system

Abstract

We provide the system. [Solution] A terminal device for receiving ambiguous instructions from users, A language analysis means for analyzing the aforementioned ambiguous instructions and understanding their context, A command conversion means that converts the analysis results obtained from the language analysis means into specific action instructions, A visualization means for presenting the aforementioned specific action instructions to the user, A transportation guidance means that provides travel guidance corresponding to the means of transport, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern business environments and educational settings, miscommunications in communication and inefficiencies in operations caused by ambiguous and unclear instructions have become problems. Although users are required to accurately and quickly understand the meaning of instructions and take appropriate actions, there is a lack of effective tools to support this.

Means for Solving the Problems

[0005] This invention provides a system comprising an input device that receives ambiguous instructions from a user, and an analysis device that analyzes the instructions and understands their context. Furthermore, it provides a conversion device that converts the ambiguous instructions into specific instructions based on the analysis results, and a display device that presents the specific instructions to the user. This configuration makes it possible to quickly obtain specific action guidelines from ambiguous instructions, thereby improving communication efficiency and work productivity.

[0006] A "user" is an individual or group that operates the system, provides input information, and acts based on the information received.

[0007] "Vague instructions" are those that lack specificity, making them difficult to interpret, and are ambiguous or unclear.

[0008] An "input device" is a device or interface for users to input information, and typically enables text or voice input.

[0009] An "analysis device" is a device that receives ambiguous instructions, interprets their content using technologies such as natural language processing, and analyzes the context and intent behind them.

[0010] A "conversion device" is a device that takes the analysis results obtained by an analysis device and converts them into specific instructions.

[0011] A "display device" is a device or interface that visually presents concrete instructions or information to the user. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] This invention is a system that converts ambiguous instructions into specific actions, and is realized by coordinating various devices. An example thereof is shown below.

[0034] The user inputs vague instructions into the system using a terminal. The terminal, such as a smartphone or personal computer, provides an interface for sending the input data to the server. While text input is the primary method, voice input is also possible.

[0035] The server receives ambiguous instructions sent from the terminal and analyzes them using an analysis device. The analysis device uses natural language processing technology to understand the context and intent of the instructions, and as a result extracts important keywords and phrases. This process clarifies ambiguous parts, allowing for more specific guidance.

[0036] Next, the server sends the obtained analysis results to the conversion device. The conversion device converts the analysis results into specific instructions and information. At this stage, it is also possible to improve the accuracy of the conversion results by using data from internal databases and related external information sources.

[0037] The converted specific instructions are sent from the server to the terminal. The terminal's display visually presents the results in a user-friendly format, allowing the user to quickly decide on their next action.

[0038] As a concrete example, consider internal corporate communication. When a user inputs the instruction "Prepare the monthly report," the server analyzes this vague instruction and generates specific tasks such as "Collect past monthly data" or "Add new report items." These tasks are then displayed on the terminal, allowing the user to perform specific actions.

[0039] Thus, by implementing the present invention, it is possible to clarify ambiguous instructions and provide an environment in which users can act efficiently.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters vague instructions using the device. The device records the text and audio as digital data and prepares it for transmission to the server.

[0043] Step 2:

[0044] The terminal sends user instruction data to the server. The server receives this data and passes it on to the analysis device.

[0045] Step 3:

[0046] The server's analysis device analyzes the received instruction data. During this process, natural language processing techniques are used to extract key verbs and nouns from the text and understand the context.

[0047] Step 4:

[0048] The analysis device identifies the specific requirements for the instructions based on the analysis results and passes the results to the conversion device.

[0049] Step 5:

[0050] The server's conversion device translates analysis results into specific instructions and tasks. It improves the accuracy of the information by referencing internal databases and external information sources as needed.

[0051] Step 6:

[0052] The specific instructions that have been converted are sent from the server to the terminal. The terminal then presents the received content to the user.

[0053] Step 7:

[0054] The user checks the specific instructions displayed on the device and decides what action to take next. This allows the user to start responding immediately.

[0055] (Example 1)

[0056] 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."

[0057] In recent years, when users input instructions via terminals, there are many cases where their intentions are not accurately conveyed due to ambiguous expressions or ambiguous language. Such ambiguity can lead to problems that impair efficiency, as it prevents prompt responses to the specific actions the user intends to take. Furthermore, current systems lack sufficient additional information gathering and natural language processing to identify the user's intentions, resulting in cumbersome operation. Therefore, it is desirable to efficiently clarify ambiguous instructions, present information in an easy-to-understand manner for users, and provide an environment that allows them to take action quickly.

[0058] 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.

[0059] In this invention, the server includes a display terminal means for receiving ambiguous instructions from a user, a communication means for transmitting the ambiguous instructions in a format suitable for the processing target, and a statistical processing means for analyzing the ambiguous instructions and extracting key information. This makes it possible to convert ambiguous instructions into specific action commands and present them to the user quickly and accurately.

[0060] "Vague instructions from users" refer to expressions that do not clearly specify concrete actions or results, and are ambiguous and open to interpretation.

[0061] "Display terminal means" refers to a device used by users to input information or visually confirm results, and generally includes smartphones and personal computers.

[0062] "Communication methods" refer to technologies that use protocols and interfaces to transmit information from a source to a destination, and specifically include data transmission using the internet or local networks.

[0063] "Statistical processing methods" refer to processing methods that use statistical techniques to extract meaningful information from large amounts of data and perform data analysis and interpretation.

[0064] "Data conversion means" refers to a device or method that converts input data into a different format or structure and arranges it in a way that is suitable for a specific purpose.

[0065] "Means of information representation" refers to methods and technologies for presenting information in a format that is easy for users to understand, and includes displays using graphical user interfaces.

[0066] "Information gathering means" refers to the means of obtaining necessary information from external sources and preparing it in a format usable within the system.

[0067] "Statistical processing techniques for natural language" refer to technologies that analyze the language that people normally use using statistical approaches to understand its context and meaning.

[0068] This invention is a system that receives ambiguous instructions from a user and converts them into specific actions. This system includes a display terminal, a communication means, a statistical processing means, a data conversion means, and an information representation means.

[0069] Specifically, the user inputs ambiguous instructions into the system using a display terminal. This display terminal can be a smartphone or a personal computer. The terminal then transmits the input data to a server via a communication device. This communication can utilize the internet or a local network.

[0070] The server analyzes the received ambiguous instructions using statistical processing techniques. These techniques incorporate natural language processing technologies and machine learning models, with libraries such as TENSORFLOW® and PyTorch being used as specific examples. The server uses these techniques to understand the context of the instructions and extract important keywords.

[0071] Subsequently, the server uses data conversion tools to convert the analysis results into specific action commands. During this process, information can be collected from internal databases and external sources to improve the accuracy of the commands. SQL queries and REST APIs are used for information gathering.

[0072] The converted specific action commands are then transmitted again from the server to the display terminal. The information representation means presents the converted commands in a visually easy-to-understand format for the user. This allows the user to quickly proceed to the next action.

[0073] For example, if a user enters the instruction "Prepare for tomorrow's team meeting," the server analyzes this vague instruction and generates specific tasks such as "Check the attendee list" and "Prepare meeting materials." These generated tasks are then displayed on the device, allowing the user to immediately perform these actions.

[0074] An example of a prompt to the generative AI model is, "Please translate vague instructions into specific tasks." This invention efficiently concretizes vague instructions and helps users make quick decisions about what to do.

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

[0076] Step 1:

[0077] The user inputs ambiguous instructions using a display terminal. The primary input method is text, and direct input is possible using a keyboard or microphone. This input data becomes basic information used in subsequent processing.

[0078] Step 2:

[0079] The terminal transmits information entered by the user to the server via a communication method. Encryption technology (SSL / TLS) is applied to ensure that the data is transmitted securely over the internet. The output of this process becomes the initial input data for processing by the server.

[0080] Step 3:

[0081] The server analyzes the received ambiguous instructions using statistical processing techniques. Natural language processing techniques are used to analyze words and phrases in the input text and understand the context. Specifically, machine learning models (e.g., BERT or GPT) are used to extract important keywords. The output of this process is a list of keywords as a result of the analysis.

[0082] Step 4:

[0083] The server converts the analysis results into specific action commands using data conversion means. It generates specific tasks while acquiring necessary information from internal databases and external information sources. For example, it forms action commands such as "send an email" or "aggregate data." The output of this process is a clearly defined set of action commands.

[0084] Step 5:

[0085] The server transmits the converted command back to the display terminal via communication means. The terminal's information display means visually displays this specific action command, presenting it in a way that allows the user to understand the next action to take. This final output is a concrete task display in a format that the user can confirm.

[0086] (Application Example 1)

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

[0088] In modern times, users of autonomous vehicles face the challenge of converting vague instructions into clear, precise, and specific action instructions when setting their destinations. Furthermore, a lack of necessary information for efficient transportation guidance makes it difficult to determine the optimal mode of transport and route to one's destination. To address these challenges, a user-friendly and intuitive system is required.

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

[0090] In this invention, the server includes terminal means for receiving ambiguous instructions from a user, language analysis means for analyzing the ambiguous instructions and understanding their context, and command conversion means for converting the analysis results obtained by the language analysis means into specific action instructions. As a result, users can obtain the necessary location information accurately and specific action instructions, including optimal route guidance, simply by inputting ambiguous instructions.

[0091] "Terminal means" refers to electronic devices used to receive ambiguous instructions from users, such as smartphones and computers.

[0092] A "language analysis device" is a device that uses natural language processing technology to analyze ambiguous instructions received and understand their context.

[0093] A "command conversion means" is a device that converts the analysis results obtained by the language analysis means into specific action instructions.

[0094] A "visualization means" is a display device that presents the converted, specific action instructions to the user.

[0095] A "transportation guidance system" refers to a system or device that provides travel guidance corresponding to a means of transportation.

[0096] "Information gathering means" refers to devices or processes for acquiring additional location information in accordance with specific instructions.

[0097] The system that realizes this application is one that can translate vague instructions given by users into concrete actions. The server plays a central role in this system, first receiving vague instructions from the user via a terminal device. The terminal device is an electronic device that the user can directly input, such as a smartphone or a personal computer.

[0098] The server then analyzes the received instructions using language analysis tools. Here, natural language processing techniques are used to understand the context and intent of the instructions. The analyzed instructions are then converted into specific action instructions by command conversion tools. During this process, data from databases and related external information sources are utilized to improve the accuracy of the converted instructions.

[0099] Ultimately, these instructions are presented to the user in an easily understandable format through visualization means. The system also includes transportation guidance means to provide information related to means of transport, ensuring that users receive appropriate travel guidance.

[0100] As a concrete example, a user riding in an autonomous vehicle might use their smartphone to give instructions such as, "I want to go to a nice cafe nearby." Upon receiving this instruction, the server analyzes it based on context and creates a list of nearby cafes. The user's past preference data and current location information are also taken into account to suggest the optimal destination. The transportation guidance system is integrated with the autonomous vehicle's navigation system and also provides the most efficient route.

[0101] Prompt phrases such as "cafe," "lunch," "quiet," and "Wi-Fi" are considered, and this system can use a generative AI model to provide more appropriate and personalized guidance.

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

[0103] Step 1:

[0104] The user uses a device to input vague instructions. For example, they might input an instruction such as, "I want to go to a nice cafe nearby." The input data is collected as text or voice and sent to the server. At this stage, the input data arrives at the server as vague natural language data.

