Step 7:

[0056] Based on user feedback and the actions taken, the server updates the database and improves the accuracy of the model. This feedback loop allows the system to continuously improve.

[0057] (Example 1)

[0058] 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."

[0059] In modern households, users seek greater efficiency in their daily lives, yet find it difficult to manage multiple household chores and tasks simultaneously. Furthermore, despite the increasing use of voice-activated assistance, systems that accurately predict and support individual tasks and actions remain limited. There is a need to improve this situation and provide more effective support for users' time management and task completion.

[0060] 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.

[0061] In this invention, the server includes detection means for detecting and acquiring information on the user's actions and environmental conditions, transmission means for transmitting the information wirelessly, and analysis means for analyzing the received information to infer the user's intentions and recommend the next action based on the behavioral history. As a result, the user can benefit from a system that supports complex task management and efficiently complete tasks in their daily life.

[0062] A "user" is a person who operates this system and receives support for managing their daily tasks.

[0063] "Action" refers to the physical movements and tasks performed by the user, which are detected by sensor devices.

[0064] "Environmental conditions" refer to physical conditions related to the situation inside a home, such as indoor temperature and brightness.

[0065] "Detection means" refers to devices or systems used to sense the user's actions and environmental conditions in real time.

[0066] "Information" refers to all data related to the user's actions and environmental conditions obtained through detection means.

[0067] "Wireless communication" is a communication technology that uses radio waves to send and receive data, and is a means of transmitting information between servers.

[0068] "Transmission means" refers to the technology and equipment used to wirelessly transmit collected information to a server.

[0069] "Receiving" refers to the act or process by which a server receives information sent via a transmission method.

[0070] "Analysis" refers to the act of processing data to infer the user's intentions and next actions based on the information received.

[0071] "Intention" refers to the purpose or desired outcome that a user has for their actions.

[0072] "Behavioral history" refers to records based on a user's actions and environmental conditions in the past.

[0073] "Recommendation" refers to a suggestion of actions or tasks that are considered optimal for the user.

[0074] "Analysis means" refers to technologies and devices that interpret received information to clarify the user's intentions and suggest the next course of action.

[0075] "Notification means" refers to technologies or devices used to convey the analyzed results to the user in the form of audio or text.

[0076] This system is designed to support daily life more efficiently and works in conjunction with various devices. The terminal acquires real-time data on the user's movements and environmental conditions from multiple sensor devices and wearable devices placed throughout the home. This includes, for example, heart rate and step count data provided by a smartwatch, and indoor temperature data measured by a temperature sensor. This data is collected by the terminal using Bluetooth or Wi-Fi.

[0077] Next, the device transmits the collected data to the server using wireless communication technology (e.g., HTTPS). This communication is encrypted to ensure data security and protect user privacy.

[0078] The server's role is to analyze the received data. Using programming languages ​​such as Python and R, it applies machine learning and natural language processing to the received data to infer the user's intent. For example, it uses natural language processing techniques to convert the user's voice input into text and read the intent of their actions from that text. A concrete use case would be to analyze the voice-inputted question, "What did I come here for?", and suggest the next task.

[0079] The analyzed results are notified to the user via the device for convenience. Using speech synthesis technology (e.g., voice assistant technology), the analysis results can be announced verbally through the speaker or displayed as text on the screen. Furthermore, by integrating with smart devices equipped with a user interface, visual information presentation is also possible.

[0080] For example, when a user asks, "When is garbage day?", the server checks the local garbage collection schedule and answers, "It's Friday," thus resolving the user's question. Furthermore, by using a prompt message such as, "Based on the question the user asked in the kitchen, determine the next suggested action," the generative AI model can be made to suggest more appropriate tasks.

[0081] This system supports smooth task management within the home and provides the ability to streamline users' daily lives.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The terminal collects data from sensor devices placed within the home. The input in this step is physical data obtained from the sensor devices (e.g., heart rate, temperature, brightness), and this data is processed by being temporarily stored in the terminal before being sent to the cloud. In operation, the terminal collects information from each sensor device via Bluetooth or Wi-Fi and organizes it.

[0085] Step 2:

[0086] The terminal transmits the collected data to the server using wireless communication technology. The input in this step is the sensor data stored in the terminal in step 1, and the output is that this data is delivered to the server via a secure protocol (e.g., HTTPS). Specifically, the terminal periodically establishes a connection with the server and sends encrypted data packets.

[0087] Step 3:

[0088] The server analyzes the received data. The input is the received sensor data, and the output is the result of inferring the user's intentions and the next recommended action. Data processing here involves analyzing the data using natural language processing and machine learning algorithms. Specifically, the server processes the data using Python libraries and uses a generative AI model to recommend actions.

[0089] Step 4:

[0090] The server sends the analysis results to the terminal in voice or text format. The input in this step is the analysis results generated in step 3, and the output is prepared notification information to be provided to the user. Specifically, the server formats the results obtained by the generated AI model appropriately and sends them to the terminal again using wireless communication.

[0091] Step 5:

[0092] The terminal notifies the user of the received information using speech synthesis technology or display. In this step, the input is the notification information sent from the server, and the output is the voice message or text information received by the user. Specifically, the terminal activates its speech synthesis engine to read the information aloud through the speaker or displays it as text on the screen.

[0093] (Application Example 1)

[0094] 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."

[0095] A challenge exists in that it is difficult for customers to quickly access product and promotional information they need while shopping in stores. This is especially true in large stores or stores handling multiple product categories, where it is difficult for customers to find the products they are looking for and their detailed information. Existing systems require customers to ask store staff, which does not provide an efficient shopping experience.

[0096] 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.

[0097] In this invention, the server includes detection means for detecting movement patterns, processing means for inferring purchase intent, and response means for providing product information. This allows customers to instantly obtain product information and campaign information within the store.

[0098] "Detection methods for detecting movement patterns" refer to technologies for understanding the location and direction of movement of users in real time, and include devices and algorithms that accurately track the movements and actions of users within a store.

[0099] The "processing means for inferring purchasing intent" is an analytical function that uses user movement data obtained from detection means to infer the products that users are looking for and the categories they are interested in.

[0100] A "means of providing product information" refers to a system that provides information to users through voice, text, or visual displays, in accordance with the user's purchasing intent, and is a method for conveying relevant product and campaign information to users.

[0101] To implement this invention, a server, a terminal, and a sensor device for detecting user movement are used. The sensor device is placed within the store and monitors the user's location and movement in real time. The server receives data acquired from the sensor and analyzes the user's movement pattern using a detection means for detecting movement. Based on this analysis, the server performs processing to infer the user's purchasing intent.

[0102] Based on the analyzed data, the server utilizes response mechanisms to provide product information. Specifically, it generates personalized product and campaign information by combining products the user showed interest in during their browsing history with their past purchase history, and sends this information to the terminal. Terminals include smartphones and smart glasses, and these devices provide information to the user through voice and visual displays.

[0103] For example, if the server infers that a user is looking for clothing from a specific brand, it will quickly provide inventory and promotional information for products related to that brand. Also, if a user voice-inputs a question such as "When is this item on sale?" through their device, speech recognition software will convert the speech into text, and the server will retrieve that information from its database and respond. Existing technologies such as Google® Speech-to-Text will be used for speech recognition.

[0104] The generated information can be used to improve the shopping experience by allowing users to quickly access the information they want. For example, by giving the AI ​​model instructions such as, "When a user asks for details about a specific product in the store, please provide relevant information. For example, if a user wants to know if a product is on sale or what color variations are available, please provide appropriate information in both voice and text," the effectiveness of the response method can be increased.

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

[0106] Step 1:

[0107] As a user moves within the store, sensor devices acquire their location data in real time. This acquired location data is sent to a server as input to create a map of the user's movement path. The server analyzes the user's current location and movement pattern based on this movement path data.

[0108] Step 2:

[0109] The server infers the user's purchasing intent from their movement data. This inference utilizes past purchase history data and location information of products the user has shown interest in within the store. The server processes this input data and generates output information about products that the user is likely to be interested in.

[0110] Step 3:

[0111] A user asks a question by voice, such as, "What is the sale period for this product?" This voice is captured by the device, and speech recognition software converts it into text data. This text data is sent to a server and used as input for analysis. The server queries a database based on this text data and outputs relevant product information and sale information.

[0112] Step 4:

[0113] Product information output from the server is transmitted to the terminal. The terminal then provides the user with answers via voice or visual display. As a result, users can smoothly obtain detailed product information and campaign information within the store.

[0114] In this way, a system is realized in which servers, terminals, and sensor devices work together to improve the user's shopping experience.

[0115] 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.

[0116] This invention is a system for understanding a user's behavior and emotions in real time within the home and providing appropriate advice. This system is composed of a combination of sensor means, voice processing means, emotion engine, and learning means.

[0117] The terminal uses multiple sensor devices to acquire information about the user's movements and environment. This includes motion sensors and temperature / humidity sensors, and based on this information, it understands the user's current situation. The terminal also receives voice input from the user and converts the voice into text data using voice processing equipment.

[0118] Next, the server analyzes the received text data to infer the user's intent. Natural language processing techniques are used for this analysis. Then, the emotion engine analyzes the user's voice and facial expression data to determine their emotional state, such as joy, anger, sadness, or happiness. This makes it possible to provide advice that takes into account not only the user's actions but also their emotions.

[0119] For example, if a user says "Can I rest now?" in a tired voice, the server will determine that rest is necessary based on the voice-to-text analysis and sentiment analysis results. The server will then provide suggestions for relaxation and advice considering the priorities of the next day's schedule from the device.

[0120] Ultimately, the server learns by combining emotional data with past behavioral history, using a feedback loop to provide more precise and personalized support to the user again. This enables an efficient and less stressful life tailored to the user's preferences. By using a variety of devices and technologies in this way, it is possible to implement a form of life support that takes the user's emotions into consideration.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The device uses sensor devices placed within the home to collect data on the user's movements and the surrounding environment. This includes motion detection using motion sensors and facial expression capture using cameras. The data is processed in real time.

[0124] Step 2:

[0125] The terminal receives the user's voice input via a microphone, and a voice processing device converts the voice into text data. This text data is then sent to a server for later analysis.

[0126] Step 3:

[0127] The server analyzes the speech-to-text data and performs natural language processing to understand the user's questions and intentions. This clarifies what the user is asking for.

[0128] Step 4:

[0129] To recognize emotions from the user's voice tone and facial expressions, the server uses an emotion engine. It determines the user's current emotions from the volume of their voice and image data, and stores this information in a database.

[0130] Step 5:

[0131] The server integrates analyzed text data, sentiment data, and past behavioral history data to generate optimal advice for the user. In this process, a learning algorithm is used to reflect the user's past emotional responses and behavioral patterns.

[0132] Step 6:

[0133] The generated advice is sent from the server to the terminal. The terminal then delivers this advice to the user via voice output or text display. For example, if the user is feeling stressed, it might suggest relaxing activities.

[0134] Step 7:

[0135] Users take action and provide feedback based on the advice they receive. They can also determine whether the advice was effective and request more detailed assistance through additional utterances.

[0136] Step 8:

[0137] The server updates the system's database based on user feedback and actual behavioral results, improving the accuracy of future recommendations. In this way, the system continuously evolves, more effectively enhancing support for users.

