system

Abstract

We provide the system. [Solution] A means for receiving operation requests from users in natural language, analyzing those operation requests, and extracting necessary information, A means for identifying multiple different artificial intelligence services based on extracted information and for instructing those artificial intelligence services to perform appropriate processing, A means of presenting the processing results obtained from the artificial intelligence service to the user and receiving correction instructions from the user, A means of instructing the artificial intelligence service to process again based on correction instructions from the user, A means for receiving product search requests from users in natural language at a commercial facility and extracting the product information, A means of obtaining product inventory information from a database and sending the obtained information to the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, users can carry out various creative activities by using various artificial intelligence services. However, since each service has different operation methods and interfaces, advanced knowledge and preparation are required to effectively use them. Such a situation becomes a factor that impairs the convenience for users, especially when using multiple services, the complexity increases. Therefore, there is a need for a technology that allows users to easily and efficiently use different artificial intelligence services.

Means for Solving the Problems

[0005] This invention provides a system that interprets natural language operation requests from users, identifies appropriate artificial intelligence services based on those requests, and issues instructions. This system includes means for analyzing user input and extracting necessary information, means for issuing instructions to the identified service, means for presenting results and accepting correction instructions, and means for issuing further instructions to the service based on the correction instructions. This allows users to intuitively and efficiently utilize the system without needing to understand different artificial intelligence services. Furthermore, it includes features for checking service costs and automatic parameter adjustment based on user feedback, enhancing convenience.

[0006] "Natural language" refers to the language that humans use on a daily basis, and it requires processing and analysis to be understood by artificial intelligence.

[0007] "Artificial intelligence services" are programming services that use computer systems to perform intelligent processing similar to that done by humans, and are used to automate specific tasks.

[0008] A "user" is an individual or legal entity that utilizes the system and is the entity that makes a request or performs an action through the artificial intelligence service.

[0009] An "interface" is a means for a user to interact with a computer system, a point of contact through which information is exchanged via input and output.

[0010] "Analysis" refers to the process of breaking down and examining data and information in detail, and then extracting the necessary elements and meanings from them.

[0011] A "parameter" is a variable or condition set to adjust the behavior or output of a system or algorithm.

[0012] "Feedback" refers to the act of users providing evaluations and opinions to a system, which are used to improve and adjust the system. [Brief explanation of the drawing]

[0013] [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] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that interprets operation requests entered by users in natural language and executes the requested tasks using appropriate artificial intelligence services. This system mainly consists of the following components.

[0035] First, users can instruct the system on specific tasks using natural language. For example, they can input a specific request such as "I want to turn a picture of my dog ​​into an anime style" into the user interface. This user interface is simple and intuitive, designed to allow users to operate it without stress.

[0036] Next, the terminal receives this input and sends it to the central server. The terminal preprocesses the input as needed and passes it to the server in the appropriate format.

[0037] The server analyzes the received input. It uses a natural language processing engine to understand the user's request and extract relevant elements, such as keywords like "image" and "convert to anime style."

[0038] Next, the server identifies an appropriate artificial intelligence service based on the analysis results and sends a request to it via an API. It then retrieves the processing results. This process could involve various AI services, such as image generation, speech translation, and text generation.

[0039] Once the service returns results, the server sends them to the terminal and presents them to the user. At this time, the server accepts user feedback on the results and makes corrections or reruns as needed.

[0040] For example, if a user provides feedback saying, "I don't like the result. I want the colors to be brighter," the server will reprocess the data based on that instruction and generate a new result.

[0041] Thus, the present invention enables flexible and rapid responses to user needs and the effective use of multiple artificial intelligence services. As a result, users can perform a variety of tasks with natural operation without having to understand the technical details of each service.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "I want to convert a picture of a cat into an oil painting style."

[0045] Step 2:

[0046] The terminal receives input from the user, formats and transforms the data, and then sends it to the server.

[0047] Step 3:

[0048] The server receives the user's request sent from the terminal.

[0049] Step 4:

[0050] The server uses a natural language processing engine to analyze the user's request. This extracts the necessary elements and parameters (keywords such as "cat image" and "oil painting style").

[0051] Step 5:

[0052] The server identifies the appropriate artificial intelligence service based on the analyzed information. In this case, it selects a service that supports image conversion.

[0053] Step 6:

[0054] The server sends a request to the identified artificial intelligence service via API. This request includes the original image file as necessary data and specifies the conversion parameters.

[0055] Step 7:

[0056] The server receives the processing results from the artificial intelligence service. Specifically, it retrieves image data that has been converted to an oil painting style.

[0057] Step 8:

[0058] The server sends the acquired results to the terminal and instructs it to present them to the user.

[0059] Step 9:

[0060] The user reviews the presented results and, if necessary, communicates correction requests to the server via their device.

[0061] Step 10:

[0062] The server receives feedback and correction instructions from the user and sends a request to the artificial intelligence service again with the new parameters. The process is completed by presenting the reprocessed results to the user again.

[0063] (Example 1)

[0064] 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."

[0065] Current systems utilizing artificial intelligence technology struggle to accurately analyze user input in natural language and select the optimal machine learning engine. Furthermore, they lack the ability to automatically adjust processes based on real-time user feedback and opinions, making it difficult to meet user expectations. Additionally, the lack of a way for users to inquire about machine learning engine costs beforehand can lead to financial concerns.

[0066] 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.

[0067] In this invention, the server includes means for receiving instructions in natural language from a user and analyzing those instructions; means for identifying multiple different machine learning engines based on the extracted elements and instructing them to perform calculations; and means for presenting the calculation results and instructing them to perform calculations again based on correction instructions from the user. This makes it possible to accurately recognize user requests and provide optimal processing. Furthermore, by generating prompt statements and sending them in a format suitable for machine learning engines, flexible processing based on user instructions becomes possible, thereby increasing user satisfaction.

[0068] A "user" refers to an individual or group that operates a system and inputs requests using natural language.

[0069] "Natural language" refers to the language that humans use on a daily basis, including instructions and requests expressed in speech and text.

[0070] "Instructions" refer to requests from a user to perform a specific task or action on the system.

[0071] "Analysis" refers to the process of understanding natural language instructions received from the user and extracting necessary elements and keywords.

[0072] An "element" is a piece of information extracted from natural language during the analysis process, and it is useful for selecting a machine learning engine.

[0073] A "machine learning engine" refers to a platform or model that uses artificial intelligence technology to automatically perform specific tasks.

[0074] "Computation" refers to the calculations and processes that a machine learning engine performs to carry out a given task.

[0075] "Presentation" refers to the act of displaying the calculation result to the user visually or audibly.

[0076] A "correction instruction" refers to a request from a user to modify or change the results presented.

[0077] A "prompt" refers to a sentence structured to provide instructions or requests in a format suitable for a machine learning engine.