[0105] Step 2:

[0106] The server analyzes the received ambiguous instructions using language analysis tools. It receives ambiguous instructions as input, analyzes them using natural language processing techniques, and understands their context. Here, it analyzes the prompt sentence using a generative AI model and extracts important keywords such as "cafe" and "delicious." As a result, the analyzed intent is output.

[0107] Step 3:

[0108] The server uses a command conversion mechanism to convert the analysis results into specific action instructions. It receives the analysis results as input and identifies specific cafe candidates by referencing data from databases and external information sources. The candidate list obtained in this process is generated as output.

[0109] Step 4:

[0110] The server uses transportation guidance methods to provide the optimal route to the autonomous vehicle's navigation system. It receives specific action instructions as input and converts them into travel guidance corresponding to the means of transport. Estimated travel route and travel time information is output and guided to the autonomous vehicle.

[0111] Step 5:

[0112] The terminal uses visualization to present users with specific action instructions and directions. It receives specific action instructions and navigation information sent from the server as input and displays them visually. This allows users to confirm and select the optimal cafe option and how to get there.

[0113] 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.

[0114] This invention provides a system that offers more appropriate and personalized responses by taking into account the user's emotions when concretizing ambiguous instructions. An example thereof is shown below.

[0115] The user uses the terminal to input ambiguous instructions or questions. The terminal records or logs this input as digital data and prepares it for transmission to the server. Once the input is complete, the data is sent to the server.

[0116] The server passes the data received from the terminal to the analysis device. The analysis device uses natural language processing technology to analyze ambiguous instructions, understanding the context and clarifying the intent of the instructions. Based on the analysis results, key information is extracted, and specific instructions are formed.

[0117] A distinctive feature of this invention is the integration of an emotion engine into the server. The emotion engine analyzes the user's input data and the emotional expressions within it to understand the emotional state in which the user is issuing instructions. This emotional information is fed back into the analysis and conversion process, generating more specific instructions that are sensitive to the user's emotions.

[0118] Furthermore, specific instructions are generated by a conversion device, and an information acquisition device is activated to obtain additional information as needed. This additional information may be optimized based on the user's emotions.

[0119] For example, when a user enters "the project is behind schedule," if feelings of anxiety or stress are detected, the server will prioritize providing specific steps and support resources that can help resolve the problem.

[0120] The generated specific instructions are sent from the server to the terminal and displayed to the user. Here too, the tone and expression are adjusted according to the user's emotions, making it a more comfortable experience for the user.

[0121] By implementing this invention, flexible responses that take emotions into account can be obtained even in the face of ambiguous instructions, thereby improving the user experience.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user inputs vague instructions into the system using a terminal. The terminal records this input as text data and prepares it for transmission to the server.

[0125] Step 2:

[0126] The user's input data is sent from the terminal to the server. The server receives this data and immediately begins processing it.

[0127] Step 3:

[0128] The server passes the received data to the analysis device. The analysis device uses natural language processing technology to analyze ambiguous instructions and understand their intent. At this stage, the context of the instructions is clarified, and the necessary marquee information is extracted.

[0129] Step 4:

[0130] The server's emotion engine detects the user's emotions contained within the analyzed instructions. In this process, emotion analysis technology is used to analyze emotional expressions and tone from the user's input, and to infer their emotional state.

[0131] Step 5:

[0132] Based on the analysis results obtained from the analysis device and the emotional information from the emotion engine, the server's conversion device transforms the user's instructions into specific actions and information. The content and tone of the instructions are adjusted according to the emotional information.

[0133] Step 6:

[0134] The server sends the translated, specific instructions to the terminal. At this point, the terminal prepares to display the information in a way that takes the user's emotional state into account.

[0135] Step 7:

[0136] The device displays specific instructions to the user. Based on these instructions, the user can more easily decide on the appropriate course of action. This allows the user to proceed to the next step quickly and efficiently.

[0137] (Example 2)

[0138] 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".

[0139] Conventional systems for analyzing ambiguous instructions simply analyze and concretize instructions without considering the emotions of the user receiving them. This often leads to inappropriate responses that do not meet user expectations and a decrease in usability. Furthermore, providing information that ignores the user's emotional state can impair user satisfaction.

[0140] 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.

[0141] In this invention, the server includes input / output means, analysis means, conversion means, sentiment analysis means, and display means. This makes it possible to respond appropriately and specifically to ambiguous instructions while taking emotions into consideration, thereby realizing a more satisfying interaction for the user.

[0142] "Input / output means" refers to communication and interface devices for receiving ambiguous instructions from users and transmitting them to a server.

[0143] An "analysis tool" is a device that uses natural language processing technology to analyze ambiguous instructions received and clarify their context and intent.

[0144] A "conversion means" is a device that generates specific instructions based on the analysis results obtained by the analysis means.

[0145] An "emotional analysis tool" is a device that analyzes the emotional state contained in the user's instructions and reflects that information in specific instructions.

[0146] A "display means" is a device for presenting the generated specific instructions to the user.

[0147] This invention is a system that receives ambiguous instructions from users and converts them into specific instructions based on advanced analytical methods, including sentiment analysis. This enables the provision of personalized responses that take into account the user's emotional state.

[0148] The system primarily consists of terminals and servers. The terminals receive user input in either voice or text format. Voice input is converted to text using speech recognition technology. The terminals then transmit this digital data to the server.

[0149] The server processes the received data using an analysis device. The analysis device utilizes algorithms commonly used in natural language processing. For example, it uses BERT or similar models to understand the context of ambiguous instructions and clarify their intent. Based on the analysis results, a conversion device forms specific instructions.

[0150] The server incorporates an emotion analysis device that understands the user's emotional state from their input data. For example, it analyzes emotions such as "anxiety," and this information is reflected in specific instructions. This enables responses that are sensitive to the user's emotions, allowing for the presentation of appropriate solutions and suggestions.

[0151] As a concrete example, consider a scenario where a user inputs "I'm worried because the project is behind schedule" into the terminal. The emotion analysis device detects the emotion "worry" and feeds this information back into the analysis process. The conversion device uses this information to generate specific guidance, such as "Utilize resources to speed up the project and check the checklist."

[0152] Finally, the server sends the generated specific instructions to the terminal and displays them to the user. Here, the tone and expression are adjusted according to the user's emotions. For example, a message with a gentle tone such as, "Don't worry, please check out these resources," might be displayed.

[0153] An example of a prompt would be, "What kind of support do you need to alleviate my worries when my project is behind schedule?" In response to this prompt, the system can suggest specific measures, including emotional support.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The user uses the terminal to input ambiguous instructions or questions in voice or text format. The terminal uses speech recognition technology to convert voice input into text format. This converted digital data is then prepared for transmission to the server. The input is the user's natural language instructions, and the output is digital data in text format.

[0157] Step 2:

[0158] The terminal establishes a communication channel for sending digital data to the server and then sends that digital data to the server. The input is digital data in text format, and the output is the transmission of data to the server. Specifically, the terminal establishes a network connection and sends text data to the server according to a data transfer protocol.

[0159] Step 3:

[0160] The server passes the digital data received from the terminal to the analysis device. The analysis device uses a generative AI model to analyze the instructions using natural language processing technology. The analysis involves understanding the context and extracting important information. The input is the text data received by the server, and the output is the analyzed contextual information and important information. The analysis device performs natural language contextual analysis using a specific algorithm.

[0161] Step 4:

[0162] The server generates specific instructions using the analyzed contextual information. A conversion mechanism is used to transform the analysis results into clear action guidelines. A generative AI model is utilized in this process. The input is the analysis result, and the output is specific action guidelines. For example, it forms concrete steps in response to questions such as "What is the next step?"

[0163] Step 5:

[0164] A sentiment analysis system built into the server analyzes the emotional elements contained in the input data. This allows the system to understand the user's emotional state and reflect this information in the specific instructions it generates. The input is the user's text data, and the output is specific instructions that reflect their emotions. For example, if "anxiety" is detected, the system will generate instructions that provide a sense of reassurance.

[0165] Step 6:

[0166] The server sends specific instructions to the terminal, taking emotions into consideration. The terminal displays these instructions to the user, presenting the received data visually or audibly in an appropriate format. The input is specific instruction data from the server, and the output is the visual or audible presentation of instructions to the user. Specific actions on the terminal include displaying messages on the screen or playing audio through the speaker.

[0167] (Application Example 2)

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

[0169] Traditionally, system responses to ambiguous user instructions have been made without considering the user's emotional state, sometimes resulting in responses that don't align with the user's psychological condition. This raises concerns, especially in home robots, that users may experience stress. There is a need to achieve more personalized responses that take user emotions into account.

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

[0171] In this invention, the server includes data receiving means, data analysis means, data conversion means, emotion analysis means, and data display means. This enables specific and personalized responses to ambiguous instructions that reflect the user's emotional state.

[0172] A "data receiving means" is a device that has the function of receiving ambiguous instructions from a user.

[0173] A "data analysis device" is a device that has the function of analyzing ambiguous instructions received and understanding the context of those instructions.

[0174] A "data conversion means" is a device that has the function of converting analysis results into specific instructions.

[0175] An "emotional analysis device" is a device that has the function of analyzing the emotional state of a user.

[0176] A "data display means" is a device that has the function of adjusting and presenting instructions to the user based on their emotional state.

[0177] The system for realizing this invention will primarily be developed around a scenario in which a user uses a smart robot assistant in their home. The system will receive ambiguous instructions from the user via a program running on a terminal. A data receiving means can be used to collect the user's voice and text data. It is preferable to use low-power hardware such as a Raspberry Pi or Jetson Nano as the terminal.

[0178] The server uses the Google Cloud Speech-to-Text API to convert speech data into text as a data analysis tool. For natural language processing, it utilizes a BERT model based on TensorFlow to analyze the context of ambiguous instructions received. The analyzed data is then used for sentiment analysis, evaluating the user's emotional state using Python libraries such as TextBlob and NLTK.

[0179] The server generates specific instructions through data conversion means, taking into account the results of sentiment analysis. In this process, it utilizes generative AI models such as GPT-3(registered trademark).5 to create personalized responses tailored to the user's emotions. The generated instructions are transmitted to the terminal via data display means and presented to the user in audio and visual formats.

[0180] For example, if a user says, "I'm feeling down today," the server performs analysis and sentiment assessment, and generates a gentle, encouraging response such as, "It's okay to feel that way sometimes; maybe you should take a break." This response is delivered to the user through the device's speaker module.

[0181] An example of a prompt for a generative AI model is, "Consider the user's emotional state when responding to vague commands. If the user expresses sadness, generate a comforting response that includes actionable support." This prompt is used to generate a response that reflects the user's emotional state.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The user provides ambiguous instructions via voice input through a device. This input is recorded as voice data, which is then collected by a data receiving device.

[0185] Step 2:

[0186] The device sends voice data to the server. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in the voice input being output as text.

[0187] Step 3:

[0188] The server passes text data to a data analysis tool. A BERT model using TensorFlow is used to analyze the context of the text data and clarify the intent of the instructions. The analysis results in data that shows the specific intent of the instructions.

[0189] Step 4:

[0190] The analyzed data is sent to a sentiment analysis system. The server uses Python libraries such as TextBlob and NLTK to evaluate the emotional state within the text. An output in the form of a sentiment score is generated.

[0191] Step 5:

[0192] Based on the sentiment analysis results, the server creates specific instructions through data transformation mechanisms. Using a generative AI model, such as GPT-3.5, it generates personalized responses that match the user's emotions using prompt sentences. These responses are output as text data.

[0193] Step 6:

[0194] The final response is transmitted to the terminal via a data display device. The terminal then uses a speaker module to present the generated response to the user as audio.