[0138] (Example 2)

[0139] 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".

[0140] In modern home environments, users face a variety of tasks and stresses, but they need appropriate advice based on their individual behavior and emotions. Conventional technologies struggle to effectively analyze and respond to user behavior and environmental information, and there is a particular challenge in providing support that addresses emotions.

[0141] 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.

[0142] In this invention, the server includes a device that detects and acquires data on the user's behavior and environmental information, a processing device that analyzes the data and voice input to infer the user's intentions and emotional state, and a response device that provides personalized advice based on the user's intentions and emotional state. This enables precise and personalized support based on the user's behavior and emotions.

[0143] A "device that detects behavioral and environmental information and acquires data" is a device that includes sensor technology to sense a user's physical movements and multiple environmental factors, and record that information as digital data.

[0144] A "processing device that analyzes data and voice input to infer the user's intentions and emotional state" is a device that analyzes acquired data and voice information and performs calculations to understand the user's purpose and feelings.

[0145] A "personalized advice-providing response device" is a device that generates support and suggestions tailored to the user's specific situation and psychological state, and then communicates them to the user.

[0146] A "learning device that accumulates historical data and improves accuracy" is a device that uses records of past interactions to improve the system's decision-making ability, enabling more accurate and effective functionality in future use.

[0147] An "analysis device that converts audio data into text and determines emotional state" is a device equipped with the function of converting received audio data into textual information and using it to identify the user's emotional state.

[0148] A "generation device that converts and provides individualized advice using a generation algorithm" is a device that incorporates technology to create personalized advice for each user based on analysis results using a mechanical method, and then present that advice to the user.

[0149] This invention is a system for understanding a user's behavior and emotions within the home and providing personalized advice. The system consists of a combination of a sensor device, an audio processing device, a processing device, a response device, and a learning device.

[0150] The device is equipped with multiple sensors, including motion sensors and environmental sensors, to acquire information about the user's behavior and environment. Based on this information, it understands the user's current situation. It also receives voice input and converts the voice into text data using voice processing software (e.g., speech recognition API).

[0151] The server analyzes text data received from the terminal and determines the user's intent and emotional state through natural language processing techniques (e.g., language analysis libraries). This analysis identifies emotions such as joy, anger, sadness, and happiness, and understands the user's intent. For example, if a user uses an expression like "I want to know how to relax," the server analyzes that intent and integrates the results of the emotional analysis performed by the emotion engine.

[0152] Based on the analysis results, the server uses a generation algorithm (e.g., a generation AI model) to create personalized advice best suited to the user. This advice may include relaxation techniques or advice on prioritizing daily life.

[0153] The response device provides the user with generated advice via voice or display. The user can then act based on this response, and the system uses this response and action again for learning. The server uses a feedback loop to accumulate recorded behavioral history and sentiment information, improving the overall accuracy of the system.

[0154] As a concrete example, here is an example of a prompt statement for a generative AI model: "Please give specific advice if the user says they want to relax." Using this prompt statement, the model will generate advice tailored to the user.

[0155] As described above, this system provides personalized support tailored to each user's individual circumstances, promoting the realization of an efficient and fulfilling life.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The device collects information on the user's physical movements and the indoor environment through motion sensors and environmental sensors. Inputs include the user's location and movements, as well as environmental information such as room temperature and humidity. The collected data is transmitted to a server in digital format as output. This process allows the user's current activity and surrounding conditions to be recorded in real time as digital data.

[0159] Step 2:

[0160] The terminal receives the user's voice using a microphone. The input is the user's voice data. This data is converted into text data by a speech processing device. The output is the converted text data, which is sent to the server. Specifically, a process is performed to convert speech into text information using speech recognition technology.

[0161] Step 3:

[0162] The server receives text data sent from the terminal and analyzes its content using natural language processing technology. The input is text data converted from speech. This analysis involves data processing to extract the user's intent and requests. The output is information about the user's purpose and questions. To clarify the user's intent from the analysis results, a machine learning algorithm that understands the context is applied.

[0163] Step 4:

[0164] The server analyzes received emotional data in addition to text data using an emotion engine. The input is text data containing metadata such as the user's voice tone and pace. This process involves data calculations to identify emotional states such as joy, anger, sadness, and happiness. The output is a determination of the user's emotional state. At this stage, the user's psychological state is evaluated based on the tone and speed of their voice.

[0165] Step 5:

[0166] The server uses a generative AI model based on the obtained intentions and emotional state to generate the best advice to provide to the user. The input is the analysis results regarding the user's intentions and emotional state. The output is a specific advice message for the user. The generative AI model uses this information to create personalized advice. For example, if the user inputs "I want to rest a little," it will suggest ways to relax based on that.

[0167] Step 6:

[0168] The terminal communicates the generated advice to the user. The input is the advice generated by the server. The output is the specific suggestions the user receives visually or audibly. At this stage, speech synthesis technology can be used to provide voice feedback, and it can also be displayed as text via the display.

[0169] Step 7:

[0170] The server accumulates user interaction and behavioral history and performs feedback learning to improve the quality of future responses. The input is past response history and behavioral data. The output is an updated learning model that continuously improves system response accuracy. This learning process adjusts future user interactions to be more effective and precise.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] In today's commercial environment, accurately meeting the diverse needs of customers remains a major challenge. To address this, a system is needed that can understand customer behavior and emotions in real time and provide more personalized sales support. However, current technology struggles to accurately analyze customer emotions and provide immediate advice based on that analysis. Therefore, there is a need for a system that enables appropriate approaches tailored to each individual customer.

[0174] 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.

[0175] In this invention, the server includes sensor means for detecting user behavior and acquiring data, processing means for inferring the user's intentions based on the analysis of the data, emotion analysis means for identifying and analyzing the user's emotions, and response means for providing sales support in a commercial environment. This enables sales promotion that takes into account the diverse emotions of customers.

[0176] A "sensor device" is a device used to detect user behavior and acquire data.

[0177] "Processing means" refers to a device or system that has the function of analyzing data acquired from sensor means and inferring the user's intentions.

[0178] "Emotional analysis means" refers to technology for identifying and analyzing data collected from users' emotions.

[0179] A "response means" is a device or system that has the function of providing appropriate sales support to users in a commercial environment based on the analysis results.

[0180] The system implementing this invention is designed to understand customer behavior and emotions in a commercial environment in real time and provide appropriate sales support advice. The server uses motion sensors and cameras to collect data necessary for emotion analysis in order to detect the movements of users (customers). This data is transmitted immediately in a cloud environment using services such as Google Cloud Platform and Microsoft Azure.

[0181] The server analyzes the acquired data using natural language processing and machine learning techniques. For example, it uses the Google Cloud Natural Language API to convert customer voice input into text data, which is then interpreted and sentiment analyzed. The analyzed results are displayed on the salesperson's smart device, such as smart glasses. This response method enables salespeople to provide more personalized service based on customer needs and emotions.

[0182] For example, if a customer looks hesitant while browsing a particular product, the server displays information on the salesperson's smart glasses indicating that "this customer may be unsure about making a purchase." Based on this information, the salesperson can provide more detailed explanations about the product, thereby increasing customer satisfaction.

[0183] An example of a prompt when using a generative AI model is, "Please provide information on how to approach a target product that will interest the customer." In this way, advanced customer service is realized by combining real-time data analysis with sales promotion.

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

[0185] Step 1:

[0186] The terminal uses in-store sensors to acquire customer motion data and environmental data. This includes capturing customer movements and facial expressions using motion sensors and cameras. Inputs are motion data and environmental data, while output is raw data containing these.

[0187] Step 2:

[0188] The device sends the acquired motion data to the server. If audio data is included, it is converted into text data using speech processing equipment. This conversion process utilizes the Google Cloud Natural Language API. The input is audio data, and the output is text data.

[0189] Step 3:

[0190] The server analyzes text data and facial expression data using emotion analysis tools to determine the customer's emotional state. For example, it uses the Microsoft Azure Face API to identify emotions such as joy, anger, sadness, and happiness from facial expressions. The input is text data and facial expression data, and the output is the emotional state as a result of the analysis.

[0191] Step 4:

[0192] The server generates appropriate advice commands based on the customer's emotional state. During this process, sales promotion prompts are sent to the AI ​​model, resulting in specific suggestions such as, "This customer may be hesitant to make a purchase." The input is the emotional state, and the output is the advice command.

[0193] Step 5:

[0194] The server sends the generated advice commands to the terminal, which then displays the information on the salesperson's smart glasses. This allows the salesperson to respond appropriately to the customer in real time. The input is the advice commands, and the output is visual information for the salesperson.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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".

[0211] To implement this invention, it is necessary to appropriately place multiple sensor devices, wearable devices, and devices with voice recognition capabilities within the home. The sensor means record the user's physical movements and environmental conditions in real time. Based on this information, the process primarily involves inferring the user's actions and intentions.

[0212] Data is first collected by the terminal and transmitted wirelessly to the server. The server analyzes the received data using natural language processing and machine learning algorithms. For example, if a user says, "What did I come here for?", the server refers to relevant past behavioral data and infers that the next task, "hanging up the laundry," has not yet been performed. To communicate this to the user, the terminal provides advice via voice or text.

[0213] As a concrete example, consider a scenario where a user asks in the kitchen, "When is garbage day for this?" A sensor identifies the type of object, and a voice processing device converts the question into text. The server then consults a local database of garbage information and answers, "Friday." The terminal then forwards the answer to the user via its speaker, providing them with the information. Other specific applications include integration with devices such as smartphones and smart speakers, allowing for a user interface that visually displays the progress of household chores.

[0214] In this way, a concrete embodiment of the household co-pilot system is realized to support the user's daily life and improve efficiency.

[0215] The following describes the processing flow.

[0216] Step 1:

[0217] The device collects data in real time about the user's movements and environmental conditions from sensor devices placed throughout the home. For example, a motion sensor detects when the user stands up, and a temperature and humidity sensor records the room's conditions.

[0218] Step 2:

[0219] The device transmits the collected data to the server via wireless communication. Here, the data is typically encrypted for privacy reasons.

[0220] Step 3:

[0221] The server analyzes the received data and uses natural language processing algorithms to infer the user's intent behind their speech and actions. For example, it might refer to data converted from speech input to text to analyze the user's question, "What should I do next?"

[0222] Step 4:

[0223] The server generates recommended tasks based on the analysis results and the user's past activity history. Machine learning models are used to create advice that takes into account the user's habits and current context.

[0224] Step 5:

[0225] The server sends the generated advice (for example, "Please hang up the laundry") to the terminal. The terminal receives this message and communicates it to the user using voice or display.

[0226] Step 6:

[0227] Based on the advice provided, users will take the next steps. If necessary, they can ask additional questions to the device to seek further advice.

[0228] Step 7:

[0229] Based on user feedback and the actions taken, the server updates the database and improves the accuracy of the model. This feedback loop allows the system to continuously improve.

[0230] (Example 1)

[0231] 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."

[0232] In modern households, users seek greater efficiency in their daily lives, yet find it difficult to manage multiple household chores and tasks simultaneously. Furthermore, despite the increasing use of voice-activated assistance, systems that accurately predict and support individual tasks and actions remain limited. There is a need to improve this situation and provide more effective support for users' time management and task completion.