[0078] This system consists of three main elements: the user, the terminal, and the server. First, the user inputs tasks into the terminal using natural language. For example, they can naturally describe specific requests such as "I want to turn a picture of my dog ​​into an anime style."

[0079] The terminal receives input from the user and sends it to the server. Here, the terminal preprocesses and formats the data. Natural language processing techniques are used for this preprocessing, particularly to extract specific elements intended by the user. A general-purpose computer, smartphone, or natural language processing library can be used for this.

[0080] When the server receives data sent from the terminal, it uses a natural language processing engine to analyze the input. This analysis extracts keywords such as "image" and "convert to anime style." Next, the server selects the most suitable machine learning engine and generates and sends an appropriate prompt message to it. If a generative AI model is used, the prompt will include instructions such as "convert the image of the dog to an anime style."

[0081] For example, a prompt to a generative AI model might include a natural language instruction such as, "Convert the following dog photo into an anime style." This prompt is crucial for the generative AI model to perform the expected processing.

[0082] The server receives results from the machine learning engine and presents them to the user via the terminal. When the user provides feedback on the results, the server reprocesses them based on that feedback. This allows for flexible responses based on user requests and enables the task to be performed with higher accuracy.

[0083] This embodiment allows users to easily perform a variety of tasks utilizing AI technology through natural instructions, even without specialized knowledge.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user inputs a specific task into the terminal using natural language. For example, they might input a request like, "I want to convert a picture of a dog into an anime style." This input is then passed to the terminal.

[0087] Step 2:

[0088] The terminal receives input from the user and performs natural language processing. It analyzes the input text and extracts necessary keywords such as "dog photos" and "convert to anime style." This processing generates structured data that allows the server to understand the request.

[0089] Step 3:

[0090] The terminal sends the pre-processed data to the server. Here, the structured data is passed to the server and handed over to the next processing step.

[0091] Step 4:

[0092] The server analyzes the data received from the terminal. Using a natural language processing engine, it determines the appropriate machine learning engine based on the identified keywords. Based on the analysis results, it generates prompt messages and prepares the system.

[0093] Step 5:

[0094] The server uses the generated prompt to send a request to the machine learning engine. By selecting a generating AI model and sending a prompt such as "Convert dog photos into an anime style," it starts the corresponding data calculation.

[0095] Step 6:

[0096] The server receives the computation results from the machine learning engine. For example, it retrieves converted anime-style image data. This result is held in preparation for being presented to the user in the next step.

[0097] Step 7:

[0098] The server formats the acquired calculation results and sends them to the terminal. The formatted data is then ready to be presented to the user.

[0099] Step 8:

[0100] The terminal receives data sent from the server and presents the results to the user visually. The user can review the presented results and rate their satisfaction level.

[0101] Step 9:

[0102] The user provides feedback on the results presented. For example, they can enter specific correction instructions such as, "I want it a little brighter."

[0103] Step 10:

[0104] The server generates a new prompt based on user feedback and resubmits the request to the machine learning engine. This process is repeated until the user is satisfied with the results.

[0105] (Application Example 1)

[0106] 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."

[0107] The problem that this invention aims to solve is to enable users in commercial facilities to quickly and intuitively obtain product information and inventory status. Conventional methods have the problem that product searches and inventory checks take time, and the convenience for users is not fully realized.

[0108] 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.

[0109] In this invention, the server includes means for receiving operation requests from users in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for receiving product search requests from users in natural language at commercial facilities and extracting product information. This enables users to instantly grasp product information and inventory status.

[0110] A "user" is an entity that makes operational requests or inquiries to a system and obtains information using natural language.

[0111] "Natural language operation requests" refer to operational instructions and inquiries using language that people use on a daily basis, and include specific requests to a system.

[0112] "Analysis" is the process of processing received natural language manipulation requests and extracting necessary information and keywords.

[0113] "Artificial intelligence services" refer to a group of AI technologies used to perform specific tasks, providing functions such as image processing, speech recognition, and text analysis.

[0114] A "commercial facility" is a place that provides goods and services, and includes shops, supermarkets, department stores, and so on.

[0115] A "product search request" is an operation request made by a user to obtain information about a specific product, such as specifying the product name or category to check inventory information.

[0116] "Inventory information" refers to information regarding the quantity, location, and replenishment status of products within a commercial facility.

[0117] A "database" is a collection of information in which a system stores data about products and inventory, and which can be accessed as needed.

[0118] The system implementing this invention is an application designed to expedite the provision of product information in commercial facilities.

[0119] The server receives product search requests in natural language sent from the user via their device. The received requests are analyzed using a natural language processing engine to extract keywords and intent. This analysis can utilize Google® Cloud Natural Language API or similar natural language processing technologies.

[0120] Based on the analyzed information, the server accesses the commercial facility's database to retrieve relevant product and inventory information. This database stores information such as product name, category, price, and inventory quantity, and is optimized for quick searching.

[0121] The acquired information is then sent back from the server to the user's terminal and presented to the user visually through a dedicated application. The application provides a responsive user interface, enabling intuitive operation for the user.

[0122] For example, if a user enters "Do you have organic fruit in stock?" into a smartphone app while in a store, the server analyzes the request and quickly retrieves the relevant information from the database. The app then displays a list of available fruits and related information on the screen.

[0123] Examples of prompts to input into a generative AI model:

[0124] "Analyze the user's input, 'Do you have {}_product_{}_in stock?', retrieve product inventory information from the relevant database, and return the result."

[0125] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0126] Step 1:

[0127] The terminal receives product search requests from the user in natural language. This input is obtained through the user interface. The terminal standardizes the input and prepares to send the request to the server over the network.

[0128] Step 2:

[0129] The server parses natural language requests received from the terminal. It receives the request text as input and performs analysis using a natural language processing engine such as the Google Cloud Natural Language API. Through this analysis, it extracts keywords related to product names and categories, and understands the intent. Based on these analysis results, it proceeds to the next step.

[0130] Step 3:

[0131] The server sends queries to the database based on the analyzed information to retrieve relevant product and inventory information. The database stores information such as product name, category, and inventory status. The input is keywords obtained from the analysis results, and the output is detailed information about the relevant product. The server extracts the most suitable product data based on the search criteria.

[0132] Step 4:

[0133] The server sends the retrieved product information to the terminal. The output product information is converted into a format that is easy for the user to understand. When sending to the terminal, the data is appropriately formatted via the communication protocol.

[0134] Step 5:

[0135] The terminal displays product information received from the server on the user interface. The information is visually organized and presented to the user in an easy-to-understand manner. For example, if a user checks the "stock of organic fruit," the terminal will display a list of the types of fruit and their stock status.

[0136] Step 6:

[0137] The user makes a decision based on the displayed information. Alternatively, they may enter further requests or feedback into the terminal to update information or check for other products. This input may return to step 1 and initiate a new process.

[0138] 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.