[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] This invention is a system that converts ambiguous instructions into specific actions, and is realized by coordinating various devices. An example thereof is shown below.

[0212] The user inputs vague instructions into the system using a terminal. The terminal, such as a smartphone or personal computer, provides an interface for sending the input data to the server. While text input is the primary method, voice input is also possible.

[0213] The server receives ambiguous instructions sent from the terminal and analyzes them using an analysis device. The analysis device uses natural language processing technology to understand the context and intent of the instructions, and as a result extracts important keywords and phrases. This process clarifies ambiguous parts, allowing for more specific guidance.

[0214] Next, the server sends the obtained analysis results to the conversion device. The conversion device converts the analysis results into specific instructions and information. At this stage, it is also possible to improve the accuracy of the conversion results by using data from internal databases and related external information sources.

[0215] The converted specific instructions are sent from the server to the terminal. The terminal's display visually presents the results in a user-friendly format, allowing the user to quickly decide on their next action.

[0216] As a concrete example, consider internal corporate communication. When a user inputs the instruction "Prepare the monthly report," the server analyzes this vague instruction and generates specific tasks such as "Collect past monthly data" or "Add new report items." These tasks are then displayed on the terminal, allowing the user to perform specific actions.

[0217] Thus, by implementing the present invention, it is possible to clarify ambiguous instructions and provide an environment in which users can act efficiently.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The user enters vague instructions using the device. The device records the text and audio as digital data and prepares it for transmission to the server.

[0221] Step 2:

[0222] The terminal sends user instruction data to the server. The server receives this data and passes it on to the analysis device.

[0223] Step 3:

[0224] The server's analysis device analyzes the received instruction data. During this process, natural language processing techniques are used to extract key verbs and nouns from the text and understand the context.

[0225] Step 4:

[0226] The analysis device identifies the specific requirements for the instructions based on the analysis results and passes the results to the conversion device.

[0227] Step 5:

[0228] The server's conversion device translates analysis results into specific instructions and tasks. It improves the accuracy of the information by referencing internal databases and external information sources as needed.

[0229] Step 6:

[0230] The specific instructions that have been converted are sent from the server to the terminal. The terminal then presents the received content to the user.

[0231] Step 7:

[0232] The user checks the specific instructions displayed on the device and decides what action to take next. This allows the user to start responding immediately.

[0233] (Example 1)

[0234] 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."

[0235] In recent years, when users input instructions via terminals, there are many cases where their intentions are not accurately conveyed due to ambiguous expressions or ambiguous language. Such ambiguity can lead to problems that impair efficiency, as it prevents prompt responses to the specific actions the user intends to take. Furthermore, current systems lack sufficient additional information gathering and natural language processing to identify the user's intentions, resulting in cumbersome operation. Therefore, it is desirable to efficiently clarify ambiguous instructions, present information in an easy-to-understand manner for users, and provide an environment that allows them to take action quickly.

[0236] 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.

[0237] In this invention, the server includes a display terminal means for receiving ambiguous instructions from a user, a communication means for transmitting the ambiguous instructions in a format suitable for the processing target, and a statistical processing means for analyzing the ambiguous instructions and extracting key information. This makes it possible to convert ambiguous instructions into specific action commands and present them to the user quickly and accurately.

[0238] "Vague instructions from users" refer to expressions that do not clearly specify concrete actions or results, and are ambiguous and open to interpretation.

[0239] "Display terminal means" refers to a device used by users to input information or visually confirm results, and generally includes smartphones and personal computers.

[0240] "Communication methods" refer to technologies that use protocols and interfaces to transmit information from a source to a destination, and specifically include data transmission using the internet or local networks.

[0241] "Statistical processing methods" refer to processing methods that use statistical techniques to extract meaningful information from large amounts of data and perform data analysis and interpretation.

[0242] "Data conversion means" refers to a device or method that converts input data into a different format or structure and arranges it in a way that is suitable for a specific purpose.

[0243] "Means of information representation" refers to methods and technologies for presenting information in a format that is easy for users to understand, and includes displays using graphical user interfaces.

[0244] "Information gathering means" refers to the means of obtaining necessary information from external sources and preparing it in a format usable within the system.

[0245] "Statistical processing techniques for natural language" refer to technologies that analyze the language that people normally use using statistical approaches to understand its context and meaning.

[0246] This invention is a system that receives ambiguous instructions from a user and converts them into specific actions. This system includes a display terminal, a communication means, a statistical processing means, a data conversion means, and an information representation means.

[0247] Specifically, the user inputs ambiguous instructions into the system using a display terminal. This display terminal can be a smartphone or a personal computer. The terminal then transmits the input data to a server via a communication device. This communication can utilize the internet or a local network.

[0248] The server analyzes the received ambiguous instructions using statistical processing techniques. These techniques incorporate natural language processing technologies and machine learning models, with libraries such as TensorFlow and PyTorch being used as specific examples. The server uses these techniques to understand the context of the instructions and extract important keywords.

[0249] Subsequently, the server uses data conversion tools to convert the analysis results into specific action commands. During this process, information can be collected from internal databases and external sources to improve the accuracy of the commands. SQL queries and REST APIs are used for information gathering.

[0250] The converted specific action commands are then transmitted again from the server to the display terminal. The information representation means presents the converted commands in a visually easy-to-understand format for the user. This allows the user to quickly proceed to the next action.

[0251] For example, if a user enters the instruction "Prepare for tomorrow's team meeting," the server analyzes this vague instruction and generates specific tasks such as "Check the attendee list" and "Prepare meeting materials." These generated tasks are then displayed on the device, allowing the user to immediately perform these actions.

[0252] An example of a prompt to the generative AI model is, "Please translate vague instructions into specific tasks." This invention efficiently concretizes vague instructions and helps users make quick decisions about what to do.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The user inputs ambiguous instructions using a display terminal. The primary input method is text, and direct input is possible using a keyboard or microphone. This input data becomes basic information used in subsequent processing.

[0256] Step 2:

[0257] The terminal transmits information entered by the user to the server via a communication method. Encryption technology (SSL / TLS) is applied to ensure that the data is transmitted securely over the internet. The output of this process becomes the initial input data for processing by the server.

[0258] Step 3:

[0259] The server analyzes the received ambiguous instructions using statistical processing techniques. Natural language processing techniques are used to analyze words and phrases in the input text and understand the context. Specifically, machine learning models (e.g., BERT or GPT) are used to extract important keywords. The output of this process is a list of keywords as a result of the analysis.

[0260] Step 4:

[0261] The server converts the analysis results into specific action commands using data conversion means. It generates specific tasks while acquiring necessary information from internal databases and external information sources. For example, it forms action commands such as "send an email" or "aggregate data." The output of this process is a clearly defined set of action commands.

[0262] Step 5:

[0263] The server transmits the converted command back to the display terminal via communication means. The terminal's information display means visually displays this specific action command, presenting it in a way that allows the user to understand the next action to take. This final output is a concrete task display in a format that the user can confirm.

[0264] (Application Example 1)

[0265] 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."

[0266] In modern times, users of autonomous vehicles face the challenge of converting vague instructions into clear, precise, and specific action instructions when setting their destinations. Furthermore, a lack of necessary information for efficient transportation guidance makes it difficult to determine the optimal mode of transport and route to one's destination. To address these challenges, a user-friendly and intuitive system is required.

[0267] 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.

[0268] In this invention, the server includes terminal means for receiving ambiguous instructions from a user, language analysis means for analyzing the ambiguous instructions and understanding their context, and command conversion means for converting the analysis results obtained by the language analysis means into specific action instructions. As a result, users can obtain the necessary location information accurately and specific action instructions, including optimal route guidance, simply by inputting ambiguous instructions.

[0269] "Terminal means" refers to electronic devices used to receive ambiguous instructions from users, such as smartphones and computers.

[0270] A "language analysis device" is a device that uses natural language processing technology to analyze ambiguous instructions received and understand their context.

[0271] A "command conversion means" is a device that converts the analysis results obtained by the language analysis means into specific action instructions.

[0272] A "visualization means" is a display device that presents the converted, specific action instructions to the user.

[0273] A "transportation guidance system" refers to a system or device that provides travel guidance corresponding to a means of transportation.

[0274] "Information gathering means" refers to devices or processes for acquiring additional location information in accordance with specific instructions.

[0275] The system that realizes this application is one that can translate vague instructions given by users into concrete actions. The server plays a central role in this system, first receiving vague instructions from the user via a terminal device. The terminal device is an electronic device that the user can directly input, such as a smartphone or a personal computer.

[0276] The server then analyzes the received instructions using language analysis tools. Here, natural language processing techniques are used to understand the context and intent of the instructions. The analyzed instructions are then converted into specific action instructions by command conversion tools. During this process, data from databases and related external information sources are utilized to improve the accuracy of the converted instructions.

[0277] Ultimately, these instructions are presented to the user in an easily understandable format through visualization means. The system also includes transportation guidance means to provide information related to means of transport, ensuring that users receive appropriate travel guidance.

[0278] As a concrete example, a user riding in an autonomous vehicle might use their smartphone to give instructions such as, "I want to go to a nice cafe nearby." Upon receiving this instruction, the server analyzes it based on context and creates a list of nearby cafes. The user's past preference data and current location information are also taken into account to suggest the optimal destination. The transportation guidance system is integrated with the autonomous vehicle's navigation system and also provides the most efficient route.

[0279] Prompt phrases such as "cafe," "lunch," "quiet," and "Wi-Fi" are considered, and this system can use a generative AI model to provide more appropriate and personalized guidance.

[0280] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0281] Step 1:

[0282] The user uses the terminal to input an ambiguous instruction. For example, an instruction such as "want to go to a delicious café nearby" can be input. The input data is collected as text or voice and sent to the server. At this stage, the input data reaches the server as ambiguous natural language data.

[0283] Step 2:

[0284] The server analyzes the received ambiguous instruction using language analysis means. It receives the ambiguous instruction as input, analyzes it with natural language processing technology, and understands its context. Here, the prompt sentence is analyzed using a generative AI model to extract important keywords such as "café" and "delicious". As a result, the analyzed intention is output.

[0285] Step 3:

[0286] The server uses command conversion means to convert it into a specific action instruction based on the analysis result. It receives the analysis result as input, refers to data from a database or external information source, and identifies specific café candidates. The candidate list obtained in this process is generated as output.

[0287] Step 4:

[0288] The server uses transportation guidance means to provide an optimal route to the navigation system of the autonomous vehicle. It receives the specific action instruction as input and converts it into a movement guidance corresponding to the transportation means. Estimated information on the movement route and required time is output, and guidance is provided to the autonomous vehicle.

[0289] Step 5:

[0290] The terminal uses visualization to present users with specific action instructions and directions. It receives specific action instructions and navigation information sent from the server as input and displays them visually. This allows users to confirm and select the optimal cafe option and how to get there.

[0291] 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.

[0292] This invention provides a system that offers more appropriate and personalized responses by taking into account the user's emotions when concretizing ambiguous instructions. An example thereof is shown below.

[0293] The user uses the terminal to input ambiguous instructions or questions. The terminal records or logs this input as digital data and prepares it for transmission to the server. Once the input is complete, the data is sent to the server.

[0294] The server passes the data received from the terminal to the analysis device. The analysis device uses natural language processing technology to analyze ambiguous instructions, understanding the context and clarifying the intent of the instructions. Based on the analysis results, key information is extracted, and specific instructions are formed.

[0295] A distinctive feature of this invention is the integration of an emotion engine into the server. The emotion engine analyzes the user's input data and the emotional expressions within it to understand the emotional state in which the user is issuing instructions. This emotional information is fed back into the analysis and conversion process, generating more specific instructions that are sensitive to the user's emotions.