[0233] 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.

[0234] In this invention, the server includes detection means for detecting and acquiring information on the user's actions and environmental conditions, transmission means for transmitting the information wirelessly, and analysis means for analyzing the received information to infer the user's intentions and recommend the next action based on the behavioral history. As a result, the user can benefit from a system that supports complex task management and efficiently complete tasks in their daily life.

[0235] A "user" is a person who operates this system and receives support for managing their daily tasks.

[0236] "Action" refers to the physical movements and tasks performed by the user, which are detected by sensor devices.

[0237] "Environmental conditions" refer to physical conditions related to the situation inside a home, such as indoor temperature and brightness.

[0238] "Detection means" refers to devices or systems used to sense the user's actions and environmental conditions in real time.

[0239] "Information" refers to all data related to the user's actions and environmental conditions obtained through detection means.

[0240] "Wireless communication" is a communication technology that uses radio waves to send and receive data, and is a means of transmitting information between servers.

[0241] "Transmission means" refers to the technology and equipment used to wirelessly transmit collected information to a server.

[0242] "Receiving" refers to the act or process by which a server receives information sent via a transmission method.

[0243] "Analysis" refers to the act of processing data to infer the user's intentions and next actions based on the information received.

[0244] "Intention" refers to the purpose or desired outcome that a user has for their actions.

[0245] "Behavioral history" refers to records based on a user's actions and environmental conditions in the past.

[0246] "Recommendation" refers to a suggestion of actions or tasks that are considered optimal for the user.

[0247] "Analysis means" refers to technologies and devices that interpret received information to clarify the user's intentions and suggest the next course of action.

[0248] "Notification means" refers to technologies or devices used to convey the analyzed results to the user in the form of audio or text.

[0249] This system is designed to support daily life more efficiently and works in conjunction with various devices. The terminal acquires real-time data on the user's movements and environmental conditions from multiple sensor devices and wearable devices placed throughout the home. This includes, for example, heart rate and step count data provided by a smartwatch, and indoor temperature data measured by a temperature sensor. This data is collected by the terminal using Bluetooth or Wi-Fi.

[0250] Next, the device transmits the collected data to the server using wireless communication technology (e.g., HTTPS). This communication is encrypted to ensure data security and protect user privacy.

[0251] The server's role is to analyze the received data. Using programming languages ​​such as Python and R, it applies machine learning and natural language processing to the received data to infer the user's intent. For example, it uses natural language processing techniques to convert the user's voice input into text and read the intent of their actions from that text. A concrete use case would be to analyze the voice-inputted question, "What did I come here for?", and suggest the next task.

[0252] The analyzed results are notified to the user via the device for convenience. Using speech synthesis technology (e.g., voice assistant technology), the analysis results can be announced verbally through the speaker or displayed as text on the screen. Furthermore, by integrating with smart devices equipped with a user interface, visual information presentation is also possible.

[0253] For example, when a user asks, "When is garbage day?", the server checks the local garbage collection schedule and answers, "It's Friday," thus resolving the user's question. Furthermore, by using a prompt message such as, "Based on the question the user asked in the kitchen, determine the next suggested action," the generative AI model can be made to suggest more appropriate tasks.

[0254] This system supports smooth task management within the home and provides the ability to streamline users' daily lives.

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

[0256] Step 1:

[0257] The terminal collects data from sensor devices placed within the home. The input in this step is physical data obtained from the sensor devices (e.g., heart rate, temperature, brightness), and this data is processed by being temporarily stored in the terminal before being sent to the cloud. In operation, the terminal collects information from each sensor device via Bluetooth or Wi-Fi and organizes it.

[0258] Step 2:

[0259] The terminal transmits the collected data to the server using wireless communication technology. The input in this step is the sensor data stored in the terminal in step 1, and the output is that this data is delivered to the server via a secure protocol (e.g., HTTPS). Specifically, the terminal periodically establishes a connection with the server and sends encrypted data packets.

[0260] Step 3:

[0261] The server analyzes the received data. The input is the received sensor data, and the output is the result of inferring the user's intentions and the next recommended action. Data processing here involves analyzing the data using natural language processing and machine learning algorithms. Specifically, the server processes the data using Python libraries and uses a generative AI model to recommend actions.

[0262] Step 4:

[0263] The server sends the analysis results to the terminal in voice or text format. The input in this step is the analysis results generated in step 3, and the output is prepared notification information to be provided to the user. Specifically, the server formats the results obtained by the generated AI model appropriately and sends them to the terminal again using wireless communication.

[0264] Step 5:

[0265] The terminal notifies the user of the received information using speech synthesis technology or display. In this step, the input is the notification information sent from the server, and the output is the voice message or text information received by the user. Specifically, the terminal activates its speech synthesis engine to read the information aloud through the speaker or displays it as text on the screen.

[0266] (Application Example 1)

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

[0268] A challenge exists in that it is difficult for customers to quickly access product and promotional information they need while shopping in stores. This is especially true in large stores or stores handling multiple product categories, where it is difficult for customers to find the products they are looking for and their detailed information. Existing systems require customers to ask store staff, which does not provide an efficient shopping experience.

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

[0270] In this invention, the server includes detection means for detecting movement patterns, processing means for inferring purchase intent, and response means for providing product information. This allows customers to instantly obtain product information and campaign information within the store.

[0271] "Detection methods for detecting movement patterns" refer to technologies for understanding the location and direction of movement of users in real time, and include devices and algorithms that accurately track the movements and actions of users within a store.

[0272] The "processing means for inferring purchasing intent" is an analytical function that uses user movement data obtained from detection means to infer the products that users are looking for and the categories they are interested in.

[0273] A "means of providing product information" refers to a system that provides information to users through voice, text, or visual displays, in accordance with the user's purchasing intent, and is a method for conveying relevant product and campaign information to users.

[0274] To implement this invention, a server, a terminal, and a sensor device for detecting user movement are used. The sensor device is placed within the store and monitors the user's location and movement in real time. The server receives data acquired from the sensor and analyzes the user's movement pattern using a detection means for detecting movement. Based on this analysis, the server performs processing to infer the user's purchasing intent.

[0275] Based on the analyzed data, the server utilizes response mechanisms to provide product information. Specifically, it generates personalized product and campaign information by combining products the user showed interest in during their browsing history with their past purchase history, and sends this information to the terminal. Terminals include smartphones and smart glasses, and these devices provide information to the user through voice and visual displays.

[0276] For example, if the server infers that a user is looking for clothing from a specific brand, it will quickly provide inventory and promotional information for products related to that brand. Also, if a user voice-inputs a question such as "When is this item on sale?" through their device, speech recognition software will convert the speech into text, and the server will retrieve that information from its database and respond. Existing technologies such as Google Speech-to-Text will be used for speech recognition.

[0277] The generated information can be used to improve the shopping experience by allowing users to quickly access the information they want. As an example of a prompt sentence, by giving an instruction such as "When a user asks for details of a specific product in a store, please provide relevant information. For example, when a user wants to know if a product is on sale or the color variations in stock, please respond with appropriate information in voice and text." to the AI model, the effectiveness of the response means can be enhanced.

[0278] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0279] Step 1:

[0280] When the user moves within the store, the sensor device acquires the user's position data in real time. The acquired position data is transmitted to the server as input to form the user's movement path. The server analyzes the user's current position and movement pattern based on this movement path data.

[0281] Step 2:

[0282] The server infers the user's purchase intention from the movement path data. For this inference, past purchase history data and the position information of the products that showed interest in the store are used. The server processes these input data and generates, as output, product information that the user is likely to be interested in.

[0283] Step 3:

[0284] The user asks "What is the sale period of this product?" in voice. The voice is captured by the terminal and converted into text data by voice recognition software. This text data is transmitted to the server and used as input for analysis. The server queries the database based on this text data and outputs relevant product information and sale information.

[0285] Step 4:

[0286] The product information output from the server is transmitted to the terminal. The terminal presents an answer to the user through voice or a visual display. As a result, the user can smoothly obtain detailed product information and campaign information within the store.

[0287] In this way, a system is realized in which the server, the terminal, and the sensor device cooperate to improve the user's shopping experience.

[0288] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0289] This invention is a system for grasping the actions and emotions of users in real time within a home and providing appropriate advice. This system is configured by combining sensor means, voice processing means, an emotion engine, and learning means.

[0290] The terminal uses a plurality of sensor devices to acquire the user's motion and environmental information. This includes motion sensors and temperature / humidity sensors, and based on this information, the current situation of the user is grasped. Also, the terminal receives the user's voice input and converts the voice into text data using voice processing means.

[0291] Next, the server performs a process of analyzing the received text data to infer the user's intention. Natural language processing techniques are used for the analysis. Then, the emotion engine analyzes the user's voice and facial expression data to determine the emotional state such as joy, anger, sorrow, and happiness. This enables advice that takes into account not only the user's actions but also their emotions.

[0292] For example, if a user says "Can I rest now?" in a tired voice, the server will determine that rest is necessary based on the voice-to-text analysis and sentiment analysis results. The server will then provide suggestions for relaxation and advice considering the priorities of the next day's schedule from the device.

[0293] Ultimately, the server learns by combining emotional data with past behavioral history, using a feedback loop to provide more precise and personalized support to the user again. This enables an efficient and less stressful life tailored to the user's preferences. By using a variety of devices and technologies in this way, it is possible to implement a form of life support that takes the user's emotions into consideration.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The device uses sensor devices placed within the home to collect data on the user's movements and the surrounding environment. This includes motion detection using motion sensors and facial expression capture using cameras. The data is processed in real time.

[0297] Step 2:

[0298] The terminal receives the user's voice input via a microphone, and a voice processing device converts the voice into text data. This text data is then sent to a server for later analysis.

[0299] Step 3:

[0300] The server analyzes the speech-to-text data and performs natural language processing to understand the user's questions and intentions. This clarifies what the user is asking for.

[0301] Step 4:

[0302] To recognize emotions from the tone of the user's voice and expressions, the server uses an emotion engine. From the strength of the voice and image data, the server discriminates the user's current emotion and stores it in the database.

[0303] Step 5:

[0304] The server integrates the analyzed text data, emotion data, and data on past behavior history to generate optimal advice for the user. At this time, a learning algorithm is used to reflect the user's past emotional reactions and behavior patterns.

[0305] Step 6:

[0306] The generated advice is sent from the server to the terminal. The terminal distributes the advice to the user through voice output or text display. For example, when the user is feeling stressed, suggestions for relaxing activities are provided.

[0307] Step 7:

[0308] The user takes actions and provides feedback based on the provided advice. It is also possible to determine whether the advice was effective and request more detailed support through additional conversation.

[0309] Step 8:

[0310] The server updates the system's database based on the feedback from the user and the actual action results to improve the accuracy of future recommendations. In this way, the system continuously evolves to more effectively strengthen the support for the user.

[0311] (Example 2)

[0312] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] In modern home environments, users face a variety of tasks and stresses, but they need appropriate advice based on their individual behavior and emotions. Conventional technologies struggle to effectively analyze and respond to user behavior and environmental information, and there is a particular challenge in providing support that addresses emotions.

[0314] 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.