[0139] This invention is a system that interprets user requests in natural language, selects and executes appropriate artificial intelligence services accordingly, and further recognizes the user's emotions to optimize the response. This system has the following components.

[0140] First, the user inputs operation requests to the system in natural language through the interface. For example, a specific request might be, "I'm feeling down today, so please generate some relaxing music."

[0141] The terminal receives user input, formats the data, and then sends it to the server.

[0142] When the server receives user input, it uses a natural language processing engine to analyze the request and extract the necessary information. During this process, it uses an emotion recognition engine to identify emotions from the user's speech and text. For example, it might determine that the user is seeking relaxation.

[0143] Next, the server selects an appropriate artificial intelligence service based on the extracted information and emotional data. This allows the server to utilize a music generation service under defined conditions to generate music that suits the user's mood.

[0144] Once the AI ​​service processes the data, the server sends it to the terminal, presenting the results to the user. The user can then listen to or play the generated music.

[0145] Furthermore, users can provide feedback on the results. For example, they might request, "I'd like the song to be a bit more upbeat." The server receives this feedback and, along with the emotion recognition data, sends instructions to the artificial intelligence service again to adjust the output.

[0146] This invention allows users to obtain customized output according to their emotional state and to intuitively utilize a variety of artificial intelligence services. This improves the user experience and enables the provision of more personalized services.

[0147] The following describes the processing flow.

[0148] Step 1:

[0149] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "Please generate relaxing images."

[0150] Step 2:

[0151] The terminal receives input from the user, formats the text appropriately, and prepares it for transmission to the server.

[0152] Step 3:

[0153] The server receives text data sent from the terminal. Then, using natural language processing, it analyzes the text and extracts information related to the requested task (in this case, image generation).

[0154] Step 4:

[0155] The server uses an emotion recognition engine to analyze the emotional tone (such as relaxation) contained in the user's input. This allows the server to understand the user's current feelings and state.

[0156] Step 5:

[0157] The server selects an appropriate artificial intelligence service based on the analyzed task information and emotion data. In this case, it selects an image generation service that can produce images that emphasize relaxation.

[0158] Step 6:

[0159] The server sends a request to the selected artificial intelligence service via an API. The request includes user sentiment information as a generation condition.

[0160] Step 7:

[0161] The server receives the results generated from the artificial intelligence service. For example, the service provides an image that creates a relaxed atmosphere.

[0162] Step 8:

[0163] The server sends the generated image to the terminal and prepares the content to be presented.

[0164] Step 9:

[0165] The user reviews the generated image through their device. They can provide feedback requesting changes or adjustments as needed.

[0166] Step 10:

[0167] The server receives feedback from the user and sends instructions to the artificial intelligence service again, along with the emotion recognition results. The results are regenerated based on the new parameters and presented to the user.

[0168] (Example 2)

[0169] 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".

[0170] It is difficult to appropriately select diverse artificial intelligence functions based on natural language manipulation requests from users and to provide personalized output according to the user's emotional state. In addition, it is also a challenge to effectively reflect user requests for corrections and feedback on the generated results and improve the quality of the service. These challenges need to be addressed.

[0171] 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.

[0172] In this invention, the server includes means for receiving operation requests in natural language from the user, analyzing the operation requests to extract the content of the requests and the emotional state, selecting an appropriate artificial intelligence function based on the extracted information and emotional data, and using the artificial intelligence function to generate output that matches the user's emotional state, and presenting the processing results obtained from the artificial intelligence function to the user, accepting requests for corrections and additions from the user, and adjusting prompt sentences to the generated AI model. This enables the selection of an appropriate artificial intelligence function in response to the user's natural language requests, the generation of personalized output according to emotions, and continuous improvement of the output based on user feedback.

[0173] "User" refers to the person who operates the system and inputs requests using natural language.

[0174] "Natural language" refers to the language that people use on a daily basis, not a specific programming language, but the linguistic forms that humans normally use in conversation.

[0175] An "operation request" refers to a natural language expression of the specific actions or processes that a user wants the system to perform.

[0176] "Analysis" refers to the process by which a system syntactically and semantically understands a natural language request it receives and extracts the necessary information.

[0177] "Emotional state" refers to data that identifies the user's emotions, such as excitement, relaxation, or sadness.

[0178] "Artificial intelligence functions" refer to computer programs and algorithms that perform natural language processing or data generation according to specific purposes.

[0179] "Generation" refers to the process of creating new data or content using artificial intelligence capabilities.

[0180] "Requests for modification and addition" refers to new instructions from the user requesting changes or additions to existing processing results.

[0181] A "generative AI model" refers to an algorithm that is trained on large datasets, understands natural language, and is capable of responding to and generating responses.

[0182] A "prompt statement" refers to an instruction given to a generative AI model to cause it to perform a specific response or generate something.

[0183] This invention is a system that receives operation requests from the user in natural language, analyzes them to identify the user's emotional state, and generates personalized output. The system mainly consists of the user's terminal, a server that handles central processing, and a set of necessary algorithms.

[0184] The user first uses the terminal's interface to input requests to the system in natural language. This input is performed using a text-based user interface or speech recognition software. For example, a "general speech recognition engine" can be used as the speech recognition software.

[0185] When a user's request is received by the terminal, the data is formatted and converted into a format suitable for natural language processing. The formatted data is then sent to the server via digital communication technology. The server uses an advanced natural language processing engine and emotion recognition algorithms to analyze the request and determine the user's emotions. This allows the system to understand the user's emotional state and select the most appropriate service.

[0186] Next, the server selects the appropriate artificial intelligence function and generates output that matches the user's emotions. For example, if the user requests to "relax," it uses a music generation algorithm to generate relaxation music. The generated music is sent back to the terminal and presented to the user.

[0187] Furthermore, users can provide feedback on the results. For example, they might input feedback such as, "I'd like to hear more lively music." This feedback is sent to the generating AI model as a prompt. A concrete example of a prompt might be, "The user wants relaxing music. Please generate music using melodies and tempos that reduce stress." The server analyzes this feedback and readjusts the output using the generating AI model.

[0188] Thus, the present invention aims to provide users with an optimal experience by offering a variety of artificial intelligence services based on their emotions.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user inputs operation requests in natural language through the terminal's interface. These inputs include specific requests such as "Please generate relaxing music." The input data is stored on the terminal as text data.

[0192] Step 2:

[0193] The terminal formats the input natural language data. Specifically, it performs noise reduction and standardizes character encoding formats, converting it into a format that can be parsed by a natural language processing engine. The formatted data is then ready to be sent to the next step.

[0194] Step 3:

[0195] The terminal sends the formatted data to the server. This transmission is performed using cloud communication technology, and the data reaches the server in real time. The server then passes the received data to a natural language processing engine.

[0196] Step 4:

[0197] The server analyzes the received natural language data using a natural language processing engine, understanding the content of the operation request through grammatical and contextual analysis. During this process, it extracts necessary information using a generative AI model and identifies the user's intent. The analyzed request content is then moved on to the next processing step.