[0296] Furthermore, specific instructions are generated by a conversion device, and an information acquisition device is activated to obtain additional information as needed. This additional information may be optimized based on the user's emotions.

[0297] For example, when a user inputs "The progress of the project is lagging", if emotions such as anxiety and stress are detected, the server preferentially presents specific procedures and support resources that are helpful for problem-solving.

[0298] The generated specific instructions are sent from the server to the terminal and displayed to the user. Here, efforts are also made to adjust the tone and expression according to the emotions so that the user can receive them more comfortably.

[0299] By implementing the present invention, a flexible response considering emotions can be obtained even for ambiguous instructions, improving the user experience.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The user uses the terminal to input an ambiguous instruction to the system. The terminal records this input as text data and prepares to send it to the server.

[0303] Step 2:

[0304] The instruction data input by the user is sent from the terminal to the server. The server receives this data and quickly starts processing.

[0305] Step 3:

[0306] The server passes the received data to the analysis device. The analysis device uses natural language processing technology to analyze the ambiguous instruction and understand its intention. At this stage, the context of the instruction is clarified and the necessary markup information is extracted.

[0307] Step 4:

[0308] The server's emotion engine detects the user's emotions contained within the analyzed instructions. In this process, emotion analysis technology is used to analyze emotional expressions and tone from the user's input, and to infer their emotional state.

[0309] Step 5:

[0310] Based on the analysis results obtained from the analysis device and the emotional information from the emotion engine, the server's conversion device transforms the user's instructions into specific actions and information. The content and tone of the instructions are adjusted according to the emotional information.

[0311] Step 6:

[0312] The server sends the translated, specific instructions to the terminal. At this point, the terminal prepares to display the information in a way that takes the user's emotional state into account.

[0313] Step 7:

[0314] The device displays specific instructions to the user. Based on these instructions, the user can more easily decide on the appropriate course of action. This allows the user to proceed to the next step quickly and efficiently.

[0315] (Example 2)

[0316] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0317] Conventional systems for analyzing ambiguous instructions simply analyze and concretize instructions without considering the emotions of the user receiving them. This often leads to inappropriate responses that do not meet user expectations and a decrease in usability. Furthermore, providing information that ignores the user's emotional state can impair user satisfaction.

[0318] 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.

[0319] In this invention, the server includes input / output means, analysis means, conversion means, sentiment analysis means, and display means. This makes it possible to respond appropriately and specifically to ambiguous instructions while taking emotions into consideration, thereby realizing a more satisfying interaction for the user.

[0320] "Input / output means" refers to communication and interface devices for receiving ambiguous instructions from users and transmitting them to a server.

[0321] An "analysis tool" is a device that uses natural language processing technology to analyze ambiguous instructions received and clarify their context and intent.

[0322] A "conversion means" is a device that generates specific instructions based on the analysis results obtained by the analysis means.

[0323] An "emotional analysis tool" is a device that analyzes the emotional state contained in the user's instructions and reflects that information in specific instructions.

[0324] A "display means" is a device for presenting the generated specific instructions to the user.

[0325] This invention is a system that receives ambiguous instructions from users and converts them into specific instructions based on advanced analytical methods, including sentiment analysis. This enables the provision of personalized responses that take into account the user's emotional state.

[0326] The system primarily consists of terminals and servers. The terminals receive user input in either voice or text format. Voice input is converted to text using speech recognition technology. The terminals then transmit this digital data to the server.

[0327] The server processes the received data using an analysis device. The analysis device utilizes algorithms commonly used in natural language processing. For example, it uses BERT or similar models to understand the context of ambiguous instructions and clarify their intent. Based on the analysis results, a conversion device forms specific instructions.

[0328] The server incorporates an emotion analysis device that understands the user's emotional state from their input data. For example, it analyzes emotions such as "anxiety," and this information is reflected in specific instructions. This enables responses that are sensitive to the user's emotions, allowing for the presentation of appropriate solutions and suggestions.

[0329] As a concrete example, consider a scenario where a user inputs "I'm worried because the project is behind schedule" into the terminal. The emotion analysis device detects the emotion "worry" and feeds this information back into the analysis process. The conversion device uses this information to generate specific guidance, such as "Utilize resources to speed up the project and check the checklist."

[0330] Finally, the server sends the generated specific instructions to the terminal and displays them to the user. Here, the tone and expression are adjusted according to the user's emotions. For example, a message with a gentle tone such as, "Don't worry, please check out these resources," might be displayed.

[0331] An example of a prompt would be, "What kind of support do you need to alleviate my worries when my project is behind schedule?" In response to this prompt, the system can suggest specific measures, including emotional support.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The user uses the terminal to input ambiguous instructions or questions in voice or text format. The terminal uses speech recognition technology to convert voice input into text format. This converted digital data is then prepared for transmission to the server. The input is the user's natural language instructions, and the output is digital data in text format.

[0335] Step 2:

[0336] The terminal establishes a communication channel for sending digital data to the server and then sends that digital data to the server. The input is digital data in text format, and the output is the transmission of data to the server. Specifically, the terminal establishes a network connection and sends text data to the server according to a data transfer protocol.

[0337] Step 3:

[0338] The server passes the digital data received from the terminal to the analysis device. The analysis device uses a generative AI model to analyze the instructions using natural language processing technology. The analysis involves understanding the context and extracting important information. The input is the text data received by the server, and the output is the analyzed contextual information and important information. The analysis device performs natural language contextual analysis using a specific algorithm.

[0339] Step 4:

[0340] The server generates specific instructions using the analyzed contextual information. A conversion mechanism is used to transform the analysis results into clear action guidelines. A generative AI model is utilized in this process. The input is the analysis result, and the output is specific action guidelines. For example, it forms concrete steps in response to questions such as "What is the next step?"

[0341] Step 5:

[0342] A sentiment analysis system built into the server analyzes the emotional elements contained in the input data. This allows the system to understand the user's emotional state and reflect this information in the specific instructions it generates. The input is the user's text data, and the output is specific instructions that reflect their emotions. For example, if "anxiety" is detected, the system will generate instructions that provide a sense of reassurance.

[0343] Step 6:

[0344] The server sends specific instructions to the terminal, taking emotions into consideration. The terminal displays these instructions to the user, presenting the received data visually or audibly in an appropriate format. The input is specific instruction data from the server, and the output is the visual or audible presentation of instructions to the user. Specific actions on the terminal include displaying messages on the screen or playing audio through the speaker.

[0345] (Application Example 2)

[0346] 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."

[0347] Traditionally, system responses to ambiguous user instructions have been made without considering the user's emotional state, sometimes resulting in responses that don't align with the user's psychological condition. This raises concerns, especially in home robots, that users may experience stress. There is a need to achieve more personalized responses that take user emotions into account.

[0348] 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.

[0349] In this invention, the server includes data receiving means, data analysis means, data conversion means, emotion analysis means, and data display means. This enables specific and personalized responses to ambiguous instructions that reflect the user's emotional state.

[0350] A "data receiving means" is a device that has the function of receiving ambiguous instructions from a user.

[0351] A "data analysis device" is a device that has the function of analyzing ambiguous instructions received and understanding the context of those instructions.

[0352] A "data conversion means" is a device that has the function of converting analysis results into specific instructions.

[0353] An "emotional analysis device" is a device that has the function of analyzing the emotional state of a user.

[0354] A "data display means" is a device that has the function of adjusting and presenting instructions to the user based on their emotional state.

[0355] The system for realizing this invention will primarily be developed around a scenario in which a user uses a smart robot assistant in their home. The system will receive ambiguous instructions from the user via a program running on a terminal. A data receiving means can be used to collect the user's voice and text data. It is preferable to use low-power hardware such as a Raspberry Pi or Jetson Nano as the terminal.

[0356] The server uses the Google Cloud Speech-to-Text API to convert speech data into text as a data analysis tool. For natural language processing, it utilizes a BERT model powered by TensorFlow to analyze the context of ambiguous instructions received. The analyzed data is then used for sentiment analysis, evaluating the user's emotional state using Python libraries such as TextBlob and NLTK.

[0357] The server generates specific instructions through a data transformation mechanism, taking into account the results of sentiment analysis. In this process, it utilizes generative AI models such as GPT-3.5 to create personalized responses tailored to the user's emotions. The generated instructions are transmitted to the terminal via a data display mechanism and presented to the user via audio and visual means.

[0358] For example, if a user says, "I'm feeling down today," the server performs analysis and sentiment assessment, and generates a gentle, encouraging response such as, "It's okay to feel that way sometimes; maybe you should take a break." This response is delivered to the user through the device's speaker module.

[0359] An example of a prompt for a generative AI model is, "Consider the user's emotional state when responding to vague commands. If the user expresses sadness, generate a comforting response that includes actionable support." This prompt is used to generate a response that reflects the user's emotional state.

[0360] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0361] Step 1:

[0362] The user provides ambiguous instructions via voice input through a device. This input is recorded as voice data, which is then collected by a data receiving device.

[0363] Step 2:

[0364] The device sends voice data to the server. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in the voice input being output as text.

[0365] Step 3:

[0366] The server passes text data to a data analysis tool. A BERT model using TensorFlow is used to analyze the context of the text data and clarify the intent of the instructions. The analysis results in data that shows the specific intent of the instructions.

[0367] Step 4:

[0368] The analyzed data is sent to a sentiment analysis system. The server uses Python libraries such as TextBlob and NLTK to evaluate the emotional state within the text. An output in the form of a sentiment score is generated.

[0369] Step 5:

[0370] Based on the sentiment analysis results, the server creates specific instructions through data transformation mechanisms. Using a generative AI model, such as GPT-3.5, it generates personalized responses that match the user's emotions using prompt sentences. These responses are output as text data.

[0371] Step 6:

[0372] The final response is transmitted to the terminal via a data display device. The terminal then uses a speaker module to present the generated response to the user as audio.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] [Third Embodiment]

[0377] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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".

[0389] This invention is a system that converts ambiguous instructions into specific actions, and is realized by coordinating various devices. An example thereof is shown below.

[0390] The user inputs vague instructions into the system using a terminal. The terminal, such as a smartphone or personal computer, provides an interface for sending the input data to the server. While text input is the primary method, voice input is also possible.

[0391] The server receives ambiguous instructions sent from the terminal and analyzes them using an analysis device. The analysis device uses natural language processing technology to understand the context and intent of the instructions, and as a result extracts important keywords and phrases. This process clarifies ambiguous parts, allowing for more specific guidance.

[0392] Next, the server sends the obtained analysis results to the conversion device. The conversion device converts the analysis results into specific instructions and information. At this stage, it is also possible to improve the accuracy of the conversion results by using data from internal databases and related external information sources.

[0393] The converted specific instructions are sent from the server to the terminal. The terminal's display visually presents the results in a user-friendly format, allowing the user to quickly decide on their next action.

[0394] As a concrete example, consider internal corporate communication. When a user inputs the instruction "Prepare the monthly report," the server analyzes this vague instruction and generates specific tasks such as "Collect past monthly data" or "Add new report items." These tasks are then displayed on the terminal, allowing the user to perform specific actions.

[0395] Thus, by implementing the present invention, it is possible to clarify ambiguous instructions and provide an environment in which users can act efficiently.

[0396] The following describes the processing flow.

[0397] Step 1:

[0398] The user enters vague instructions using the device. The device records the text and audio as digital data and prepares it for transmission to the server.