[0315] In this invention, the server includes a device that detects and acquires data on the user's behavior and environmental information, a processing device that analyzes the data and voice input to infer the user's intentions and emotional state, and a response device that provides personalized advice based on the user's intentions and emotional state. This enables precise and personalized support based on the user's behavior and emotions.

[0316] A "device that detects behavioral and environmental information and acquires data" is a device that includes sensor technology to sense a user's physical movements and multiple environmental factors, and record that information as digital data.

[0317] A "processing device that analyzes data and voice input to infer the user's intentions and emotional state" is a device that analyzes acquired data and voice information and performs calculations to understand the user's purpose and feelings.

[0318] A "personalized advice-providing response device" is a device that generates support and suggestions tailored to the user's specific situation and psychological state, and then communicates them to the user.

[0319] A "learning device that accumulates historical data and improves accuracy" is a device that uses records of past interactions to improve the system's decision-making ability, enabling more accurate and effective functionality in future use.

[0320] An "analysis device that converts audio data into text and determines emotional state" is a device equipped with the function of converting received audio data into textual information and using it to identify the user's emotional state.

[0321] A "generation device that converts and provides individualized advice using a generation algorithm" is a device that incorporates technology to create personalized advice for each user based on analysis results using a mechanical method, and then present that advice to the user.

[0322] This invention is a system for understanding a user's behavior and emotions within the home and providing personalized advice. The system consists of a combination of a sensor device, an audio processing device, a processing device, a response device, and a learning device.

[0323] The device is equipped with multiple sensors, including motion sensors and environmental sensors, to acquire information about the user's behavior and environment. Based on this information, it understands the user's current situation. It also receives voice input and converts the voice into text data using voice processing software (e.g., speech recognition API).

[0324] The server analyzes text data received from the terminal and determines the user's intent and emotional state through natural language processing techniques (e.g., language analysis libraries). This analysis identifies emotions such as joy, anger, sadness, and happiness, and understands the user's intent. For example, if a user uses an expression like "I want to know how to relax," the server analyzes that intent and integrates the results of the emotional analysis performed by the emotion engine.

[0325] Based on the analysis results, the server uses a generation algorithm (e.g., a generation AI model) to create personalized advice best suited to the user. This advice may include relaxation techniques or advice on prioritizing daily life.

[0326] The response device provides the user with generated advice via voice or display. The user can then act based on this response, and the system uses this response and action again for learning. The server uses a feedback loop to accumulate recorded behavioral history and sentiment information, improving the overall accuracy of the system.

[0327] As a concrete example, here is an example of a prompt statement for a generative AI model: "Please give specific advice if the user says they want to relax." Using this prompt statement, the model will generate advice tailored to the user.

[0328] As described above, this system provides personalized support tailored to each user's individual circumstances, promoting the realization of an efficient and fulfilling life.

[0329] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0330] Step 1:

[0331] The device collects information on the user's physical movements and the indoor environment through motion sensors and environmental sensors. Inputs include the user's location and movements, as well as environmental information such as room temperature and humidity. The collected data is transmitted to a server in digital format as output. This process allows the user's current activity and surrounding conditions to be recorded in real time as digital data.

[0332] Step 2:

[0333] The terminal receives the user's voice using a microphone. The input is the user's voice data. This data is converted into text data by a speech processing device. The output is the converted text data, which is sent to the server. Specifically, a process is performed to convert speech into text information using speech recognition technology.

[0334] Step 3:

[0335] The server receives text data sent from the terminal and analyzes its content using natural language processing technology. The input is text data converted from speech. This analysis involves data processing to extract the user's intent and requests. The output is information about the user's purpose and questions. To clarify the user's intent from the analysis results, a machine learning algorithm that understands the context is applied.

[0336] Step 4:

[0337] The server analyzes received emotional data in addition to text data using an emotion engine. The input is text data containing metadata such as the user's voice tone and pace. This process involves data calculations to identify emotional states such as joy, anger, sadness, and happiness. The output is a determination of the user's emotional state. At this stage, the user's psychological state is evaluated based on the tone and speed of their voice.

[0338] Step 5:

[0339] The server uses a generative AI model based on the obtained intentions and emotional state to generate the best advice to provide to the user. The input is the analysis results regarding the user's intentions and emotional state. The output is a specific advice message for the user. The generative AI model uses this information to create personalized advice. For example, if the user inputs "I want to rest a little," it will suggest ways to relax based on that.

[0340] Step 6:

[0341] The terminal communicates the generated advice to the user. The input is the advice generated by the server. The output is the specific suggestions the user receives visually or audibly. At this stage, speech synthesis technology can be used to provide voice feedback, and it can also be displayed as text via the display.

[0342] Step 7:

[0343] The server accumulates user interaction and behavioral history and performs feedback learning to improve the quality of future responses. The input is past response history and behavioral data. The output is an updated learning model that continuously improves system response accuracy. This learning process adjusts future user interactions to be more effective and precise.

[0344] (Application Example 2)

[0345] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0346] In today's commercial environment, accurately meeting the diverse needs of customers remains a major challenge. To address this, a system is needed that can understand customer behavior and emotions in real time and provide more personalized sales support. However, current technology struggles to accurately analyze customer emotions and provide immediate advice based on that analysis. Therefore, there is a need for a system that enables appropriate approaches tailored to each individual customer.

[0347] 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.

[0348] In this invention, the server includes sensor means for detecting user behavior and acquiring data, processing means for inferring the user's intentions based on the analysis of the data, emotion analysis means for identifying and analyzing the user's emotions, and response means for providing sales support in a commercial environment. This enables sales promotion that takes into account the diverse emotions of customers.

[0349] A "sensor device" is a device used to detect user behavior and acquire data.

[0350] "Processing means" refers to a device or system that has the function of analyzing data acquired from sensor means and inferring the user's intentions.

[0351] "Emotional analysis means" refers to technology for identifying and analyzing data collected from users' emotions.

[0352] A "response means" is a device or system that has the function of providing appropriate sales support to users in a commercial environment based on the analysis results.

[0353] The system implementing this invention is designed to understand customer behavior and emotions in a commercial environment in real time and provide appropriate sales support advice. The server uses motion sensors and cameras to collect data necessary for emotion analysis in order to detect the movements of users (customers). This data is transmitted immediately in a cloud environment using services such as Google Cloud Platform and Microsoft Azure.

[0354] The server analyzes the acquired data using natural language processing and machine learning techniques. For example, it uses the Google Cloud Natural Language API to convert customer voice input into text data, which is then interpreted and sentiment analyzed. The analyzed results are displayed on the salesperson's smart device, such as smart glasses. This response method enables salespeople to provide more personalized service based on customer needs and emotions.

[0355] For example, if a customer looks hesitant while browsing a particular product, the server displays information on the salesperson's smart glasses indicating that "this customer may be unsure about making a purchase." Based on this information, the salesperson can provide more detailed explanations about the product, thereby increasing customer satisfaction.

[0356] An example of a prompt when using a generative AI model is, "Please provide information on how to approach a target product that will interest the customer." In this way, advanced customer service is realized by combining real-time data analysis with sales promotion.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The terminal uses in-store sensors to acquire customer motion data and environmental data. This includes capturing customer movements and facial expressions using motion sensors and cameras. Inputs are motion data and environmental data, while output is raw data containing these.

[0360] Step 2:

[0361] The device sends the acquired motion data to the server. If audio data is included, it is converted into text data using speech processing equipment. This conversion process utilizes the Google Cloud Natural Language API. The input is audio data, and the output is text data.

[0362] Step 3:

[0363] The server analyzes text data and facial expression data using emotion analysis tools to determine the customer's emotional state. For example, it uses the Microsoft Azure Face API to identify emotions such as joy, anger, sadness, and happiness from facial expressions. The input is text data and facial expression data, and the output is the emotional state as a result of the analysis.

[0364] Step 4:

[0365] The server generates appropriate advice commands based on the customer's emotional state. During this process, sales promotion prompts are sent to the AI ​​model, resulting in specific suggestions such as, "This customer may be hesitant to make a purchase." The input is the emotional state, and the output is the advice command.

[0366] Step 5:

[0367] The server sends the generated advice commands to the terminal, which then displays the information on the salesperson's smart glasses. This allows the salesperson to respond appropriately to the customer in real time. The input is the advice commands, and the output is visual information for the salesperson.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] [Third Embodiment]

[0372] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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".

[0384] To implement this invention, it is necessary to appropriately place multiple sensor devices, wearable devices, and devices with voice recognition capabilities within the home. The sensor means record the user's physical movements and environmental conditions in real time. Based on this information, the process primarily involves inferring the user's actions and intentions.

[0385] Data is first collected by the terminal and transmitted wirelessly to the server. The server analyzes the received data using natural language processing and machine learning algorithms. For example, if a user says, "What did I come here for?", the server refers to relevant past behavioral data and infers that the next task, "hanging up the laundry," has not yet been performed. To communicate this to the user, the terminal provides advice via voice or text.

[0386] As a concrete example, consider a scenario where a user asks in the kitchen, "When is garbage day for this?" A sensor identifies the type of object, and a voice processing device converts the question into text. The server then consults a local database of garbage information and answers, "Friday." The terminal then forwards the answer to the user via its speaker, providing them with the information. Other specific applications include integration with devices such as smartphones and smart speakers, allowing for a user interface that visually displays the progress of household chores.

[0387] In this way, a concrete embodiment of the household co-pilot system is realized to support the user's daily life and improve efficiency.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The device collects data in real time about the user's movements and environmental conditions from sensor devices placed throughout the home. For example, a motion sensor detects when the user stands up, and a temperature and humidity sensor records the room's conditions.

[0391] Step 2:

[0392] The device transmits the collected data to the server via wireless communication. Here, the data is typically encrypted for privacy reasons.

[0393] Step 3:

[0394] The server analyzes the received data and uses natural language processing algorithms to infer the user's intent behind their speech and actions. For example, it might refer to data converted from speech input to text to analyze the user's question, "What should I do next?"

[0395] Step 4:

[0396] The server generates recommended tasks based on the analysis results and the user's past activity history. Machine learning models are used to create advice that takes into account the user's habits and current context.

[0397] Step 5:

[0398] The server sends the generated advice (for example, "Please hang up the laundry") to the terminal. The terminal receives this message and communicates it to the user using voice or display.

[0399] Step 6:

[0400] Based on the advice provided, users will take the next steps. If necessary, they can ask additional questions to the device to seek further advice.

[0401] Step 7:

[0402] Based on user feedback and the actions taken, the server updates the database and improves the accuracy of the model. This feedback loop allows the system to continuously improve.

[0403] (Example 1)

[0404] 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."

[0405] In modern households, users seek greater efficiency in their daily lives, yet find it difficult to manage multiple household chores and tasks simultaneously. Furthermore, despite the increasing use of voice-activated assistance, systems that accurately predict and support individual tasks and actions remain limited. There is a need to improve this situation and provide more effective support for users' time management and task completion.

[0406] 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.

[0407] In this invention, the server includes detection means for detecting and acquiring information on the user's actions and environmental conditions, transmission means for transmitting the information wirelessly, and analysis means for analyzing the received information to infer the user's intentions and recommend the next action based on the behavioral history. As a result, the user can benefit from a system that supports complex task management and efficiently complete tasks in their daily life.