[0198] Step 5:

[0199] The server uses an emotion recognition engine to determine the user's emotional state. Based on keywords and context extracted from natural language data, it applies an emotion analysis algorithm to determine what the user wants. The output emotion data is used for service selection.

[0200] Step 6:

[0201] The server selects the appropriate artificial intelligence function based on the analyzed request content and emotion data. For example, if it determines that the user is seeking "relaxation," it will select a music generation algorithm. The selected artificial intelligence function will then generate output that corresponds to the user's request.

[0202] Step 7:

[0203] The server executes selected artificial intelligence functions to generate output that matches the user's emotional state. Specifically, it uses a generative AI model to create relaxation music. In this process, pre-configured prompt sentences are given to the generative AI model as input, and music data is obtained as output.

[0204] Step 8:

[0205] The server sends the generated music data to the terminal. The terminal presents the received music data to the user and prepares the system for music playback.

[0206] Step 9:

[0207] Users listen to the presented music and provide feedback as needed. This feedback may include specific requests such as, "I'd like the music to be more lively."

[0208] Step 10:

[0209] The device sends the user feedback back to the server. The server receives this feedback, adjusts the prompt text, and inputs it back into the AI ​​model to obtain a new output. This process provides a more optimized output tailored to the user's requests.

[0210] (Application Example 2)

[0211] 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".

[0212] Traditional AI-powered systems sometimes struggled to analyze users' natural language requests and provide appropriate services. Furthermore, they lacked the ability to generate and deliver media that considered user emotions. As a result, personalized services that users desired were not adequately provided, leading to decreased user satisfaction.

[0213] 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.

[0214] In this invention, the server includes means for receiving operation requests from a user in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for analyzing the user's emotions and selecting media types based on the emotion data. This makes it possible to generate and provide media that responds to the user's emotions.

[0215] "Natural language" refers to the language that humans use on a daily basis, and is a means of conveying intentions to a system through speech or text.

[0216] An "operation request" is a request made by a user to a system to perform a specific action or process.

[0217] An "artificial intelligence service" is software or a system that uses machine learning and data processing technologies to analyze data or perform specific tasks based on user input.

[0218] "Sentiment analysis" is the process of identifying emotions from a user's speech or text and recognizing them as digital data.

[0219] "Media type" refers to different forms of content, such as music and videos, and is selected based on user needs and emotions.

[0220] The system for implementing this invention consists of three components: a server, a terminal, and a user. First, the user inputs emotions and requests into the terminal using natural language. The terminal then formats these requests into digital data and sends it to the server.

[0221] The server analyzes the received data and extracts the necessary information using a natural language processing engine. The sentiment analysis engine identifies emotions from the user's input and selects the most suitable artificial intelligence service based on that data. This selection uses a generative AI model to generate or recommend media types such as music and videos that match the user's emotions.

[0222] Media content provided by the server is presented to the user via the terminal. The user provides feedback on this content, and this feedback is sent back to the server. Based on the feedback, the server adjusts the parameters of the generated media and repeatedly provides content optimized for the user.

[0223] For example, if a user types "I want to listen to relaxing music today" into their smartphone, the server receives this request, generates relaxing music through sentiment analysis and natural language processing, and presents it to the user. Furthermore, if the user provides feedback such as "Please speed up the tempo a bit," the server incorporates this feedback, adjusts the music's tempo, and provides the content again.

[0224] Examples of prompt statements include the following:

[0225] "How are you feeling?", "I want to relax today." → Generated prompt: "Generating relaxing music and recommending videos that match your mood."

[0226] The main software used includes natural language processing engines and sentiment analysis engines, and by integrating these, it is possible to generate and deliver media that meets user requirements.

[0227] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0228] Step 1:

[0229] The user uses a device such as a smartphone or tablet to input a request in natural language, such as requesting relaxing music. This request is sent to the device as voice or text data.

[0230] Step 2:

[0231] The terminal converts user input into digital data and formats it for transmission to the server. Specifically, in the case of voice input, it performs speech recognition and converts it into text. The formatted text data is then sent to the server.

[0232] Step 3:

[0233] The server analyzes the received user request data using a natural language processing engine. At this stage, it extracts important keywords and phrases from the request to clarify the user's intent. As output, it generates data that indicates the intent of the request.

[0234] Step 4:

[0235] The server uses an emotion analysis engine based on the intent of the request to identify the user's emotions. The emotion data output will include emotional states such as "seeking relaxation."

[0236] Step 5:

[0237] Based on the extracted intent and emotion data, the server uses a generative AI model to select and generate the optimal media type (e.g., music). Music data that matches the user's emotions is output.

[0238] Step 6:

[0239] Music data generated from the server is sent to the terminal and played back by the user. By listening to the presented music, the user can enjoy content that matches their emotions.

[0240] Step 7:

[0241] When a user enters feedback about music, the device sends that feedback to the server. The specific feedback (for example, "Please speed up the tempo") is then formatted as data.

[0242] Step 8:

[0243] The server analyzes user feedback and regenerates music data based on the feedback data. The generation AI model generates the newly adjusted music data and sends it to the device.

[0244] Step 9:

[0245] The device provides the user with regenerated music data, allowing them to enjoy music that has been adjusted based on their feedback.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] [Second Embodiment]

[0250] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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".

[0262] This invention is a system that interprets operation requests entered by users in natural language and executes the requested tasks using appropriate artificial intelligence services. This system mainly consists of the following components.

[0263] First, users can instruct the system on specific tasks using natural language. For example, they can input a specific request such as "I want to turn a picture of my dog ​​into an anime style" into the user interface. This user interface is simple and intuitive, designed to allow users to operate it without stress.

[0264] Next, the terminal receives this input and sends it to the central server. The terminal preprocesses the input as needed and passes it to the server in the appropriate format.

[0265] The server analyzes the received input. It uses a natural language processing engine to understand the user's request and extract relevant elements, such as keywords like "image" and "convert to anime style."

[0266] Next, the server identifies an appropriate artificial intelligence service based on the analysis results and sends a request to it via an API. It then retrieves the processing results. This process could involve various AI services, such as image generation, speech translation, and text generation.

[0267] Once the service returns results, the server sends them to the terminal and presents them to the user. At this time, the server accepts user feedback on the results and makes corrections or reruns as needed.

[0268] For example, if a user provides feedback saying, "I don't like the result. I want the colors to be brighter," the server will reprocess the data based on that instruction and generate a new result.

[0269] Thus, the present invention enables flexible and rapid responses to user needs and the effective use of multiple artificial intelligence services. As a result, users can perform a variety of tasks with natural operation without having to understand the technical details of each service.

[0270] The following describes the processing flow.

[0271] Step 1:

[0272] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "I want to convert a picture of a cat into an oil painting style."