[0399] Step 2:

[0400] The terminal sends user instruction data to the server. The server receives this data and passes it on to the analysis device.

[0401] Step 3:

[0402] The server's analysis device analyzes the received instruction data. During this process, natural language processing techniques are used to extract key verbs and nouns from the text and understand the context.

[0403] Step 4:

[0404] The analysis device identifies the specific requirements for the instructions based on the analysis results and passes the results to the conversion device.

[0405] Step 5:

[0406] The server's conversion device translates analysis results into specific instructions and tasks. It improves the accuracy of the information by referencing internal databases and external information sources as needed.

[0407] Step 6:

[0408] The specific instructions that have been converted are sent from the server to the terminal. The terminal then presents the received content to the user.

[0409] Step 7:

[0410] The user checks the specific instructions displayed on the device and decides what action to take next. This allows the user to start responding immediately.

[0411] (Example 1)

[0412] 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."

[0413] In recent years, when users input instructions via terminals, there are many cases where their intentions are not accurately conveyed due to ambiguous expressions or ambiguous language. Such ambiguity can lead to problems that impair efficiency, as it prevents prompt responses to the specific actions the user intends to take. Furthermore, current systems lack sufficient additional information gathering and natural language processing to identify the user's intentions, resulting in cumbersome operation. Therefore, it is desirable to efficiently clarify ambiguous instructions, present information in an easy-to-understand manner for users, and provide an environment that allows them to take action quickly.

[0414] 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.

[0415] In this invention, the server includes a display terminal means for receiving ambiguous instructions from a user, a communication means for transmitting the ambiguous instructions in a format suitable for the processing target, and a statistical processing means for analyzing the ambiguous instructions and extracting key information. This makes it possible to convert ambiguous instructions into specific action commands and present them to the user quickly and accurately.

[0416] "Vague instructions from users" refer to expressions that do not clearly specify concrete actions or results, and are ambiguous and open to interpretation.

[0417] "Display terminal means" refers to a device used by users to input information or visually confirm results, and generally includes smartphones and personal computers.

[0418] "Communication methods" refer to technologies that use protocols and interfaces to transmit information from a source to a destination, and specifically include data transmission using the internet or local networks.

[0419] "Statistical processing methods" refer to processing methods that use statistical techniques to extract meaningful information from large amounts of data and perform data analysis and interpretation.

[0420] "Data conversion means" refers to a device or method that converts input data into a different format or structure and arranges it in a way that is suitable for a specific purpose.

[0421] "Means of information representation" refers to methods and technologies for presenting information in a format that is easy for users to understand, and includes displays using graphical user interfaces.

[0422] "Information gathering means" refers to the means of obtaining necessary information from external sources and preparing it in a format usable within the system.

[0423] "Statistical processing techniques for natural language" refer to technologies that analyze the language that people normally use using statistical approaches to understand its context and meaning.

[0424] This invention is a system that receives ambiguous instructions from a user and converts them into specific actions. This system includes a display terminal, a communication means, a statistical processing means, a data conversion means, and an information representation means.

[0425] Specifically, the user inputs ambiguous instructions into the system using a display terminal. This display terminal can be a smartphone or a personal computer. The terminal then transmits the input data to a server via a communication device. This communication can utilize the internet or a local network.

[0426] The server analyzes the received ambiguous instructions using statistical processing techniques. These techniques incorporate natural language processing technologies and machine learning models, with libraries such as TensorFlow and PyTorch being used as specific examples. The server uses these techniques to understand the context of the instructions and extract important keywords.

[0427] Subsequently, the server uses data conversion tools to convert the analysis results into specific action commands. During this process, information can be collected from internal databases and external sources to improve the accuracy of the commands. SQL queries and REST APIs are used for information gathering.

[0428] The converted specific action commands are then transmitted again from the server to the display terminal. The information representation means presents the converted commands in a visually easy-to-understand format for the user. This allows the user to quickly proceed to the next action.

[0429] For example, if a user enters the instruction "Prepare for tomorrow's team meeting," the server analyzes this vague instruction and generates specific tasks such as "Check the attendee list" and "Prepare meeting materials." These generated tasks are then displayed on the device, allowing the user to immediately perform these actions.

[0430] An example of a prompt to the generative AI model is, "Please translate vague instructions into specific tasks." This invention efficiently concretizes vague instructions and helps users make quick decisions about what to do.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The user inputs ambiguous instructions using a display terminal. The primary input method is text, and direct input is possible using a keyboard or microphone. This input data becomes basic information used in subsequent processing.

[0434] Step 2:

[0435] The terminal transmits information entered by the user to the server via a communication method. Encryption technology (SSL / TLS) is applied to ensure that the data is transmitted securely over the internet. The output of this process becomes the initial input data for processing by the server.

[0436] Step 3:

[0437] The server analyzes the received ambiguous instructions using statistical processing techniques. Natural language processing techniques are used to analyze words and phrases in the input text and understand the context. Specifically, machine learning models (e.g., BERT or GPT) are used to extract important keywords. The output of this process is a list of keywords as a result of the analysis.

[0438] Step 4:

[0439] The server converts the analysis results into specific action commands using data conversion means. It generates specific tasks while acquiring necessary information from internal databases and external information sources. For example, it forms action commands such as "send an email" or "aggregate data." The output of this process is a clearly defined set of action commands.

[0440] Step 5:

[0441] The server transmits the converted command back to the display terminal via communication means. The terminal's information display means visually displays this specific action command, presenting it in a way that allows the user to understand the next action to take. This final output is a concrete task display in a format that the user can confirm.

[0442] (Application Example 1)

[0443] 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."

[0444] In modern times, users of autonomous vehicles face the challenge of converting vague instructions into clear, precise, and specific action instructions when setting their destinations. Furthermore, a lack of necessary information for efficient transportation guidance makes it difficult to determine the optimal mode of transport and route to one's destination. To address these challenges, a user-friendly and intuitive system is required.

[0445] 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.

[0446] In this invention, the server includes terminal means for receiving ambiguous instructions from a user, language analysis means for analyzing the ambiguous instructions and understanding their context, and command conversion means for converting the analysis results obtained by the language analysis means into specific action instructions. As a result, users can obtain the necessary location information accurately and specific action instructions, including optimal route guidance, simply by inputting ambiguous instructions.

[0447] "Terminal means" refers to electronic devices used to receive ambiguous instructions from users, such as smartphones and computers.

[0448] A "language analysis device" is a device that uses natural language processing technology to analyze ambiguous instructions received and understand their context.

[0449] A "command conversion means" is a device that converts the analysis results obtained by the language analysis means into specific action instructions.

[0450] A "visualization means" is a display device that presents the converted, specific action instructions to the user.

[0451] A "transportation guidance system" refers to a system or device that provides travel guidance corresponding to a means of transportation.

[0452] "Information gathering means" refers to devices or processes for acquiring additional location information in accordance with specific instructions.

[0453] The system that realizes this application is one that can translate vague instructions given by users into concrete actions. The server plays a central role in this system, first receiving vague instructions from the user via a terminal device. The terminal device is an electronic device that the user can directly input, such as a smartphone or a personal computer.

[0454] The server then analyzes the received instructions using language analysis tools. Here, natural language processing techniques are used to understand the context and intent of the instructions. The analyzed instructions are then converted into specific action instructions by command conversion tools. During this process, data from databases and related external information sources are utilized to improve the accuracy of the converted instructions.

[0455] Ultimately, these instructions are presented to the user in an easily understandable format through visualization means. The system also includes transportation guidance means to provide information related to means of transport, ensuring that users receive appropriate travel guidance.

[0456] As a concrete example, a user riding in an autonomous vehicle might use their smartphone to give instructions such as, "I want to go to a nice cafe nearby." Upon receiving this instruction, the server analyzes it based on context and creates a list of nearby cafes. The user's past preference data and current location information are also taken into account to suggest the optimal destination. The transportation guidance system is integrated with the autonomous vehicle's navigation system and also provides the most efficient route.

[0457] Prompt phrases such as "cafe," "lunch," "quiet," and "Wi-Fi" are considered, and this system can use a generative AI model to provide more appropriate and personalized guidance.

[0458] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0459] Step 1:

[0460] The user uses a device to input vague instructions. For example, they might input an instruction such as, "I want to go to a nice cafe nearby." The input data is collected as text or voice and sent to the server. At this stage, the input data arrives at the server as vague natural language data.

[0461] Step 2:

[0462] The server analyzes the received ambiguous instructions using language analysis tools. It receives ambiguous instructions as input, analyzes them using natural language processing techniques, and understands their context. Here, it analyzes the prompt sentence using a generative AI model and extracts important keywords such as "cafe" and "delicious." As a result, the analyzed intent is output.

[0463] Step 3:

[0464] The server uses a command conversion mechanism to convert the analysis results into specific action instructions. It receives the analysis results as input and identifies specific cafe candidates by referencing data from databases and external information sources. The candidate list obtained in this process is generated as output.

[0465] Step 4:

[0466] The server uses transportation guidance methods to provide the optimal route to the autonomous vehicle's navigation system. It receives specific action instructions as input and converts them into travel guidance corresponding to the means of transport. Estimated travel route and travel time information is output and guided to the autonomous vehicle.

[0467] Step 5:

[0468] The terminal uses visualization to present users with specific action instructions and directions. It receives specific action instructions and navigation information sent from the server as input and displays them visually. This allows users to confirm and select the optimal cafe option and how to get there.

[0469] 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.

[0470] This invention provides a system that offers more appropriate and personalized responses by taking into account the user's emotions when concretizing ambiguous instructions. An example thereof is shown below.

[0471] The user uses the terminal to input ambiguous instructions or questions. The terminal records or logs this input as digital data and prepares it for transmission to the server. Once the input is complete, the data is sent to the server.

[0472] The server passes the data received from the terminal to the analysis device. The analysis device uses natural language processing technology to analyze ambiguous instructions, understanding the context and clarifying the intent of the instructions. Based on the analysis results, key information is extracted, and specific instructions are formed.

[0473] A distinctive feature of this invention is the integration of an emotion engine into the server. The emotion engine analyzes the user's input data and the emotional expressions within it to understand the emotional state in which the user is issuing instructions. This emotional information is fed back into the analysis and conversion process, generating more specific instructions that are sensitive to the user's emotions.

[0474] Furthermore, specific instructions are generated by a conversion device, and an information acquisition device is activated to obtain additional information as needed. This additional information may be optimized based on the user's emotions.

[0475] For example, when a user enters "the project is behind schedule," if feelings of anxiety or stress are detected, the server will prioritize providing specific steps and support resources that can help resolve the problem.

[0476] The generated specific instructions are sent from the server to the terminal and displayed to the user. Here too, the tone and expression are adjusted according to the user's emotions, making it a more comfortable experience for the user.

[0477] By implementing this invention, flexible responses that take emotions into account can be obtained even in the face of ambiguous instructions, thereby improving the user experience.

[0478] The following describes the processing flow.

[0479] Step 1:

[0480] The user inputs vague instructions into the system using a terminal. The terminal records this input as text data and prepares it for transmission to the server.

[0481] Step 2:

[0482] The user's input data is sent from the terminal to the server. The server receives this data and immediately begins processing it.

[0483] Step 3:

[0484] The server passes the received data to the analysis device. The analysis device uses natural language processing technology to analyze ambiguous instructions and understand their intent. At this stage, the context of the instructions is clarified, and the necessary marquee information is extracted.

[0485] Step 4:

[0486] The server's emotion engine detects the user's emotions contained within the analyzed instructions. In this process, emotion analysis technology is used to analyze emotional expressions and tone from the user's input, and to infer their emotional state.