[0408] A "user" is a person who operates this system and receives support for managing their daily tasks.

[0409] "Action" refers to the physical movements and tasks performed by the user, which are detected by sensor devices.

[0410] "Environmental conditions" refer to physical conditions related to the situation inside a home, such as indoor temperature and brightness.

[0411] "Detection means" refers to devices or systems used to sense the user's actions and environmental conditions in real time.

[0412] "Information" refers to all data related to the user's actions and environmental conditions obtained through detection means.

[0413] "Wireless communication" is a communication technology that uses radio waves to send and receive data, and is a means of transmitting information between servers.

[0414] "Transmission means" refers to the technology and equipment used to wirelessly transmit collected information to a server.

[0415] "Receiving" refers to the act or process by which a server receives information sent via a transmission method.

[0416] "Analysis" refers to the act of processing data to infer the user's intentions and next actions based on the information received.

[0417] "Intention" refers to the purpose or desired outcome that a user has for their actions.

[0418] "Behavioral history" refers to records based on a user's actions and environmental conditions in the past.

[0419] "Recommendation" refers to a suggestion of actions or tasks that are considered optimal for the user.

[0420] "Analysis means" refers to technologies and devices that interpret received information to clarify the user's intentions and suggest the next course of action.

[0421] "Notification means" refers to technologies or devices used to convey the analyzed results to the user in the form of audio or text.

[0422] This system is designed to support daily life more efficiently and works in conjunction with various devices. The terminal acquires real-time data on the user's movements and environmental conditions from multiple sensor devices and wearable devices placed throughout the home. This includes, for example, heart rate and step count data provided by a smartwatch, and indoor temperature data measured by a temperature sensor. This data is collected by the terminal using Bluetooth or Wi-Fi.

[0423] Next, the device transmits the collected data to the server using wireless communication technology (e.g., HTTPS). This communication is encrypted to ensure data security and protect user privacy.

[0424] The server's role is to analyze the received data. Using programming languages ​​such as Python and R, it applies machine learning and natural language processing to the received data to infer the user's intent. For example, it uses natural language processing techniques to convert the user's voice input into text and read the intent of their actions from that text. A concrete use case would be to analyze the voice-inputted question, "What did I come here for?", and suggest the next task.

[0425] The analyzed results are notified to the user via the device for convenience. Using speech synthesis technology (e.g., voice assistant technology), the analysis results can be announced verbally through the speaker or displayed as text on the screen. Furthermore, by integrating with smart devices equipped with a user interface, visual information presentation is also possible.

[0426] For example, when a user asks, "When is garbage day?", the server checks the local garbage collection schedule and answers, "It's Friday," thus resolving the user's question. Furthermore, by using a prompt message such as, "Based on the question the user asked in the kitchen, determine the next suggested action," the generative AI model can be made to suggest more appropriate tasks.

[0427] This system supports smooth task management within the home and provides the ability to streamline users' daily lives.

[0428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0429] Step 1:

[0430] The terminal collects data from sensor devices placed within the home. The input in this step is physical data obtained from the sensor devices (e.g., heart rate, temperature, brightness), and this data is processed by being temporarily stored in the terminal before being sent to the cloud. In operation, the terminal collects information from each sensor device via Bluetooth or Wi-Fi and organizes it.

[0431] Step 2:

[0432] The terminal transmits the collected data to the server using wireless communication technology. The input in this step is the sensor data stored in the terminal in step 1, and the output is that this data is delivered to the server via a secure protocol (e.g., HTTPS). Specifically, the terminal periodically establishes a connection with the server and sends encrypted data packets.

[0433] Step 3:

[0434] The server analyzes the received data. The input is the received sensor data, and the output is the result of inferring the user's intentions and the next recommended action. Data processing here involves analyzing the data using natural language processing and machine learning algorithms. Specifically, the server processes the data using Python libraries and uses a generative AI model to recommend actions.

[0435] Step 4:

[0436] The server sends the analysis results to the terminal in voice or text format. The input in this step is the analysis results generated in step 3, and the output is prepared notification information to be provided to the user. Specifically, the server formats the results obtained by the generated AI model appropriately and sends them to the terminal again using wireless communication.

[0437] Step 5:

[0438] The terminal notifies the user of the received information using speech synthesis technology or display. In this step, the input is the notification information sent from the server, and the output is the voice message or text information received by the user. Specifically, the terminal activates its speech synthesis engine to read the information aloud through the speaker or displays it as text on the screen.

[0439] (Application Example 1)

[0440] 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."

[0441] A challenge exists in that it is difficult for customers to quickly access product and promotional information they need while shopping in stores. This is especially true in large stores or stores handling multiple product categories, where it is difficult for customers to find the products they are looking for and their detailed information. Existing systems require customers to ask store staff, which does not provide an efficient shopping experience.

[0442] 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.

[0443] In this invention, the server includes detection means for detecting movement patterns, processing means for inferring purchase intent, and response means for providing product information. This allows customers to instantly obtain product information and campaign information within the store.

[0444] "Detection methods for detecting movement patterns" refer to technologies for understanding the location and direction of movement of users in real time, and include devices and algorithms that accurately track the movements and actions of users within a store.

[0445] The "processing means for inferring purchasing intent" is an analytical function that uses user movement data obtained from detection means to infer the products that users are looking for and the categories they are interested in.

[0446] A "means of providing product information" refers to a system that provides information to users through voice, text, or visual displays, in accordance with the user's purchasing intent, and is a method for conveying relevant product and campaign information to users.

[0447] To implement this invention, a server, a terminal, and a sensor device for detecting user movement are used. The sensor device is placed within the store and monitors the user's location and movement in real time. The server receives data acquired from the sensor and analyzes the user's movement pattern using a detection means for detecting movement. Based on this analysis, the server performs processing to infer the user's purchasing intent.

[0448] Based on the analyzed data, the server utilizes response mechanisms to provide product information. Specifically, it generates personalized product and campaign information by combining products the user showed interest in during their browsing history with their past purchase history, and sends this information to the terminal. Terminals include smartphones and smart glasses, and these devices provide information to the user through voice and visual displays.

[0449] For example, if the server infers that a user is looking for clothing from a specific brand, it will quickly provide inventory and promotional information for products related to that brand. Also, if a user voice-inputs a question such as "When is this item on sale?" through their device, speech recognition software will convert the speech into text, and the server will retrieve that information from its database and respond. Existing technologies such as Google Speech-to-Text will be used for speech recognition.

[0450] The generated information can be used to improve the shopping experience by allowing users to quickly access the information they want. For example, by giving the AI ​​model instructions such as, "When a user asks for details about a specific product in the store, please provide relevant information. For example, if a user wants to know if a product is on sale or what color variations are available, please provide appropriate information in both voice and text," the effectiveness of the response method can be increased.

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] As a user moves within the store, sensor devices acquire their location data in real time. This acquired location data is sent to a server as input to create a map of the user's movement path. The server analyzes the user's current location and movement pattern based on this movement path data.

[0454] Step 2:

[0455] The server infers the user's purchasing intent from their movement data. This inference utilizes past purchase history data and location information of products the user has shown interest in within the store. The server processes this input data and generates output information about products that the user is likely to be interested in.

[0456] Step 3:

[0457] A user asks a question by voice, such as, "What is the sale period for this product?" This voice is captured by the device, and speech recognition software converts it into text data. This text data is sent to a server and used as input for analysis. The server queries a database based on this text data and outputs relevant product information and sale information.

[0458] Step 4:

[0459] Product information output from the server is transmitted to the terminal. The terminal then provides the user with answers via voice or visual display. As a result, users can smoothly obtain detailed product information and campaign information within the store.

[0460] In this way, a system is realized in which servers, terminals, and sensor devices work together to improve the user's shopping experience.

[0461] 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.

[0462] This invention is a system for understanding a user's behavior and emotions in real time within the home and providing appropriate advice. This system is composed of a combination of sensor means, voice processing means, emotion engine, and learning means.

[0463] The terminal uses multiple sensor devices to acquire information about the user's movements and environment. This includes motion sensors and temperature / humidity sensors, and based on this information, it understands the user's current situation. The terminal also receives voice input from the user and converts the voice into text data using voice processing equipment.

[0464] Next, the server analyzes the received text data to infer the user's intent. Natural language processing techniques are used for this analysis. Then, the emotion engine analyzes the user's voice and facial expression data to determine their emotional state, such as joy, anger, sadness, or happiness. This makes it possible to provide advice that takes into account not only the user's actions but also their emotions.

[0465] For example, if a user says "Can I rest now?" in a tired voice, the server will determine that rest is necessary based on the voice-to-text analysis and sentiment analysis results. The server will then provide suggestions for relaxation and advice considering the priorities of the next day's schedule from the device.

[0466] Ultimately, the server learns by combining emotional data with past behavioral history, using a feedback loop to provide more precise and personalized support to the user again. This enables an efficient and less stressful life tailored to the user's preferences. By using a variety of devices and technologies in this way, it is possible to implement a form of life support that takes the user's emotions into consideration.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The device uses sensor devices placed within the home to collect data on the user's movements and the surrounding environment. This includes motion detection using motion sensors and facial expression capture using cameras. The data is processed in real time.

[0470] Step 2:

[0471] The terminal receives the user's voice input via a microphone, and a voice processing device converts the voice into text data. This text data is then sent to a server for later analysis.

[0472] Step 3:

[0473] The server analyzes the speech-to-text data and performs natural language processing to understand the user's questions and intentions. This clarifies what the user is asking for.

[0474] Step 4:

[0475] To recognize emotions from the user's voice tone and facial expressions, the server uses an emotion engine. It determines the user's current emotions from the volume of their voice and image data, and stores this information in a database.

[0476] Step 5:

[0477] The server integrates analyzed text data, sentiment data, and past behavioral history data to generate optimal advice for the user. In this process, a learning algorithm is used to reflect the user's past emotional responses and behavioral patterns.

[0478] Step 6:

[0479] The generated advice is sent from the server to the terminal. The terminal then delivers this advice to the user via voice output or text display. For example, if the user is feeling stressed, it might suggest relaxing activities.

[0480] Step 7:

[0481] Users take action and provide feedback based on the advice they receive. They can also determine whether the advice was effective and request more detailed assistance through additional utterances.

[0482] Step 8:

[0483] The server updates the system's database based on user feedback and actual behavioral results, improving the accuracy of future recommendations. In this way, the system continuously evolves, more effectively enhancing support for users.

[0484] (Example 2)

[0485] 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."

[0486] In modern home environments, users face a variety of tasks and stresses, but they need appropriate advice based on their individual behavior and emotions. Conventional technologies struggle to effectively analyze and respond to user behavior and environmental information, and there is a particular challenge in providing support that addresses emotions.

[0487] 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.

[0488] In this invention, the server includes a device that detects and acquires data on the user's behavior and environmental information, a processing device that analyzes the data and voice input to infer the user's intentions and emotional state, and a response device that provides personalized advice based on the user's intentions and emotional state. This enables precise and personalized support based on the user's behavior and emotions.

[0489] A "device that detects behavioral and environmental information and acquires data" is a device that includes sensor technology to sense a user's physical movements and multiple environmental factors, and record that information as digital data.