[0273] Step 2:

[0274] The terminal receives an input from the user, performs data formatting and format conversion, and then sends it to the server.

[0275] Step 3:

[0276] The server receives the user's request sent from the terminal.

[0277] Step 4:

[0278] The server uses a natural language processing engine to analyze the user's operation request. As a result, necessary elements and parameters (keywords such as "a picture of a cat" and "in the style of an oil painting") are extracted.

[0279] Step 5:

[0280] Based on the analyzed information, the server identifies an appropriate artificial intelligence service. In this case, a service corresponding to image conversion is selected.

[0281] Step 6:

[0282] The server sends a request to the identified artificial intelligence service via the API. Here, the original image file is included as the necessary data, and conversion parameters are specified.

[0283] Step 7:

[0284] The server receives the processing result from the artificial intelligence service. Specifically, it obtains the image data converted into the style of an oil painting.

[0285] Step 8:

[0286] The server sends the obtained result to the terminal and instructs it to present it to the user.

[0287] Step 9:

[0288] The user reviews the presented results and, if necessary, communicates correction requests to the server via their device.

[0289] Step 10:

[0290] The server receives feedback and correction instructions from the user and sends a request to the artificial intelligence service again with the new parameters. The process is completed by presenting the reprocessed results to the user again.

[0291] (Example 1)

[0292] 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."

[0293] Current systems utilizing artificial intelligence technology struggle to accurately analyze user input in natural language and select the optimal machine learning engine. Furthermore, they lack the ability to automatically adjust processes based on real-time user feedback and opinions, making it difficult to meet user expectations. Additionally, the lack of a way for users to inquire about machine learning engine costs beforehand can lead to financial concerns.

[0294] 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.

[0295] In this invention, the server includes means for receiving instructions in natural language from a user and analyzing those instructions; means for identifying multiple different machine learning engines based on the extracted elements and instructing them to perform calculations; and means for presenting the calculation results and instructing them to perform calculations again based on correction instructions from the user. This makes it possible to accurately recognize user requests and provide optimal processing. Furthermore, by generating prompt statements and sending them in a format suitable for machine learning engines, flexible processing based on user instructions becomes possible, thereby increasing user satisfaction.

[0296] A "user" refers to an individual or group that operates a system and inputs requests using natural language.

[0297] "Natural language" refers to the language that humans use on a daily basis, including instructions and requests expressed in speech and text.

[0298] "Instructions" refer to requests from a user to perform a specific task or action on the system.

[0299] "Analysis" refers to the process of understanding natural language instructions received from the user and extracting necessary elements and keywords.

[0300] An "element" is a piece of information extracted from natural language during the analysis process, and it is useful for selecting a machine learning engine.

[0301] A "machine learning engine" refers to a platform or model that uses artificial intelligence technology to automatically perform specific tasks.

[0302] "Computation" refers to the calculations and processes that a machine learning engine performs to carry out a given task.

[0303] "Presentation" refers to the act of displaying the calculation result to the user visually or audibly.

[0304] A "correction instruction" refers to a request from a user to modify or change the results presented.

[0305] A "prompt" refers to a sentence structured to provide instructions or requests in a format suitable for a machine learning engine.

[0306] This system consists of three main elements: the user, the terminal, and the server. First, the user inputs tasks into the terminal using natural language. For example, they can naturally describe specific requests such as "I want to turn a picture of my dog ​​into an anime style."

[0307] The terminal receives input from the user and sends it to the server. Here, the terminal plays a role in pre - processing the data and formatting it. Natural language processing technology is used for this pre - processing, especially useful for extracting specific elements intended by the user. Here, general computers, smartphones, and natural language processing libraries can be utilized.

[0308] When the server receives the data sent from the terminal, it utilizes a natural language processing engine to analyze the input. Through this analysis, keywords such as "image" and "convert to anime style" are extracted. Next, the server selects an optimal machine learning engine and generates and sends an appropriate prompt text for it. When a generative AI model is used, the prompt includes an instruction like "Convert the image of a dog to anime style."

[0309] For example, as a prompt text for the generative AI model, it includes a natural language instruction like "Please convert the following photo of a dog into an anime style." This prompt text is important for the generative AI model to perform the expected processing.

[0310] The server receives the result from the machine learning engine and presents it to the user via the terminal. When the user provides feedback on the result, the server performs re - processing based on that feedback. This enables flexible response based on the user's requirements and allows tasks to be executed with higher accuracy.

[0311] With this embodiment, even without specialized knowledge, the user can easily execute various tasks utilizing AI technology with natural instructions.

[0312] The flow of the specific process in Example 1 will be described using FIG. 11.

[0313] Step 1:

[0314] The user inputs a specific task into the terminal using natural language. For example, they might input a request like, "I want to convert a picture of a dog into an anime style." This input is then passed to the terminal.

[0315] Step 2:

[0316] The terminal receives input from the user and performs natural language processing. It analyzes the input text and extracts necessary keywords such as "dog photos" and "convert to anime style." This processing generates structured data that allows the server to understand the request.

[0317] Step 3:

[0318] The terminal sends the pre-processed data to the server. Here, the structured data is passed to the server and handed over to the next processing step.

[0319] Step 4:

[0320] The server analyzes the data received from the terminal. Using a natural language processing engine, it determines the appropriate machine learning engine based on the identified keywords. Based on the analysis results, it generates prompt messages and prepares the system.

[0321] Step 5:

[0322] The server uses the generated prompt to send a request to the machine learning engine. By selecting a generating AI model and sending a prompt such as "Convert dog photos into an anime style," it starts the corresponding data calculation.

[0323] Step 6:

[0324] The server receives the computation results from the machine learning engine. For example, it retrieves converted anime-style image data. This result is held in preparation for being presented to the user in the next step.

[0325] Step 7:

[0326] The server formats the acquired calculation results and sends them to the terminal. The formatted data is then ready to be presented to the user.

[0327] Step 8:

[0328] The terminal receives data sent from the server and presents the results to the user visually. The user can review the presented results and rate their satisfaction level.

[0329] Step 9:

[0330] The user provides feedback on the results presented. For example, they can enter specific correction instructions such as, "I want it a little brighter."

[0331] Step 10:

[0332] The server generates a new prompt based on user feedback and resubmits the request to the machine learning engine. This process is repeated until the user is satisfied with the results.

[0333] (Application Example 1)

[0334] 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."

[0335] The problem that this invention aims to solve is to enable users in commercial facilities to quickly and intuitively obtain product information and inventory status. Conventional methods have the problem that product searches and inventory checks take time, and the convenience for users is not fully realized.

[0336] 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.

[0337] In this invention, the server includes means for receiving operation requests from users in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for receiving product search requests from users in natural language at commercial facilities and extracting product information. This enables users to instantly grasp product information and inventory status.

[0338] A "user" is an entity that makes operational requests or inquiries to a system and obtains information using natural language.