[0487] Step 5:

[0488] Based on the analysis results obtained from the analysis device and the emotional information from the emotion engine, the server's conversion device transforms the user's instructions into specific actions and information. The content and tone of the instructions are adjusted according to the emotional information.

[0489] Step 6:

[0490] The server sends the translated, specific instructions to the terminal. At this point, the terminal prepares to display the information in a way that takes the user's emotional state into account.

[0491] Step 7:

[0492] The device displays specific instructions to the user. Based on these instructions, the user can more easily decide on the appropriate course of action. This allows the user to proceed to the next step quickly and efficiently.

[0493] (Example 2)

[0494] 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."

[0495] Conventional systems for analyzing ambiguous instructions simply analyze and concretize instructions without considering the emotions of the user receiving them. This often leads to inappropriate responses that do not meet user expectations and a decrease in usability. Furthermore, providing information that ignores the user's emotional state can impair user satisfaction.

[0496] 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.

[0497] In this invention, the server includes input / output means, analysis means, conversion means, sentiment analysis means, and display means. This makes it possible to respond appropriately and specifically to ambiguous instructions while taking emotions into consideration, thereby realizing a more satisfying interaction for the user.

[0498] "Input / output means" refers to communication and interface devices for receiving ambiguous instructions from users and transmitting them to a server.

[0499] An "analysis tool" is a device that uses natural language processing technology to analyze ambiguous instructions received and clarify their context and intent.

[0500] A "conversion means" is a device that generates specific instructions based on the analysis results obtained by the analysis means.

[0501] An "emotional analysis tool" is a device that analyzes the emotional state contained in the user's instructions and reflects that information in specific instructions.

[0502] A "display means" is a device for presenting the generated specific instructions to the user.

[0503] This invention is a system that receives ambiguous instructions from users and converts them into specific instructions based on advanced analytical methods, including sentiment analysis. This enables the provision of personalized responses that take into account the user's emotional state.

[0504] The system primarily consists of terminals and servers. The terminals receive user input in either voice or text format. Voice input is converted to text using speech recognition technology. The terminals then transmit this digital data to the server.

[0505] The server processes the received data using an analysis device. The analysis device utilizes algorithms commonly used in natural language processing. For example, it uses BERT or similar models to understand the context of ambiguous instructions and clarify their intent. Based on the analysis results, a conversion device forms specific instructions.

[0506] The server incorporates an emotion analysis device that understands the user's emotional state from their input data. For example, it analyzes emotions such as "anxiety," and this information is reflected in specific instructions. This enables responses that are sensitive to the user's emotions, allowing for the presentation of appropriate solutions and suggestions.

[0507] As a concrete example, consider a scenario where a user inputs "I'm worried because the project is behind schedule" into the terminal. The emotion analysis device detects the emotion "worry" and feeds this information back into the analysis process. The conversion device uses this information to generate specific guidance, such as "Utilize resources to speed up the project and check the checklist."

[0508] Finally, the server sends the generated specific instructions to the terminal and displays them to the user. Here, the tone and expression are adjusted according to the user's emotions. For example, a message with a gentle tone such as, "Don't worry, please check out these resources," might be displayed.

[0509] An example of a prompt would be, "What kind of support do you need to alleviate my worries when my project is behind schedule?" In response to this prompt, the system can suggest specific measures, including emotional support.

[0510] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0511] Step 1:

[0512] The user uses the terminal to input ambiguous instructions or questions in voice or text format. The terminal uses speech recognition technology to convert voice input into text format. This converted digital data is then prepared for transmission to the server. The input is the user's natural language instructions, and the output is digital data in text format.

[0513] Step 2:

[0514] The terminal establishes a communication channel for sending digital data to the server and then sends that digital data to the server. The input is digital data in text format, and the output is the transmission of data to the server. Specifically, the terminal establishes a network connection and sends text data to the server according to a data transfer protocol.

[0515] Step 3:

[0516] The server passes the digital data received from the terminal to the analysis device. The analysis device uses a generative AI model to analyze the instructions using natural language processing technology. The analysis involves understanding the context and extracting important information. The input is the text data received by the server, and the output is the analyzed contextual information and important information. The analysis device performs natural language contextual analysis using a specific algorithm.

[0517] Step 4:

[0518] The server generates specific instructions using the analyzed contextual information. A conversion mechanism is used to transform the analysis results into clear action guidelines. A generative AI model is utilized in this process. The input is the analysis result, and the output is specific action guidelines. For example, it forms concrete steps in response to questions such as "What is the next step?"

[0519] Step 5:

[0520] A sentiment analysis system built into the server analyzes the emotional elements contained in the input data. This allows the system to understand the user's emotional state and reflect this information in the specific instructions it generates. The input is the user's text data, and the output is specific instructions that reflect their emotions. For example, if "anxiety" is detected, the system will generate instructions that provide a sense of reassurance.

[0521] Step 6:

[0522] The server sends specific instructions to the terminal, taking emotions into consideration. The terminal displays these instructions to the user, presenting the received data visually or audibly in an appropriate format. The input is specific instruction data from the server, and the output is the visual or audible presentation of instructions to the user. Specific actions on the terminal include displaying messages on the screen or playing audio through the speaker.

[0523] (Application Example 2)

[0524] 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."

[0525] Traditionally, system responses to ambiguous user instructions have been made without considering the user's emotional state, sometimes resulting in responses that don't align with the user's psychological condition. This raises concerns, especially in home robots, that users may experience stress. There is a need to achieve more personalized responses that take user emotions into account.

[0526] 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.

[0527] In this invention, the server includes data receiving means, data analysis means, data conversion means, emotion analysis means, and data display means. This enables specific and personalized responses to ambiguous instructions that reflect the user's emotional state.

[0528] A "data receiving means" is a device that has the function of receiving ambiguous instructions from a user.

[0529] A "data analysis device" is a device that has the function of analyzing ambiguous instructions received and understanding the context of those instructions.

[0530] A "data conversion means" is a device that has the function of converting analysis results into specific instructions.

[0531] An "emotional analysis device" is a device that has the function of analyzing the emotional state of a user.

[0532] A "data display means" is a device that has the function of adjusting and presenting instructions to the user based on their emotional state.

[0533] The system for realizing this invention will primarily be developed around a scenario in which a user uses a smart robot assistant in their home. The system will receive ambiguous instructions from the user via a program running on a terminal. A data receiving means can be used to collect the user's voice and text data. It is preferable to use low-power hardware such as a Raspberry Pi or Jetson Nano as the terminal.

[0534] The server uses the Google Cloud Speech-to-Text API to convert speech data into text as a data analysis tool. For natural language processing, it utilizes a BERT model powered by TensorFlow to analyze the context of ambiguous instructions received. The analyzed data is then used for sentiment analysis, evaluating the user's emotional state using Python libraries such as TextBlob and NLTK.

[0535] The server generates specific instructions through a data transformation mechanism, taking into account the results of sentiment analysis. In this process, it utilizes generative AI models such as GPT-3.5 to create personalized responses tailored to the user's emotions. The generated instructions are transmitted to the terminal via a data display mechanism and presented to the user via audio and visual means.

[0536] For example, if a user says, "I'm feeling down today," the server performs analysis and sentiment assessment, and generates a gentle, encouraging response such as, "It's okay to feel that way sometimes; maybe you should take a break." This response is delivered to the user through the device's speaker module.

[0537] An example of a prompt for a generative AI model is, "Consider the user's emotional state when responding to vague commands. If the user expresses sadness, generate a comforting response that includes actionable support." This prompt is used to generate a response that reflects the user's emotional state.

[0538] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0539] Step 1:

[0540] The user provides ambiguous instructions via voice input through a device. This input is recorded as voice data, which is then collected by a data receiving device.

[0541] Step 2:

[0542] The device sends voice data to the server. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in the voice input being output as text.

[0543] Step 3:

[0544] The server passes text data to a data analysis tool. A BERT model using TensorFlow is used to analyze the context of the text data and clarify the intent of the instructions. The analysis results in data that shows the specific intent of the instructions.

[0545] Step 4:

[0546] The analyzed data is sent to a sentiment analysis system. The server uses Python libraries such as TextBlob and NLTK to evaluate the emotional state within the text. An output in the form of a sentiment score is generated.

[0547] Step 5:

[0548] Based on the sentiment analysis results, the server creates specific instructions through data transformation mechanisms. Using a generative AI model, such as GPT-3.5, it generates personalized responses that match the user's emotions using prompt sentences. These responses are output as text data.

[0549] Step 6:

[0550] The final response is transmitted to the terminal via a data display device. The terminal then uses a speaker module to present the generated response to the user as audio.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] [Fourth Embodiment]

[0555] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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".

[0568] This invention is a system that converts ambiguous instructions into specific actions, and is realized by coordinating various devices. An example thereof is shown below.

[0569] The user inputs vague instructions into the system using a terminal. The terminal, such as a smartphone or personal computer, provides an interface for sending the input data to the server. While text input is the primary method, voice input is also possible.

[0570] The server receives ambiguous instructions sent from the terminal and analyzes them using an analysis device. The analysis device uses natural language processing technology to understand the context and intent of the instructions, and as a result extracts important keywords and phrases. This process clarifies ambiguous parts, allowing for more specific guidance.

[0571] Next, the server sends the obtained analysis results to the conversion device. The conversion device converts the analysis results into specific instructions and information. At this stage, it is also possible to improve the accuracy of the conversion results by using data from internal databases and related external information sources.

[0572] The converted specific instructions are sent from the server to the terminal. The terminal's display visually presents the results in a user-friendly format, allowing the user to quickly decide on their next action.

[0573] As a concrete example, consider internal corporate communication. When a user inputs the instruction "Prepare the monthly report," the server analyzes this vague instruction and generates specific tasks such as "Collect past monthly data" or "Add new report items." These tasks are then displayed on the terminal, allowing the user to perform specific actions.

[0574] Thus, by implementing the present invention, it is possible to clarify ambiguous instructions and provide an environment in which users can act efficiently.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The user enters vague instructions using the device. The device records the text and audio as digital data and prepares it for transmission to the server.

[0578] Step 2:

[0579] The terminal sends user instruction data to the server. The server receives this data and passes it on to the analysis device.

[0580] Step 3:

[0581] The server's analysis device analyzes the received instruction data. During this process, natural language processing techniques are used to extract key verbs and nouns from the text and understand the context.

[0582] Step 4:

[0583] The analysis device identifies the specific requirements for the instructions based on the analysis results and passes the results to the conversion device.

[0584] Step 5:

[0585] The server's conversion device translates analysis results into specific instructions and tasks. It improves the accuracy of the information by referencing internal databases and external information sources as needed.

[0586] Step 6:

[0587] The specific instructions that have been converted are sent from the server to the terminal. The terminal then presents the received content to the user.

[0588] Step 7:

[0589] The user checks the specific instructions displayed on the device and decides what action to take next. This allows the user to start responding immediately.

[0590] (Example 1)

[0591] 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".

[0592] In recent years, when users input instructions via terminals, there are many cases where their intentions are not accurately conveyed due to ambiguous expressions or ambiguous language. Such ambiguity can lead to problems that impair efficiency, as it prevents prompt responses to the specific actions the user intends to take. Furthermore, current systems lack sufficient additional information gathering and natural language processing to identify the user's intentions, resulting in cumbersome operation. Therefore, it is desirable to efficiently clarify ambiguous instructions, present information in an easy-to-understand manner for users, and provide an environment that allows them to take action quickly.

[0593] 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.