[0490] A "processing device that analyzes data and voice input to infer the user's intentions and emotional state" is a device that analyzes acquired data and voice information and performs calculations to understand the user's purpose and feelings.

[0491] A "personalized advice-providing response device" is a device that generates support and suggestions tailored to the user's specific situation and psychological state, and then communicates them to the user.

[0492] A "learning device that accumulates historical data and improves accuracy" is a device that uses records of past interactions to improve the system's decision-making ability, enabling more accurate and effective functionality in future use.

[0493] An "analysis device that converts audio data into text and determines emotional state" is a device equipped with the function of converting received audio data into textual information and using it to identify the user's emotional state.

[0494] A "generation device that converts and provides individualized advice using a generation algorithm" is a device that incorporates technology to create personalized advice for each user based on analysis results using a mechanical method, and then present that advice to the user.

[0495] This invention is a system for understanding a user's behavior and emotions within the home and providing personalized advice. The system consists of a combination of a sensor device, an audio processing device, a processing device, a response device, and a learning device.

[0496] The device is equipped with multiple sensors, including motion sensors and environmental sensors, to acquire information about the user's behavior and environment. Based on this information, it understands the user's current situation. It also receives voice input and converts the voice into text data using voice processing software (e.g., speech recognition API).

[0497] The server analyzes text data received from the terminal and determines the user's intent and emotional state through natural language processing techniques (e.g., language analysis libraries). This analysis identifies emotions such as joy, anger, sadness, and happiness, and understands the user's intent. For example, if a user uses an expression like "I want to know how to relax," the server analyzes that intent and integrates the results of the emotional analysis performed by the emotion engine.

[0498] Based on the analysis results, the server uses a generation algorithm (e.g., a generation AI model) to create personalized advice best suited to the user. This advice may include relaxation techniques or advice on prioritizing daily life.

[0499] The response device provides the user with generated advice via voice or display. The user can then act based on this response, and the system uses this response and action again for learning. The server uses a feedback loop to accumulate recorded behavioral history and sentiment information, improving the overall accuracy of the system.

[0500] As a concrete example, here is an example of a prompt statement for a generative AI model: "Please give specific advice if the user says they want to relax." Using this prompt statement, the model will generate advice tailored to the user.

[0501] As described above, this system provides personalized support tailored to each user's individual circumstances, promoting the realization of an efficient and fulfilling life.

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The device collects information on the user's physical movements and the indoor environment through motion sensors and environmental sensors. Inputs include the user's location and movements, as well as environmental information such as room temperature and humidity. The collected data is transmitted to a server in digital format as output. This process allows the user's current activity and surrounding conditions to be recorded in real time as digital data.

[0505] Step 2:

[0506] The terminal receives the user's voice using a microphone. The input is the user's voice data. This data is converted into text data by a speech processing device. The output is the converted text data, which is sent to the server. Specifically, a process is performed to convert speech into text information using speech recognition technology.

[0507] Step 3:

[0508] The server receives text data sent from the terminal and analyzes its content using natural language processing technology. The input is text data converted from speech. This analysis involves data processing to extract the user's intent and requests. The output is information about the user's purpose and questions. To clarify the user's intent from the analysis results, a machine learning algorithm that understands the context is applied.

[0509] Step 4:

[0510] The server analyzes received emotional data in addition to text data using an emotion engine. The input is text data containing metadata such as the user's voice tone and pace. This process involves data calculations to identify emotional states such as joy, anger, sadness, and happiness. The output is a determination of the user's emotional state. At this stage, the user's psychological state is evaluated based on the tone and speed of their voice.

[0511] Step 5:

[0512] The server uses a generative AI model based on the obtained intentions and emotional state to generate the best advice to provide to the user. The input is the analysis results regarding the user's intentions and emotional state. The output is a specific advice message for the user. The generative AI model uses this information to create personalized advice. For example, if the user inputs "I want to rest a little," it will suggest ways to relax based on that.

[0513] Step 6:

[0514] The terminal communicates the generated advice to the user. The input is the advice generated by the server. The output is the specific suggestions the user receives visually or audibly. At this stage, speech synthesis technology can be used to provide voice feedback, and it can also be displayed as text via the display.

[0515] Step 7:

[0516] The server accumulates user interaction and behavioral history and performs feedback learning to improve the quality of future responses. The input is past response history and behavioral data. The output is an updated learning model that continuously improves system response accuracy. This learning process adjusts future user interactions to be more effective and precise.

[0517] (Application Example 2)

[0518] 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."

[0519] In today's commercial environment, accurately meeting the diverse needs of customers remains a major challenge. To address this, a system is needed that can understand customer behavior and emotions in real time and provide more personalized sales support. However, current technology struggles to accurately analyze customer emotions and provide immediate advice based on that analysis. Therefore, there is a need for a system that enables appropriate approaches tailored to each individual customer.

[0520] 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.

[0521] In this invention, the server includes sensor means for detecting user behavior and acquiring data, processing means for inferring the user's intentions based on the analysis of the data, emotion analysis means for identifying and analyzing the user's emotions, and response means for providing sales support in a commercial environment. This enables sales promotion that takes into account the diverse emotions of customers.

[0522] A "sensor device" is a device used to detect user behavior and acquire data.

[0523] "Processing means" refers to a device or system that has the function of analyzing data acquired from sensor means and inferring the user's intentions.

[0524] "Emotional analysis means" refers to technology for identifying and analyzing data collected from users' emotions.

[0525] A "response means" is a device or system that has the function of providing appropriate sales support to users in a commercial environment based on the analysis results.

[0526] The system implementing this invention is designed to understand customer behavior and emotions in a commercial environment in real time and provide appropriate sales support advice. The server uses motion sensors and cameras to collect data necessary for emotion analysis in order to detect the movements of users (customers). This data is transmitted immediately in a cloud environment using services such as Google Cloud Platform and Microsoft Azure.

[0527] The server analyzes the acquired data using natural language processing and machine learning techniques. For example, it uses the Google Cloud Natural Language API to convert customer voice input into text data, which is then interpreted and sentiment analyzed. The analyzed results are displayed on the salesperson's smart device, such as smart glasses. This response method enables salespeople to provide more personalized service based on customer needs and emotions.

[0528] For example, if a customer looks hesitant while browsing a particular product, the server displays information on the salesperson's smart glasses indicating that "this customer may be unsure about making a purchase." Based on this information, the salesperson can provide more detailed explanations about the product, thereby increasing customer satisfaction.

[0529] An example of a prompt when using a generative AI model is, "Please provide information on how to approach a target product that will interest the customer." In this way, advanced customer service is realized by combining real-time data analysis with sales promotion.

[0530] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0531] Step 1:

[0532] The terminal uses in-store sensors to acquire customer motion data and environmental data. This includes capturing customer movements and facial expressions using motion sensors and cameras. Inputs are motion data and environmental data, while output is raw data containing these.

[0533] Step 2:

[0534] The device sends the acquired motion data to the server. If audio data is included, it is converted into text data using speech processing equipment. This conversion process utilizes the Google Cloud Natural Language API. The input is audio data, and the output is text data.

[0535] Step 3:

[0536] The server analyzes text data and facial expression data using emotion analysis tools to determine the customer's emotional state. For example, it uses the Microsoft Azure Face API to identify emotions such as joy, anger, sadness, and happiness from facial expressions. The input is text data and facial expression data, and the output is the emotional state as a result of the analysis.

[0537] Step 4:

[0538] The server generates appropriate advice commands based on the customer's emotional state. During this process, sales promotion prompts are sent to the AI ​​model, resulting in specific suggestions such as, "This customer may be hesitant to make a purchase." The input is the emotional state, and the output is the advice command.

[0539] Step 5:

[0540] The server sends the generated advice commands to the terminal, which then displays the information on the salesperson's smart glasses. This allows the salesperson to respond appropriately to the customer in real time. The input is the advice commands, and the output is visual information for the salesperson.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] [Fourth Embodiment]

[0545] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0546] 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.

[0547] 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).

[0548] 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.

[0549] 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.

[0550] 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).

[0551] 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.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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".

[0558] To implement this invention, it is necessary to appropriately place multiple sensor devices, wearable devices, and devices with voice recognition capabilities within the home. The sensor means record the user's physical movements and environmental conditions in real time. Based on this information, the process primarily involves inferring the user's actions and intentions.

[0559] Data is first collected by the terminal and transmitted wirelessly to the server. The server analyzes the received data using natural language processing and machine learning algorithms. For example, if a user says, "What did I come here for?", the server refers to relevant past behavioral data and infers that the next task, "hanging up the laundry," has not yet been performed. To communicate this to the user, the terminal provides advice via voice or text.

[0560] As a concrete example, consider a scenario where a user asks in the kitchen, "When is garbage day for this?" A sensor identifies the type of object, and a voice processing device converts the question into text. The server then consults a local database of garbage information and answers, "Friday." The terminal then forwards the answer to the user via its speaker, providing them with the information. Other specific applications include integration with devices such as smartphones and smart speakers, allowing for a user interface that visually displays the progress of household chores.

[0561] In this way, a concrete embodiment of the household co-pilot system is realized to support the user's daily life and improve efficiency.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] The device collects data in real time about the user's movements and environmental conditions from sensor devices placed throughout the home. For example, a motion sensor detects when the user stands up, and a temperature and humidity sensor records the room's conditions.

[0565] Step 2:

[0566] The device transmits the collected data to the server via wireless communication. Here, the data is typically encrypted for privacy reasons.

[0567] Step 3:

[0568] The server analyzes the received data and uses natural language processing algorithms to infer the user's intent behind their speech and actions. For example, it might refer to data converted from speech input to text to analyze the user's question, "What should I do next?"

[0569] Step 4:

[0570] The server generates recommended tasks based on the analysis results and the user's past activity history. Machine learning models are used to create advice that takes into account the user's habits and current context.

[0571] Step 5:

[0572] The server sends the generated advice (for example, "Please hang up the laundry") to the terminal. The terminal receives this message and communicates it to the user using voice or display.

[0573] Step 6:

[0574] Based on the advice provided, users will take the next steps. If necessary, they can ask additional questions to the device to seek further advice.

[0575] Step 7:

[0576] Based on user feedback and the actions taken, the server updates the database and improves the accuracy of the model. This feedback loop allows the system to continuously improve.

[0577] (Example 1)

[0578] 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".

[0579] In modern households, users seek greater efficiency in their daily lives, yet find it difficult to manage multiple household chores and tasks simultaneously. Furthermore, despite the increasing use of voice-activated assistance, systems that accurately predict and support individual tasks and actions remain limited. There is a need to improve this situation and provide more effective support for users' time management and task completion.

[0580] 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.

[0581] In this invention, the server includes detection means for detecting and acquiring information on the user's actions and environmental conditions, transmission means for transmitting the information wirelessly, and analysis means for analyzing the received information to infer the user's intentions and recommend the next action based on the behavioral history. As a result, the user can benefit from a system that supports complex task management and efficiently complete tasks in their daily life.

[0582] A "user" is a person who operates this system and receives support for managing their daily tasks.

[0583] "Action" refers to the physical movements and tasks performed by the user, which are detected by sensor devices.

[0584] "Environmental conditions" refer to physical conditions related to the situation inside a home, such as indoor temperature and brightness.