[0339] "Natural language operation requests" refer to operational instructions and inquiries using language that people use on a daily basis, and include specific requests to a system.

[0340] "Analysis" is the process of processing received natural language manipulation requests and extracting necessary information and keywords.

[0341] "Artificial intelligence services" refer to a group of AI technologies used to perform specific tasks, providing functions such as image processing, speech recognition, and text analysis.

[0342] A "commercial facility" is a place that provides goods and services, and includes shops, supermarkets, department stores, and so on.

[0343] A "product search request" is an operation request made by a user to obtain information about a specific product, such as specifying the product name or category to check inventory information.

[0344] "Inventory information" refers to information regarding the quantity, location, and replenishment status of products within a commercial facility.

[0345] A "database" is a collection of information in which a system stores data about products and inventory, and which can be accessed as needed.

[0346] The system implementing this invention is an application designed to expedite the provision of product information in commercial facilities.

[0347] The server receives product search requests in natural language sent from users via their devices. These requests are analyzed using a natural language processing engine to extract keywords and intent. This analysis can utilize Google Cloud Natural Language API or similar natural language processing technologies.

[0348] Based on the analyzed information, the server accesses the commercial facility's database to retrieve relevant product and inventory information. This database stores information such as product name, category, price, and inventory quantity, and is optimized for quick searching.

[0349] The acquired information is then sent back from the server to the user's terminal and presented to the user visually through a dedicated application. The application provides a responsive user interface, enabling intuitive operation for the user.

[0350] For example, if a user enters "Do you have organic fruit in stock?" into a smartphone app while in a store, the server analyzes the request and quickly retrieves the relevant information from the database. The app then displays a list of available fruits and related information on the screen.

[0351] Examples of prompts to input into a generative AI model:

[0352] "Analyze the user's input, 'Do you have {}_product_{}_in stock?', retrieve product inventory information from the relevant database, and return the result."

[0353] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0354] Step 1:

[0355] The terminal receives product search requests from the user in natural language. This input is obtained through the user interface. The terminal standardizes the input and prepares to send the request to the server over the network.

[0356] Step 2:

[0357] The server parses natural language requests received from the terminal. It receives the request text as input and performs analysis using a natural language processing engine such as the Google Cloud Natural Language API. Through this analysis, it extracts keywords related to product names and categories, and understands the intent. Based on these analysis results, it proceeds to the next step.

[0358] Step 3:

[0359] The server sends queries to the database based on the analyzed information to retrieve relevant product and inventory information. The database stores information such as product name, category, and inventory status. The input is keywords obtained from the analysis results, and the output is detailed information about the relevant product. The server extracts the most suitable product data based on the search criteria.

[0360] Step 4:

[0361] The server sends the retrieved product information to the terminal. The output product information is converted into a format that is easy for the user to understand. When sending to the terminal, the data is appropriately formatted via the communication protocol.

[0362] Step 5:

[0363] The terminal displays product information received from the server on the user interface. The information is visually organized and presented to the user in an easy-to-understand manner. For example, if a user checks the "stock of organic fruit," the terminal will display a list of the types of fruit and their stock status.

[0364] Step 6:

[0365] The user makes a decision based on the displayed information. Alternatively, they may enter further requests or feedback into the terminal to update information or check for other products. This input may return to step 1 and initiate a new process.

[0366] 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.

[0367] This invention is a system that interprets user requests in natural language, selects and executes appropriate artificial intelligence services accordingly, and further recognizes the user's emotions to optimize the response. This system has the following components.

[0368] First, the user inputs operation requests to the system in natural language through the interface. For example, a specific request might be, "I'm feeling down today, so please generate some relaxing music."

[0369] The terminal receives user input, formats the data, and then sends it to the server.

[0370] When the server receives user input, it uses a natural language processing engine to analyze the request and extract the necessary information. During this process, it uses an emotion recognition engine to identify emotions from the user's speech and text. For example, it might determine that the user is seeking relaxation.

[0371] Next, the server selects an appropriate artificial intelligence service based on the extracted information and emotional data. This allows the server to utilize a music generation service under defined conditions to generate music that suits the user's mood.

[0372] Once the AI ​​service processes the data, the server sends it to the terminal, presenting the results to the user. The user can then listen to or play the generated music.

[0373] Furthermore, users can provide feedback on the results. For example, they might request, "I'd like the song to be a bit more upbeat." The server receives this feedback and, along with the emotion recognition data, sends instructions to the artificial intelligence service again to adjust the output.

[0374] This invention allows users to obtain customized output according to their emotional state and to intuitively utilize a variety of artificial intelligence services. This improves the user experience and enables the provision of more personalized services.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "Please generate relaxing images."

[0378] Step 2:

[0379] The terminal receives input from the user, formats the text appropriately, and prepares it for transmission to the server.

[0380] Step 3:

[0381] The server receives text data sent from the terminal. Then, using natural language processing, it analyzes the text and extracts information related to the requested task (in this case, image generation).

[0382] Step 4:

[0383] The server uses an emotion recognition engine to analyze the emotional tone (such as relaxation) contained in the user's input. This allows the server to understand the user's current feelings and state.

[0384] Step 5:

[0385] The server selects an appropriate artificial intelligence service based on the analyzed task information and emotion data. In this case, it selects an image generation service that can produce images that emphasize relaxation.

[0386] Step 6:

[0387] The server sends a request to the selected artificial intelligence service via an API. The request includes user sentiment information as a generation condition.

[0388] Step 7:

[0389] The server receives the results generated from the artificial intelligence service. For example, the service provides an image that creates a relaxed atmosphere.

[0390] Step 8:

[0391] The server sends the generated image to the terminal and prepares the content to be presented.

[0392] Step 9:

[0393] The user reviews the generated image through their device. They can provide feedback requesting changes or adjustments as needed.

[0394] Step 10:

[0395] The server receives feedback from the user and sends instructions to the artificial intelligence service again, along with the emotion recognition results. The results are regenerated based on the new parameters and presented to the user.

[0396] (Example 2)

[0397] 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".

[0398] It is difficult to appropriately select diverse artificial intelligence functions based on natural language manipulation requests from users and to provide personalized output according to the user's emotional state. In addition, it is also a challenge to effectively reflect user requests for corrections and feedback on the generated results and improve the quality of the service. These challenges need to be addressed.

[0399] 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.

[0400] In this invention, the server includes means for receiving operation requests in natural language from the user, analyzing the operation requests to extract the content of the requests and the emotional state, selecting an appropriate artificial intelligence function based on the extracted information and emotional data, and using the artificial intelligence function to generate output that matches the user's emotional state, and presenting the processing results obtained from the artificial intelligence function to the user, accepting requests for corrections and additions from the user, and adjusting prompt sentences to the generated AI model. This enables the selection of an appropriate artificial intelligence function in response to the user's natural language requests, the generation of personalized output according to emotions, and continuous improvement of the output based on user feedback.