[0594] In this invention, the server includes a display terminal means for receiving ambiguous instructions from a user, a communication means for transmitting the ambiguous instructions in a format suitable for the processing target, and a statistical processing means for analyzing the ambiguous instructions and extracting key information. This makes it possible to convert ambiguous instructions into specific action commands and present them to the user quickly and accurately.

[0595] "Vague instructions from users" refer to expressions that do not clearly specify concrete actions or results, and are ambiguous and open to interpretation.

[0596] "Display terminal means" refers to a device used by users to input information or visually confirm results, and generally includes smartphones and personal computers.

[0597] "Communication methods" refer to technologies that use protocols and interfaces to transmit information from a source to a destination, and specifically include data transmission using the internet or local networks.

[0598] "Statistical processing methods" refer to processing methods that use statistical techniques to extract meaningful information from large amounts of data and perform data analysis and interpretation.

[0599] "Data conversion means" refers to a device or method that converts input data into a different format or structure and arranges it in a way that is suitable for a specific purpose.

[0600] "Means of information representation" refers to methods and technologies for presenting information in a format that is easy for users to understand, and includes displays using graphical user interfaces.

[0601] "Information gathering means" refers to the means of obtaining necessary information from external sources and preparing it in a format usable within the system.

[0602] "Statistical processing techniques for natural language" refer to technologies that analyze the language that people normally use using statistical approaches to understand its context and meaning.

[0603] This invention is a system that receives ambiguous instructions from a user and converts them into specific actions. This system includes a display terminal, a communication means, a statistical processing means, a data conversion means, and an information representation means.

[0604] Specifically, the user inputs ambiguous instructions into the system using a display terminal. This display terminal can be a smartphone or a personal computer. The terminal then transmits the input data to a server via a communication device. This communication can utilize the internet or a local network.

[0605] The server analyzes the received ambiguous instructions using statistical processing techniques. These techniques incorporate natural language processing technologies and machine learning models, with libraries such as TensorFlow and PyTorch being used as specific examples. The server uses these techniques to understand the context of the instructions and extract important keywords.

[0606] Subsequently, the server uses data conversion tools to convert the analysis results into specific action commands. During this process, information can be collected from internal databases and external sources to improve the accuracy of the commands. SQL queries and REST APIs are used for information gathering.

[0607] The converted specific action commands are then transmitted again from the server to the display terminal. The information representation means presents the converted commands in a visually easy-to-understand format for the user. This allows the user to quickly proceed to the next action.

[0608] For example, if a user enters the instruction "Prepare for tomorrow's team meeting," the server analyzes this vague instruction and generates specific tasks such as "Check the attendee list" and "Prepare meeting materials." These generated tasks are then displayed on the device, allowing the user to immediately perform these actions.

[0609] An example of a prompt to the generative AI model is, "Please translate vague instructions into specific tasks." This invention efficiently concretizes vague instructions and helps users make quick decisions about what to do.

[0610] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0611] Step 1:

[0612] The user inputs ambiguous instructions using a display terminal. The primary input method is text, and direct input is possible using a keyboard or microphone. This input data becomes basic information used in subsequent processing.

[0613] Step 2:

[0614] The terminal transmits information entered by the user to the server via a communication method. Encryption technology (SSL / TLS) is applied to ensure that the data is transmitted securely over the internet. The output of this process becomes the initial input data for processing by the server.

[0615] Step 3:

[0616] The server analyzes the received ambiguous instructions using statistical processing techniques. Natural language processing techniques are used to analyze words and phrases in the input text and understand the context. Specifically, machine learning models (e.g., BERT or GPT) are used to extract important keywords. The output of this process is a list of keywords as a result of the analysis.

[0617] Step 4:

[0618] The server converts the analysis results into specific action commands using data conversion means. It generates specific tasks while acquiring necessary information from internal databases and external information sources. For example, it forms action commands such as "send an email" or "aggregate data." The output of this process is a clearly defined set of action commands.

[0619] Step 5:

[0620] The server transmits the converted command back to the display terminal via communication means. The terminal's information display means visually displays this specific action command, presenting it in a way that allows the user to understand the next action to take. This final output is a concrete task display in a format that the user can confirm.

[0621] (Application Example 1)

[0622] 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".

[0623] In modern times, users of autonomous vehicles face the challenge of converting vague instructions into clear, precise, and specific action instructions when setting their destinations. Furthermore, a lack of necessary information for efficient transportation guidance makes it difficult to determine the optimal mode of transport and route to one's destination. To address these challenges, a user-friendly and intuitive system is required.

[0624] 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.

[0625] In this invention, the server includes terminal means for receiving ambiguous instructions from a user, language analysis means for analyzing the ambiguous instructions and understanding their context, and command conversion means for converting the analysis results obtained by the language analysis means into specific action instructions. As a result, users can obtain the necessary location information accurately and specific action instructions, including optimal route guidance, simply by inputting ambiguous instructions.

[0626] "Terminal means" refers to electronic devices used to receive ambiguous instructions from users, such as smartphones and computers.

[0627] A "language analysis device" is a device that uses natural language processing technology to analyze ambiguous instructions received and understand their context.

[0628] A "command conversion means" is a device that converts the analysis results obtained by the language analysis means into specific action instructions.

[0629] A "visualization means" is a display device that presents the converted, specific action instructions to the user.

[0630] A "transportation guidance system" refers to a system or device that provides travel guidance corresponding to a means of transportation.

[0631] "Information gathering means" refers to devices or processes for acquiring additional location information in accordance with specific instructions.

[0632] The system that realizes this application is one that can translate vague instructions given by users into concrete actions. The server plays a central role in this system, first receiving vague instructions from the user via a terminal device. The terminal device is an electronic device that the user can directly input, such as a smartphone or a personal computer.

[0633] The server then analyzes the received instructions using language analysis tools. Here, natural language processing techniques are used to understand the context and intent of the instructions. The analyzed instructions are then converted into specific action instructions by command conversion tools. During this process, data from databases and related external information sources are utilized to improve the accuracy of the converted instructions.

[0634] Ultimately, these instructions are presented to the user in an easily understandable format through visualization means. The system also includes transportation guidance means to provide information related to means of transport, ensuring that users receive appropriate travel guidance.

[0635] As a concrete example, a user riding in an autonomous vehicle might use their smartphone to give instructions such as, "I want to go to a nice cafe nearby." Upon receiving this instruction, the server analyzes it based on context and creates a list of nearby cafes. The user's past preference data and current location information are also taken into account to suggest the optimal destination. The transportation guidance system is integrated with the autonomous vehicle's navigation system and also provides the most efficient route.

[0636] Prompt phrases such as "cafe," "lunch," "quiet," and "Wi-Fi" are considered, and this system can use a generative AI model to provide more appropriate and personalized guidance.

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The user uses a device to input vague instructions. For example, they might input an instruction such as, "I want to go to a nice cafe nearby." The input data is collected as text or voice and sent to the server. At this stage, the input data arrives at the server as vague natural language data.

[0640] Step 2:

[0641] The server analyzes the received ambiguous instructions using language analysis tools. It receives ambiguous instructions as input, analyzes them using natural language processing techniques, and understands their context. Here, it analyzes the prompt sentence using a generative AI model and extracts important keywords such as "cafe" and "delicious." As a result, the analyzed intent is output.

[0642] Step 3:

[0643] The server uses a command conversion mechanism to convert the analysis results into specific action instructions. It receives the analysis results as input and identifies specific cafe candidates by referencing data from databases and external information sources. The candidate list obtained in this process is generated as output.

[0644] Step 4:

[0645] The server uses transportation guidance methods to provide the optimal route to the autonomous vehicle's navigation system. It receives specific action instructions as input and converts them into travel guidance corresponding to the means of transport. Estimated travel route and travel time information is output and guided to the autonomous vehicle.

[0646] Step 5:

[0647] The terminal uses visualization to present users with specific action instructions and directions. It receives specific action instructions and navigation information sent from the server as input and displays them visually. This allows users to confirm and select the optimal cafe option and how to get there.

[0648] 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.

[0649] This invention provides a system that offers more appropriate and personalized responses by taking into account the user's emotions when concretizing ambiguous instructions. An example thereof is shown below.

[0650] The user uses the terminal to input ambiguous instructions or questions. The terminal records or logs this input as digital data and prepares it for transmission to the server. Once the input is complete, the data is sent to the server.

[0651] The server passes the data received from the terminal to the analysis device. The analysis device uses natural language processing technology to analyze ambiguous instructions, understanding the context and clarifying the intent of the instructions. Based on the analysis results, key information is extracted, and specific instructions are formed.

[0652] A distinctive feature of this invention is the integration of an emotion engine into the server. The emotion engine analyzes the user's input data and the emotional expressions within it to understand the emotional state in which the user is issuing instructions. This emotional information is fed back into the analysis and conversion process, generating more specific instructions that are sensitive to the user's emotions.

[0653] Furthermore, specific instructions are generated by a conversion device, and an information acquisition device is activated to obtain additional information as needed. This additional information may be optimized based on the user's emotions.

[0654] For example, when a user enters "the project is behind schedule," if feelings of anxiety or stress are detected, the server will prioritize providing specific steps and support resources that can help resolve the problem.

[0655] The generated specific instructions are sent from the server to the terminal and displayed to the user. Here too, the tone and expression are adjusted according to the user's emotions, making it a more comfortable experience for the user.

[0656] By implementing this invention, flexible responses that take emotions into account can be obtained even in the face of ambiguous instructions, thereby improving the user experience.

[0657] The following describes the processing flow.

[0658] Step 1:

[0659] The user inputs vague instructions into the system using a terminal. The terminal records this input as text data and prepares it for transmission to the server.

[0660] Step 2:

[0661] The user's input data is sent from the terminal to the server. The server receives this data and immediately begins processing it.

[0662] Step 3:

[0663] The server passes the received data to the analysis device. The analysis device uses natural language processing technology to analyze ambiguous instructions and understand their intent. At this stage, the context of the instructions is clarified, and the necessary marquee information is extracted.

[0664] Step 4:

[0665] The server's emotion engine detects the user's emotions contained within the analyzed instructions. In this process, emotion analysis technology is used to analyze emotional expressions and tone from the user's input, and to infer their emotional state.

[0666] Step 5:

[0667] Based on the analysis results obtained from the analysis device and the emotional information from the emotion engine, the server's conversion device transforms the user's instructions into specific actions and information. The content and tone of the instructions are adjusted according to the emotional information.

[0668] Step 6:

[0669] The server sends the translated, specific instructions to the terminal. At this point, the terminal prepares to display the information in a way that takes the user's emotional state into account.

[0670] Step 7:

[0671] The device displays specific instructions to the user. Based on these instructions, the user can more easily decide on the appropriate course of action. This allows the user to proceed to the next step quickly and efficiently.

[0672] (Example 2)

[0673] 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".

[0674] Conventional systems for analyzing ambiguous instructions simply analyze and concretize instructions without considering the emotions of the user receiving them. This often leads to inappropriate responses that do not meet user expectations and a decrease in usability. Furthermore, providing information that ignores the user's emotional state can impair user satisfaction.

[0675] 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.

[0676] In this invention, the server includes input / output means, analysis means, conversion means, sentiment analysis means, and display means. This makes it possible to respond appropriately and specifically to ambiguous instructions while taking emotions into consideration, thereby realizing a more satisfying interaction for the user.

[0677] "Input / output means" refers to communication and interface devices for receiving ambiguous instructions from users and transmitting them to a server.

[0678] An "analysis tool" is a device that uses natural language processing technology to analyze ambiguous instructions received and clarify their context and intent.