[0585] "Detection means" refers to devices or systems used to sense the user's actions and environmental conditions in real time.

[0586] "Information" refers to all data related to the user's actions and environmental conditions obtained through detection means.

[0587] "Wireless communication" is a communication technology that uses radio waves to send and receive data, and is a means of transmitting information between servers.

[0588] "Transmission means" refers to the technology and equipment used to wirelessly transmit collected information to a server.

[0589] "Receiving" refers to the act or process by which a server receives information sent via a transmission method.

[0590] "Analysis" refers to the act of processing data to infer the user's intentions and next actions based on the information received.

[0591] "Intention" refers to the purpose or desired outcome that a user has for their actions.

[0592] "Behavioral history" refers to records based on a user's actions and environmental conditions in the past.

[0593] "Recommendation" refers to a suggestion of actions or tasks that are considered optimal for the user.

[0594] "Analysis means" refers to technologies and devices that interpret received information to clarify the user's intentions and suggest the next course of action.

[0595] "Notification means" refers to technologies or devices used to convey the analyzed results to the user in the form of audio or text.

[0596] This system is designed to support daily life more efficiently and works in conjunction with various devices. The terminal acquires real-time data on the user's movements and environmental conditions from multiple sensor devices and wearable devices placed throughout the home. This includes, for example, heart rate and step count data provided by a smartwatch, and indoor temperature data measured by a temperature sensor. This data is collected by the terminal using Bluetooth or Wi-Fi.

[0597] Next, the device transmits the collected data to the server using wireless communication technology (e.g., HTTPS). This communication is encrypted to ensure data security and protect user privacy.

[0598] The server's role is to analyze the received data. Using programming languages ​​such as Python and R, it applies machine learning and natural language processing to the received data to infer the user's intent. For example, it uses natural language processing techniques to convert the user's voice input into text and read the intent of their actions from that text. A concrete use case would be to analyze the voice-inputted question, "What did I come here for?", and suggest the next task.

[0599] The analyzed results are notified to the user via the device for convenience. Using speech synthesis technology (e.g., voice assistant technology), the analysis results can be announced verbally through the speaker or displayed as text on the screen. Furthermore, by integrating with smart devices equipped with a user interface, visual information presentation is also possible.

[0600] For example, when a user asks, "When is garbage day?", the server checks the local garbage collection schedule and answers, "It's Friday," thus resolving the user's question. Furthermore, by using a prompt message such as, "Based on the question the user asked in the kitchen, determine the next suggested action," the generative AI model can be made to suggest more appropriate tasks.

[0601] This system supports smooth task management within the home and provides the ability to streamline users' daily lives.

[0602] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0603] Step 1:

[0604] The terminal collects data from sensor devices placed within the home. The input in this step is physical data obtained from the sensor devices (e.g., heart rate, temperature, brightness), and this data is processed by being temporarily stored in the terminal before being sent to the cloud. In operation, the terminal collects information from each sensor device via Bluetooth or Wi-Fi and organizes it.

[0605] Step 2:

[0606] The terminal transmits the collected data to the server using wireless communication technology. The input in this step is the sensor data stored in the terminal in step 1, and the output is that this data is delivered to the server via a secure protocol (e.g., HTTPS). Specifically, the terminal periodically establishes a connection with the server and sends encrypted data packets.

[0607] Step 3:

[0608] The server analyzes the received data. The input is the received sensor data, and the output is the result of inferring the user's intentions and the next recommended action. Data processing here involves analyzing the data using natural language processing and machine learning algorithms. Specifically, the server processes the data using Python libraries and uses a generative AI model to recommend actions.

[0609] Step 4:

[0610] The server sends the analysis results to the terminal in voice or text format. The input in this step is the analysis results generated in step 3, and the output is prepared notification information to be provided to the user. Specifically, the server formats the results obtained by the generated AI model appropriately and sends them to the terminal again using wireless communication.

[0611] Step 5:

[0612] The terminal notifies the user of the received information using speech synthesis technology or display. In this step, the input is the notification information sent from the server, and the output is the voice message or text information received by the user. Specifically, the terminal activates its speech synthesis engine to read the information aloud through the speaker or displays it as text on the screen.

[0613] (Application Example 1)

[0614] 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".

[0615] A challenge exists in that it is difficult for customers to quickly access product and promotional information they need while shopping in stores. This is especially true in large stores or stores handling multiple product categories, where it is difficult for customers to find the products they are looking for and their detailed information. Existing systems require customers to ask store staff, which does not provide an efficient shopping experience.

[0616] 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.

[0617] In this invention, the server includes detection means for detecting movement patterns, processing means for inferring purchase intent, and response means for providing product information. This allows customers to instantly obtain product information and campaign information within the store.

[0618] "Detection methods for detecting movement patterns" refer to technologies for understanding the location and direction of movement of users in real time, and include devices and algorithms that accurately track the movements and actions of users within a store.

[0619] The "processing means for inferring purchasing intent" is an analytical function that uses user movement data obtained from detection means to infer the products that users are looking for and the categories they are interested in.

[0620] A "means of providing product information" refers to a system that provides information to users through voice, text, or visual displays, in accordance with the user's purchasing intent, and is a method for conveying relevant product and campaign information to users.

[0621] To implement this invention, a server, a terminal, and a sensor device for detecting user movement are used. The sensor device is placed within the store and monitors the user's location and movement in real time. The server receives data acquired from the sensor and analyzes the user's movement pattern using a detection means for detecting movement. Based on this analysis, the server performs processing to infer the user's purchasing intent.

[0622] Based on the analyzed data, the server utilizes response mechanisms to provide product information. Specifically, it generates personalized product and campaign information by combining products the user showed interest in during their browsing history with their past purchase history, and sends this information to the terminal. Terminals include smartphones and smart glasses, and these devices provide information to the user through voice and visual displays.

[0623] For example, if the server infers that a user is looking for clothing from a specific brand, it will quickly provide inventory and promotional information for products related to that brand. Also, if a user voice-inputs a question such as "When is this item on sale?" through their device, speech recognition software will convert the speech into text, and the server will retrieve that information from its database and respond. Existing technologies such as Google Speech-to-Text will be used for speech recognition.

[0624] The generated information can be used to improve the shopping experience by allowing users to quickly access the information they want. For example, by giving the AI ​​model instructions such as, "When a user asks for details about a specific product in the store, please provide relevant information. For example, if a user wants to know if a product is on sale or what color variations are available, please provide appropriate information in both voice and text," the effectiveness of the response method can be increased.

[0625] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0626] Step 1:

[0627] As a user moves within the store, sensor devices acquire their location data in real time. This acquired location data is sent to a server as input to create a map of the user's movement path. The server analyzes the user's current location and movement pattern based on this movement path data.

[0628] Step 2:

[0629] The server infers the user's purchasing intent from their movement data. This inference utilizes past purchase history data and location information of products the user has shown interest in within the store. The server processes this input data and generates output information about products that the user is likely to be interested in.

[0630] Step 3:

[0631] A user asks a question by voice, such as, "What is the sale period for this product?" This voice is captured by the device, and speech recognition software converts it into text data. This text data is sent to a server and used as input for analysis. The server queries a database based on this text data and outputs relevant product information and sale information.

[0632] Step 4:

[0633] Product information output from the server is transmitted to the terminal. The terminal then provides the user with answers via voice or visual display. As a result, users can smoothly obtain detailed product information and campaign information within the store.

[0634] In this way, a system is realized in which servers, terminals, and sensor devices work together to improve the user's shopping experience.

[0635] 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.

[0636] This invention is a system for understanding a user's behavior and emotions in real time within the home and providing appropriate advice. This system is composed of a combination of sensor means, voice processing means, emotion engine, and learning means.

[0637] The terminal uses multiple sensor devices to acquire information about the user's movements and environment. This includes motion sensors and temperature / humidity sensors, and based on this information, it understands the user's current situation. The terminal also receives voice input from the user and converts the voice into text data using voice processing equipment.

[0638] Next, the server analyzes the received text data to infer the user's intent. Natural language processing techniques are used for this analysis. Then, the emotion engine analyzes the user's voice and facial expression data to determine their emotional state, such as joy, anger, sadness, or happiness. This makes it possible to provide advice that takes into account not only the user's actions but also their emotions.

[0639] For example, if a user says "Can I rest now?" in a tired voice, the server will determine that rest is necessary based on the voice-to-text analysis and sentiment analysis results. The server will then provide suggestions for relaxation and advice considering the priorities of the next day's schedule from the device.

[0640] Ultimately, the server learns by combining emotional data with past behavioral history, using a feedback loop to provide more precise and personalized support to the user again. This enables an efficient and less stressful life tailored to the user's preferences. By using a variety of devices and technologies in this way, it is possible to implement a form of life support that takes the user's emotions into consideration.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The device uses sensor devices placed within the home to collect data on the user's movements and the surrounding environment. This includes motion detection using motion sensors and facial expression capture using cameras. The data is processed in real time.

[0644] Step 2:

[0645] The terminal receives the user's voice input via a microphone, and a voice processing device converts the voice into text data. This text data is then sent to a server for later analysis.

[0646] Step 3:

[0647] The server analyzes the speech-to-text data and performs natural language processing to understand the user's questions and intentions. This clarifies what the user is asking for.

[0648] Step 4:

[0649] To recognize emotions from the user's voice tone and facial expressions, the server uses an emotion engine. It determines the user's current emotions from the volume of their voice and image data, and stores this information in a database.

[0650] Step 5:

[0651] The server integrates analyzed text data, sentiment data, and past behavioral history data to generate optimal advice for the user. In this process, a learning algorithm is used to reflect the user's past emotional responses and behavioral patterns.

[0652] Step 6:

[0653] The generated advice is sent from the server to the terminal. The terminal then delivers this advice to the user via voice output or text display. For example, if the user is feeling stressed, it might suggest relaxing activities.

[0654] Step 7:

[0655] Users take action and provide feedback based on the advice they receive. They can also determine whether the advice was effective and request more detailed assistance through additional utterances.

[0656] Step 8:

[0657] The server updates the system's database based on user feedback and actual behavioral results, improving the accuracy of future recommendations. In this way, the system continuously evolves, more effectively enhancing support for users.

[0658] (Example 2)

[0659] 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".

[0660] In modern home environments, users face a variety of tasks and stresses, but they need appropriate advice based on their individual behavior and emotions. Conventional technologies struggle to effectively analyze and respond to user behavior and environmental information, and there is a particular challenge in providing support that addresses emotions.

[0661] 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.

[0662] In this invention, the server includes a device that detects and acquires data on the user's behavior and environmental information, a processing device that analyzes the data and voice input to infer the user's intentions and emotional state, and a response device that provides personalized advice based on the user's intentions and emotional state. This enables precise and personalized support based on the user's behavior and emotions.

[0663] A "device that detects behavioral and environmental information and acquires data" is a device that includes sensor technology to sense a user's physical movements and multiple environmental factors, and record that information as digital data.

[0664] A "processing device that analyzes data and voice input to infer the user's intentions and emotional state" is a device that analyzes acquired data and voice information and performs calculations to understand the user's purpose and feelings.

[0665] A "personalized advice-providing response device" is a device that generates support and suggestions tailored to the user's specific situation and psychological state, and then communicates them to the user.