[0401] "User" refers to the person who operates the system and inputs requests using natural language.

[0402] "Natural language" refers to the language that people use on a daily basis, not a specific programming language, but the linguistic forms that humans normally use in conversation.

[0403] An "operation request" refers to a natural language expression of the specific actions or processes that a user wants the system to perform.

[0404] "Analysis" refers to the process by which a system syntactically and semantically understands a natural language request it receives and extracts the necessary information.

[0405] "Emotional state" refers to data that identifies the user's emotions, such as excitement, relaxation, or sadness.

[0406] "Artificial intelligence functions" refer to computer programs and algorithms that perform natural language processing or data generation according to specific purposes.

[0407] "Generation" refers to the process of creating new data or content using artificial intelligence capabilities.

[0408] "Requests for modification and addition" refers to new instructions from the user requesting changes or additions to existing processing results.

[0409] A "generative AI model" refers to an algorithm that is trained on large datasets, understands natural language, and is capable of responding to and generating responses.

[0410] A "prompt statement" refers to an instruction given to a generative AI model to cause it to perform a specific response or generate something.

[0411] This invention is a system that receives operation requests from the user in natural language, analyzes them to identify the user's emotional state, and generates personalized output. The system mainly consists of the user's terminal, a server that handles central processing, and a set of necessary algorithms.

[0412] The user first uses the terminal's interface to input requests to the system in natural language. This input is performed using a text-based user interface or speech recognition software. For example, a "general speech recognition engine" can be used as the speech recognition software.

[0413] When a user's request is received by the terminal, the data is formatted and converted into a format suitable for natural language processing. The formatted data is then sent to the server via digital communication technology. The server uses an advanced natural language processing engine and emotion recognition algorithms to analyze the request and determine the user's emotions. This allows the system to understand the user's emotional state and select the most appropriate service.

[0414] Next, the server selects the appropriate artificial intelligence function and generates output that matches the user's emotions. For example, if the user requests to "relax," it uses a music generation algorithm to generate relaxation music. The generated music is sent back to the terminal and presented to the user.

[0415] Furthermore, users can provide feedback on the results. For example, they might input feedback such as, "I'd like to hear more lively music." This feedback is sent to the generating AI model as a prompt. A concrete example of a prompt might be, "The user wants relaxing music. Please generate music using melodies and tempos that reduce stress." The server analyzes this feedback and readjusts the output using the generating AI model.

[0416] Thus, the present invention aims to provide users with an optimal experience by offering a variety of artificial intelligence services based on their emotions.

[0417] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0418] Step 1:

[0419] The user inputs operation requests in natural language through the terminal's interface. These inputs include specific requests such as "Please generate relaxing music." The input data is stored on the terminal as text data.

[0420] Step 2:

[0421] The terminal formats the input natural language data. Specifically, it performs noise reduction and standardizes character encoding formats, converting it into a format that can be parsed by a natural language processing engine. The formatted data is then ready to be sent to the next step.

[0422] Step 3:

[0423] The terminal sends the formatted data to the server. This transmission is performed using cloud communication technology, and the data reaches the server in real time. The server then passes the received data to a natural language processing engine.

[0424] Step 4:

[0425] The server analyzes the received natural language data using a natural language processing engine, understanding the content of the operation request through grammatical and contextual analysis. During this process, it extracts necessary information using a generative AI model and identifies the user's intent. The analyzed request content is then moved on to the next processing step.

[0426] Step 5:

[0427] The server uses an emotion recognition engine to determine the user's emotional state. Based on keywords and context extracted from natural language data, it applies an emotion analysis algorithm to determine what the user wants. The output emotion data is used for service selection.

[0428] Step 6:

[0429] The server selects the appropriate artificial intelligence function based on the analyzed request content and emotion data. For example, if it determines that the user is seeking "relaxation," it will select a music generation algorithm. The selected artificial intelligence function will then generate output that corresponds to the user's request.

[0430] Step 7:

[0431] The server executes selected artificial intelligence functions to generate output that matches the user's emotional state. Specifically, it uses a generative AI model to create relaxation music. In this process, pre-configured prompt sentences are given to the generative AI model as input, and music data is obtained as output.

[0432] Step 8:

[0433] The server sends the generated music data to the terminal. The terminal presents the received music data to the user and prepares the system for music playback.

[0434] Step 9:

[0435] Users listen to the presented music and provide feedback as needed. This feedback may include specific requests such as, "I'd like the music to be more lively."

[0436] Step 10:

[0437] The device sends the user feedback back to the server. The server receives this feedback, adjusts the prompt text, and inputs it back into the AI ​​model to obtain a new output. This process provides a more optimized output tailored to the user's requests.

[0438] (Application Example 2)

[0439] 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."

[0440] Traditional AI-powered systems sometimes struggled to analyze users' natural language requests and provide appropriate services. Furthermore, they lacked the ability to generate and deliver media that considered user emotions. As a result, personalized services that users desired were not adequately provided, leading to decreased user satisfaction.

[0441] 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.

[0442] In this invention, the server includes means for receiving operation requests from a user in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for analyzing the user's emotions and selecting media types based on the emotion data. This makes it possible to generate and provide media that responds to the user's emotions.

[0443] "Natural language" refers to the language that humans use on a daily basis, and is a means of conveying intentions to a system through speech or text.

[0444] An "operation request" is a request made by a user to a system to perform a specific action or process.

[0445] An "artificial intelligence service" is software or a system that uses machine learning and data processing technologies to analyze data or perform specific tasks based on user input.

[0446] "Sentiment analysis" is the process of identifying emotions from a user's speech or text and recognizing them as digital data.

[0447] "Media type" refers to different forms of content, such as music and videos, and is selected based on user needs and emotions.

[0448] The system for implementing this invention consists of three components: a server, a terminal, and a user. First, the user inputs emotions and requests into the terminal using natural language. The terminal then formats these requests into digital data and sends it to the server.

[0449] The server analyzes the received data and extracts the necessary information using a natural language processing engine. The sentiment analysis engine identifies emotions from the user's input and selects the most suitable artificial intelligence service based on that data. This selection uses a generative AI model to generate or recommend media types such as music and videos that match the user's emotions.

[0450] Media content provided by the server is presented to the user via the terminal. The user provides feedback on this content, and this feedback is sent back to the server. Based on the feedback, the server adjusts the parameters of the generated media and repeatedly provides content optimized for the user.

[0451] For example, if a user types "I want to listen to relaxing music today" into their smartphone, the server receives this request, generates relaxing music through sentiment analysis and natural language processing, and presents it to the user. Furthermore, if the user provides feedback such as "Please speed up the tempo a bit," the server incorporates this feedback, adjusts the music's tempo, and provides the content again.

[0452] Examples of prompt statements include the following:

[0453] "How are you feeling?", "I want to relax today." → Generated prompt: "Generating relaxing music and recommending videos that match your mood."