[0679] A "conversion means" is a device that generates specific instructions based on the analysis results obtained by the analysis means.

[0680] An "emotional analysis tool" is a device that analyzes the emotional state contained in the user's instructions and reflects that information in specific instructions.

[0681] A "display means" is a device for presenting the generated specific instructions to the user.

[0682] This invention is a system that receives ambiguous instructions from users and converts them into specific instructions based on advanced analytical methods, including sentiment analysis. This enables the provision of personalized responses that take into account the user's emotional state.

[0683] The system primarily consists of terminals and servers. The terminals receive user input in either voice or text format. Voice input is converted to text using speech recognition technology. The terminals then transmit this digital data to the server.

[0684] The server processes the received data using an analysis device. The analysis device utilizes algorithms commonly used in natural language processing. For example, it uses BERT or similar models to understand the context of ambiguous instructions and clarify their intent. Based on the analysis results, a conversion device forms specific instructions.

[0685] The server incorporates an emotion analysis device that understands the user's emotional state from their input data. For example, it analyzes emotions such as "anxiety," and this information is reflected in specific instructions. This enables responses that are sensitive to the user's emotions, allowing for the presentation of appropriate solutions and suggestions.

[0686] As a concrete example, consider a scenario where a user inputs "I'm worried because the project is behind schedule" into the terminal. The emotion analysis device detects the emotion "worry" and feeds this information back into the analysis process. The conversion device uses this information to generate specific guidance, such as "Utilize resources to speed up the project and check the checklist."

[0687] Finally, the server sends the generated specific instructions to the terminal and displays them to the user. Here, the tone and expression are adjusted according to the user's emotions. For example, a message with a gentle tone such as, "Don't worry, please check out these resources," might be displayed.

[0688] An example of a prompt would be, "What kind of support do you need to alleviate my worries when my project is behind schedule?" In response to this prompt, the system can suggest specific measures, including emotional support.

[0689] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0690] Step 1:

[0691] The user uses the terminal to input ambiguous instructions or questions in voice or text format. The terminal uses speech recognition technology to convert voice input into text format. This converted digital data is then prepared for transmission to the server. The input is the user's natural language instructions, and the output is digital data in text format.

[0692] Step 2:

[0693] The terminal establishes a communication channel for sending digital data to the server and then sends that digital data to the server. The input is digital data in text format, and the output is the transmission of data to the server. Specifically, the terminal establishes a network connection and sends text data to the server according to a data transfer protocol.

[0694] Step 3:

[0695] The server passes the digital data received from the terminal to the analysis device. The analysis device uses a generative AI model to analyze the instructions using natural language processing technology. The analysis involves understanding the context and extracting important information. The input is the text data received by the server, and the output is the analyzed contextual information and important information. The analysis device performs natural language contextual analysis using a specific algorithm.

[0696] Step 4:

[0697] The server generates specific instructions using the analyzed contextual information. A conversion mechanism is used to transform the analysis results into clear action guidelines. A generative AI model is utilized in this process. The input is the analysis result, and the output is specific action guidelines. For example, it forms concrete steps in response to questions such as "What is the next step?"

[0698] Step 5:

[0699] A sentiment analysis system built into the server analyzes the emotional elements contained in the input data. This allows the system to understand the user's emotional state and reflect this information in the specific instructions it generates. The input is the user's text data, and the output is specific instructions that reflect their emotions. For example, if "anxiety" is detected, the system will generate instructions that provide a sense of reassurance.

[0700] Step 6:

[0701] The server sends specific instructions to the terminal, taking emotions into consideration. The terminal displays these instructions to the user, presenting the received data visually or audibly in an appropriate format. The input is specific instruction data from the server, and the output is the visual or audible presentation of instructions to the user. Specific actions on the terminal include displaying messages on the screen or playing audio through the speaker.

[0702] (Application Example 2)

[0703] 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".

[0704] Traditionally, system responses to ambiguous user instructions have been made without considering the user's emotional state, sometimes resulting in responses that don't align with the user's psychological condition. This raises concerns, especially in home robots, that users may experience stress. There is a need to achieve more personalized responses that take user emotions into account.

[0705] 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.

[0706] In this invention, the server includes data receiving means, data analysis means, data conversion means, emotion analysis means, and data display means. This enables specific and personalized responses to ambiguous instructions that reflect the user's emotional state.

[0707] A "data receiving means" is a device that has the function of receiving ambiguous instructions from a user.

[0708] A "data analysis device" is a device that has the function of analyzing ambiguous instructions received and understanding the context of those instructions.

[0709] A "data conversion means" is a device that has the function of converting analysis results into specific instructions.

[0710] An "emotional analysis device" is a device that has the function of analyzing the emotional state of a user.

[0711] A "data display means" is a device that has the function of adjusting and presenting instructions to the user based on their emotional state.

[0712] The system for realizing this invention will primarily be developed around a scenario in which a user uses a smart robot assistant in their home. The system will receive ambiguous instructions from the user via a program running on a terminal. A data receiving means can be used to collect the user's voice and text data. It is preferable to use low-power hardware such as a Raspberry Pi or Jetson Nano as the terminal.

[0713] The server uses the Google Cloud Speech-to-Text API to convert speech data into text as a data analysis tool. For natural language processing, it utilizes a BERT model powered by TensorFlow to analyze the context of ambiguous instructions received. The analyzed data is then used for sentiment analysis, evaluating the user's emotional state using Python libraries such as TextBlob and NLTK.

[0714] The server generates specific instructions through a data transformation mechanism, taking into account the results of sentiment analysis. In this process, it utilizes generative AI models such as GPT-3.5 to create personalized responses tailored to the user's emotions. The generated instructions are transmitted to the terminal via a data display mechanism and presented to the user via audio and visual means.

[0715] For example, if a user says, "I'm feeling down today," the server performs analysis and sentiment assessment, and generates a gentle, encouraging response such as, "It's okay to feel that way sometimes; maybe you should take a break." This response is delivered to the user through the device's speaker module.

[0716] An example of a prompt for a generative AI model is, "Consider the user's emotional state when responding to vague commands. If the user expresses sadness, generate a comforting response that includes actionable support." This prompt is used to generate a response that reflects the user's emotional state.

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] The user provides ambiguous instructions via voice input through a device. This input is recorded as voice data, which is then collected by a data receiving device.

[0720] Step 2:

[0721] The device sends voice data to the server. The server uses the Google Cloud Speech-to-Text API to convert the voice data into text data. This results in the voice input being output as text.

[0722] Step 3:

[0723] The server passes text data to a data analysis tool. A BERT model using TensorFlow is used to analyze the context of the text data and clarify the intent of the instructions. The analysis results in data that shows the specific intent of the instructions.

[0724] Step 4:

[0725] The analyzed data is sent to a sentiment analysis system. The server uses Python libraries such as TextBlob and NLTK to evaluate the emotional state within the text. An output in the form of a sentiment score is generated.

[0726] Step 5:

[0727] Based on the sentiment analysis results, the server creates specific instructions through data transformation mechanisms. Using a generative AI model, such as GPT-3.5, it generates personalized responses that match the user's emotions using prompt sentences. These responses are output as text data.

[0728] Step 6:

[0729] The final response is transmitted to the terminal via a data display device. The terminal then uses a speaker module to present the generated response to the user as audio.

[0730] 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.

[0731] 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.

[0732] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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."

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0751] The following is further disclosed regarding the embodiments described above.

[0752] (Claim 1)

[0753] An input device that receives ambiguous instructions from the user,

[0754] An analytical device that analyzes the aforementioned ambiguous instructions and understands their context,

[0755] A conversion device that converts the analysis results obtained from the aforementioned analysis device into specific instructions,

[0756] A display device that presents the aforementioned specific instructions to the user,

[0757] A system that includes this.

[0758] (Claim 2)

[0759] The system according to claim 1, further comprising an information acquisition device that acquires additional information in accordance with the aforementioned specific instructions.

[0760] (Claim 3)

[0761] The system according to claim 1, wherein the analysis device analyzes the context of ambiguous instructions using natural language processing technology.

[0762] "Example 1"

[0763] (Claim 1)

[0764] A display terminal means for receiving ambiguous instructions from the user,

[0765] A communication means for transmitting the aforementioned ambiguous instructions in a format suitable for the object to be processed,

[0766] A statistical processing means for analyzing the aforementioned ambiguous instructions and extracting key information,

[0767] A data conversion means that converts the analysis results into specific operation commands,

[0768] Information representation means that presents the aforementioned specific action commands to the user as visual information,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, further comprising information gathering means for acquiring relevant information based on the aforementioned specific operation commands.

[0772] (Claim 3)

[0773] The system according to claim 1, wherein statistical processing techniques for natural language are used to derive the aforementioned analysis results.

[0774] "Application Example 1"

[0775] (Claim 1)

[0776] A terminal device for receiving ambiguous instructions from users,

[0777] A language analysis means for analyzing the aforementioned ambiguous instructions and understanding their context,

[0778] A command conversion means that converts the analysis results obtained from the language analysis means into specific action instructions,

[0779] A visualization means for presenting the aforementioned specific action instructions to the user,

[0780] A transportation guidance means that provides travel guidance corresponding to the means of transport,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, further comprising information gathering means for obtaining additional location information in accordance with the aforementioned specific instructions.

[0784] (Claim 3)

[0785] The system according to claim 1, wherein the language analysis means analyzes the context of ambiguous instructions using natural language processing technology and aims to improve the analysis results by a generative artificial intelligence model.

[0786] "Example 2 of combining an emotion engine"

[0787] (Claim 1)

[0788] An input / output means for receiving ambiguous instructions from the user,

[0789] An analysis means for analyzing the aforementioned ambiguous instructions and understanding their context using natural language processing technology,

[0790] A conversion means that converts the analysis results obtained from the analysis means into specific instructions,

[0791] An emotion analysis means for analyzing the user's emotions and reflecting them in the specific instructions,

[0792] A display means for presenting the aforementioned specific instructions to the user,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, further comprising information acquisition means for acquiring additional information in accordance with the aforementioned specific instructions and optimizing based on emotional information.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein the emotion analysis means analyzes emotional expressions in the user's input data.

[0798] "Application example 2 when combining with an emotional engine"

[0799] (Claim 1)

[0800] A data receiving means for receiving ambiguous instructions from users,

[0801] A data analysis means for analyzing the aforementioned ambiguous instructions and understanding their context,

[0802] A data conversion means that converts the analysis results obtained from the aforementioned analysis device into specific instructions,

[0803] An emotion analysis tool for analyzing the emotional state of users,

[0804] A data display means that adjusts and presents the aforementioned specific instructions based on the user's emotions,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, further comprising information acquisition means for acquiring additional information in accordance with the aforementioned specific instructions and optimizing this information based on the user's emotions.

[0808] (Claim 3)

[0809] The system according to claim 1, wherein the data analysis means analyzes the context of ambiguous instructions using natural language processing technology. [Explanation of Symbols]

[0810] 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 terminal device for receiving ambiguous instructions from users, A language analysis means for analyzing the aforementioned ambiguous instructions and understanding their context, A command conversion means that converts the analysis results obtained from the language analysis means into specific action instructions, A visualization means for presenting the aforementioned specific action instructions to the user, A transportation guidance means that provides travel guidance corresponding to the means of transport, A system that includes this.

2. The system according to claim 1, further comprising information gathering means for acquiring additional location information in accordance with the aforementioned specific instructions.

3. The system according to claim 1, wherein the language analysis means analyzes the context of ambiguous instructions using natural language processing technology and aims to improve the analysis results by a generative artificial intelligence model.

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