[0666] A "learning device that accumulates historical data and improves accuracy" is a device that uses records of past interactions to improve the system's decision-making ability, enabling more accurate and effective functionality in future use.

[0667] An "analysis device that converts audio data into text and determines emotional state" is a device equipped with the function of converting received audio data into textual information and using it to identify the user's emotional state.

[0668] A "generation device that converts and provides individualized advice using a generation algorithm" is a device that incorporates technology to create personalized advice for each user based on analysis results using a mechanical method, and then present that advice to the user.

[0669] This invention is a system for understanding a user's behavior and emotions within the home and providing personalized advice. The system consists of a combination of a sensor device, an audio processing device, a processing device, a response device, and a learning device.

[0670] The device is equipped with multiple sensors, including motion sensors and environmental sensors, to acquire information about the user's behavior and environment. Based on this information, it understands the user's current situation. It also receives voice input and converts the voice into text data using voice processing software (e.g., speech recognition API).

[0671] The server analyzes text data received from the terminal and determines the user's intent and emotional state through natural language processing techniques (e.g., language analysis libraries). This analysis identifies emotions such as joy, anger, sadness, and happiness, and understands the user's intent. For example, if a user uses an expression like "I want to know how to relax," the server analyzes that intent and integrates the results of the emotional analysis performed by the emotion engine.

[0672] Based on the analysis results, the server uses a generation algorithm (e.g., a generation AI model) to create personalized advice best suited to the user. This advice may include relaxation techniques or advice on prioritizing daily life.

[0673] The response device provides the user with generated advice via voice or display. The user can then act based on this response, and the system uses this response and action again for learning. The server uses a feedback loop to accumulate recorded behavioral history and sentiment information, improving the overall accuracy of the system.

[0674] As a concrete example, here is an example of a prompt statement for a generative AI model: "Please give specific advice if the user says they want to relax." Using this prompt statement, the model will generate advice tailored to the user.

[0675] As described above, this system provides personalized support tailored to each user's individual circumstances, promoting the realization of an efficient and fulfilling life.

[0676] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0677] Step 1:

[0678] The device collects information on the user's physical movements and the indoor environment through motion sensors and environmental sensors. Inputs include the user's location and movements, as well as environmental information such as room temperature and humidity. The collected data is transmitted to a server in digital format as output. This process allows the user's current activity and surrounding conditions to be recorded in real time as digital data.

[0679] Step 2:

[0680] The terminal receives the user's voice using a microphone. The input is the user's voice data. This data is converted into text data by a speech processing device. The output is the converted text data, which is sent to the server. Specifically, a process is performed to convert speech into text information using speech recognition technology.

[0681] Step 3:

[0682] The server receives text data sent from the terminal and analyzes its content using natural language processing technology. The input is text data converted from speech. This analysis involves data processing to extract the user's intent and requests. The output is information about the user's purpose and questions. To clarify the user's intent from the analysis results, a machine learning algorithm that understands the context is applied.

[0683] Step 4:

[0684] The server analyzes received emotional data in addition to text data using an emotion engine. The input is text data containing metadata such as the user's voice tone and pace. This process involves data calculations to identify emotional states such as joy, anger, sadness, and happiness. The output is a determination of the user's emotional state. At this stage, the user's psychological state is evaluated based on the tone and speed of their voice.

[0685] Step 5:

[0686] The server uses a generative AI model based on the obtained intentions and emotional state to generate the best advice to provide to the user. The input is the analysis results regarding the user's intentions and emotional state. The output is a specific advice message for the user. The generative AI model uses this information to create personalized advice. For example, if the user inputs "I want to rest a little," it will suggest ways to relax based on that.

[0687] Step 6:

[0688] The terminal communicates the generated advice to the user. The input is the advice generated by the server. The output is the specific suggestions the user receives visually or audibly. At this stage, speech synthesis technology can be used to provide voice feedback, and it can also be displayed as text via the display.

[0689] Step 7:

[0690] The server accumulates user interaction and behavioral history and performs feedback learning to improve the quality of future responses. The input is past response history and behavioral data. The output is an updated learning model that continuously improves system response accuracy. This learning process adjusts future user interactions to be more effective and precise.

[0691] (Application Example 2)

[0692] 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".

[0693] In today's commercial environment, accurately meeting the diverse needs of customers remains a major challenge. To address this, a system is needed that can understand customer behavior and emotions in real time and provide more personalized sales support. However, current technology struggles to accurately analyze customer emotions and provide immediate advice based on that analysis. Therefore, there is a need for a system that enables appropriate approaches tailored to each individual customer.

[0694] 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.

[0695] In this invention, the server includes sensor means for detecting user behavior and acquiring data, processing means for inferring the user's intentions based on the analysis of the data, emotion analysis means for identifying and analyzing the user's emotions, and response means for providing sales support in a commercial environment. This enables sales promotion that takes into account the diverse emotions of customers.

[0696] A "sensor device" is a device used to detect user behavior and acquire data.

[0697] "Processing means" refers to a device or system that has the function of analyzing data acquired from sensor means and inferring the user's intentions.

[0698] "Emotional analysis means" refers to technology for identifying and analyzing data collected from users' emotions.

[0699] A "response means" is a device or system that has the function of providing appropriate sales support to users in a commercial environment based on the analysis results.

[0700] The system implementing this invention is designed to understand customer behavior and emotions in a commercial environment in real time and provide appropriate sales support advice. The server uses motion sensors and cameras to collect data necessary for emotion analysis in order to detect the movements of users (customers). This data is transmitted immediately in a cloud environment using services such as Google Cloud Platform and Microsoft Azure.

[0701] The server analyzes the acquired data using natural language processing and machine learning techniques. For example, it uses the Google Cloud Natural Language API to convert customer voice input into text data, which is then interpreted and sentiment analyzed. The analyzed results are displayed on the salesperson's smart device, such as smart glasses. This response method enables salespeople to provide more personalized service based on customer needs and emotions.

[0702] For example, if a customer looks hesitant while browsing a particular product, the server displays information on the salesperson's smart glasses indicating that "this customer may be unsure about making a purchase." Based on this information, the salesperson can provide more detailed explanations about the product, thereby increasing customer satisfaction.

[0703] An example of a prompt when using a generative AI model is, "Please provide information on how to approach a target product that will interest the customer." In this way, advanced customer service is realized by combining real-time data analysis with sales promotion.

[0704] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0705] Step 1:

[0706] The terminal uses in-store sensors to acquire customer motion data and environmental data. This includes capturing customer movements and facial expressions using motion sensors and cameras. Inputs are motion data and environmental data, while output is raw data containing these.

[0707] Step 2:

[0708] The device sends the acquired motion data to the server. If audio data is included, it is converted into text data using speech processing equipment. This conversion process utilizes the Google Cloud Natural Language API. The input is audio data, and the output is text data.

[0709] Step 3:

[0710] The server analyzes text data and facial expression data using emotion analysis tools to determine the customer's emotional state. For example, it uses the Microsoft Azure Face API to identify emotions such as joy, anger, sadness, and happiness from facial expressions. The input is text data and facial expression data, and the output is the emotional state as a result of the analysis.

[0711] Step 4:

[0712] The server generates appropriate advice commands based on the customer's emotional state. During this process, sales promotion prompts are sent to the AI ​​model, resulting in specific suggestions such as, "This customer may be hesitant to make a purchase." The input is the emotional state, and the output is the advice command.

[0713] Step 5:

[0714] The server sends the generated advice commands to the terminal, which then displays the information on the salesperson's smart glasses. This allows the salesperson to respond appropriately to the customer in real time. The input is the advice commands, and the output is visual information for the salesperson.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] 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."

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] The following is further disclosed regarding the embodiments described above.

[0737] (Claim 1)

[0738] A sensor means that detects user behavior and acquires data,

[0739] A processing means for inferring the user's intent based on the analysis of the aforementioned data,

[0740] A means of providing advice to users,

[0741] A household task support system that includes this.

[0742] (Claim 2)

[0743] The home task support system according to claim 1, further comprising a voice processing means for converting voice input into text data and analyzing the text data.

[0744] (Claim 3)

[0745] The home task support system according to claim 1, further comprising a learning means for analyzing data from the sensor means in real time and generating advice in combination with the user's past behavior history.

[0746] "Example 1"

[0747] (Claim 1)

[0748] A detection means that detects the user's actions and environmental conditions and acquires information,

[0749] A transmitting means for transmitting the aforementioned information by wireless communication,

[0750] An analytical means that analyzes received information to infer the user's intentions and recommends the next action based on the behavioral history,

[0751] A notification means that provides advice to the user via voice or text based on the results of the aforementioned analysis,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, further comprising a conversion means for converting voice input into text data, and a voice processing means for analyzing the text data and generating an appropriate response.

[0755] (Claim 3)

[0756] The system according to claim 1, further comprising a learning means that instantly analyzes information from the detection means and forms advice by comparing it with the behavioral history that forms the basis of the generated recommendation.

[0757] "Application Example 1"

[0758] (Claim 1)

[0759] A detection means that detects the movement of users and acquires data,

[0760] A processing means for inferring the user's purchasing intent based on the analysis of the aforementioned data,

[0761] A response mechanism that provides personalized product information to users,

[0762] A support system that includes this.

[0763] (Claim 2)

[0764] The system according to claim 1, further comprising a speech processing means for converting speech input into text data and analyzing the text data.

[0765] (Claim 3)

[0766] The system according to claim 1, further comprising a learning means for analyzing data from the detection means in real time and generating information by combining it with the user's purchasing behavior history.

[0767] "Example 2 of combining an emotion engine"

[0768] (Claim 1)

[0769] A device that detects user behavior and environmental information and acquires data,

[0770] A processing device that analyzes the aforementioned data and voice input to infer the user's intentions and emotional state,

[0771] A response device that provides personalized advice based on the user's intentions and emotional state,

[0772] A learning device that accumulates the user's history data and improves accuracy,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, further comprising an analysis device that converts audio data into text and determines the emotional state of the user.

[0776] (Claim 3)

[0777] The system according to claim 1, further comprising a generating device that converts the results of data analysis and emotion determination into individualized advice using a generating algorithm and provides it.

[0778] "Application example 2 when combining with an emotional engine"

[0779] (Claim 1)

[0780] A sensor means that detects user behavior and acquires data,

[0781] A processing means for inferring the user's intent based on the analysis of the aforementioned data,

[0782] A means of emotion analysis that identifies and analyzes the emotions of users,

[0783] A response means for providing sales support in a commercial environment based on the aforementioned analysis results,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, further comprising a speech processing means for converting speech input into text data and analyzing the text data.

[0787] (Claim 3)

[0788] The system according to claim 1, further comprising a learning means for analyzing data from the sensor means in real time and combining it with the user's past behavioral history to perform personalized sales promotion. [Explanation of Symbols]

[0789] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor means that detects user behavior and acquires data, A processing means for inferring the user's intent based on the analysis of the aforementioned data, A means of providing advice to users, A household task support system that includes this.

2. The home task support system according to claim 1, further comprising a voice processing means for converting voice input into text data and analyzing the text data.

3. The home task support system according to claim 1, further comprising a learning means for analyzing data from the aforementioned sensor means in real time and generating advice in combination with the user's past behavioral history.

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