[0454] The main software used includes natural language processing engines and sentiment analysis engines, and by integrating these, it is possible to generate and deliver media that meets user requirements.

[0455] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0456] Step 1:

[0457] The user uses a device such as a smartphone or tablet to input a request in natural language, such as requesting relaxing music. This request is sent to the device as voice or text data.

[0458] Step 2:

[0459] The terminal converts user input into digital data and formats it for transmission to the server. Specifically, in the case of voice input, it performs speech recognition and converts it into text. The formatted text data is then sent to the server.

[0460] Step 3:

[0461] The server analyzes the received user request data using a natural language processing engine. At this stage, it extracts important keywords and phrases from the request to clarify the user's intent. As output, it generates data that indicates the intent of the request.

[0462] Step 4:

[0463] The server uses an emotion analysis engine based on the intent of the request to identify the user's emotions. The emotion data output will include emotional states such as "seeking relaxation."

[0464] Step 5:

[0465] Based on the extracted intent and emotion data, the server uses a generative AI model to select and generate the optimal media type (e.g., music). Music data that matches the user's emotions is output.

[0466] Step 6:

[0467] Music data generated from the server is sent to the terminal and played back by the user. By listening to the presented music, the user can enjoy content that matches their emotions.

[0468] Step 7:

[0469] When a user enters feedback about music, the device sends that feedback to the server. The specific feedback (for example, "Please speed up the tempo") is then formatted as data.

[0470] Step 8:

[0471] The server analyzes user feedback and regenerates music data based on the feedback data. The generation AI model generates the newly adjusted music data and sends it to the device.

[0472] Step 9:

[0473] The device provides the user with regenerated music data, allowing them to enjoy music that has been adjusted based on their feedback.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] [Third Embodiment]

[0478] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0479] 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.

[0480] 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).

[0481] 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.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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".

[0490] This invention is a system that interprets operation requests entered by users in natural language and executes the requested tasks using appropriate artificial intelligence services. This system mainly consists of the following components.

[0491] First, users can instruct the system on specific tasks using natural language. For example, they can input a specific request such as "I want to turn a picture of my dog ​​into an anime style" into the user interface. This user interface is simple and intuitive, designed to allow users to operate it without stress.

[0492] Next, the terminal receives this input and sends it to the central server. The terminal preprocesses the input as needed and passes it to the server in the appropriate format.

[0493] The server analyzes the received input. It uses a natural language processing engine to understand the user's request and extract relevant elements, such as keywords like "image" and "convert to anime style."

[0494] Next, the server identifies an appropriate artificial intelligence service based on the analysis results and sends a request to it via an API. It then retrieves the processing results. This process could involve various AI services, such as image generation, speech translation, and text generation.

[0495] Once the service returns results, the server sends them to the terminal and presents them to the user. At this time, the server accepts user feedback on the results and makes corrections or reruns as needed.

[0496] For example, if a user provides feedback saying, "I don't like the result. I want the colors to be brighter," the server will reprocess the data based on that instruction and generate a new result.

[0497] Thus, the present invention enables flexible and rapid responses to user needs and the effective use of multiple artificial intelligence services. As a result, users can perform a variety of tasks with natural operation without having to understand the technical details of each service.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "I want to convert a picture of a cat into an oil painting style."

[0501] Step 2:

[0502] The terminal receives input from the user, formats and transforms the data, and then sends it to the server.

[0503] Step 3:

[0504] The server receives the user's request sent from the terminal.

[0505] Step 4:

[0506] The server uses a natural language processing engine to analyze the user's request. This extracts the necessary elements and parameters (keywords such as "cat image" and "oil painting style").

[0507] Step 5:

[0508] The server identifies the appropriate artificial intelligence service based on the analyzed information. In this case, it selects a service that supports image conversion.

[0509] Step 6:

[0510] The server sends a request to the identified artificial intelligence service via API. This request includes the original image file as necessary data and specifies the conversion parameters.

[0511] Step 7:

[0512] The server receives the processing results from the artificial intelligence service. Specifically, it retrieves image data that has been converted to an oil painting style.

[0513] Step 8:

[0514] The server sends the acquired results to the terminal and instructs it to present them to the user.

[0515] Step 9:

[0516] The user reviews the presented results and, if necessary, communicates correction requests to the server via their device.

[0517] Step 10:

[0518] The server receives feedback and correction instructions from the user and sends a request to the artificial intelligence service again with the new parameters. The process is completed by presenting the reprocessed results to the user again.

[0519] (Example 1)

[0520] 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."

[0521] Current systems utilizing artificial intelligence technology struggle to accurately analyze user input in natural language and select the optimal machine learning engine. Furthermore, they lack the ability to automatically adjust processes based on real-time user feedback and opinions, making it difficult to meet user expectations. Additionally, the lack of a way for users to inquire about machine learning engine costs beforehand can lead to financial concerns.

[0522] 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.

[0523] In this invention, the server includes means for receiving instructions in natural language from a user and analyzing those instructions; means for identifying multiple different machine learning engines based on the extracted elements and instructing them to perform calculations; and means for presenting the calculation results and instructing them to perform calculations again based on correction instructions from the user. This makes it possible to accurately recognize user requests and provide optimal processing. Furthermore, by generating prompt statements and sending them in a format suitable for machine learning engines, flexible processing based on user instructions becomes possible, thereby increasing user satisfaction.

[0524] A "user" refers to an individual or group that operates a system and inputs requests using natural language.

[0525] "Natural language" refers to the language that humans use on a daily basis, including instructions and requests expressed in speech and text.

[0526] "Instructions" refer to requests from a user to perform a specific task or action on the system.

[0527] "Analysis" refers to the process of understanding natural language instructions received from the user and extracting necessary elements and keywords.

[0528] An "element" is a piece of information extracted from natural language during the analysis process, and it is useful for selecting a machine learning engine.

[0529] A "machine learning engine" refers to a platform or model that uses artificial intelligence technology to automatically perform specific tasks.

[0530] "Computation" refers to the calculations and processes that a machine learning engine performs to carry out a given task.

[0531] "Presentation" refers to the act of displaying the calculation result to the user visually or audibly.

[0532] A "correction instruction" refers to a request from a user to modify or change the results presented.

[0533] A "prompt" refers to a sentence structured to provide instructions or requests in a format suitable for a machine learning engine.

[0534] This system consists of three main elements: the user, the terminal, and the server. First, the user inputs tasks into the terminal using natural language. For example, they can naturally describe specific requests such as "I want to turn a picture of my dog ​​into an anime style."

[0535] The terminal receives input from the user and sends it to the server. Here, the terminal preprocesses and formats the data. Natural language processing techniques are used for this preprocessing, particularly to extract specific elements intended by the user. A general-purpose computer, smartphone, or natural language processing library can be used for this.

[0536] When the server receives data sent from the terminal, it uses a natural language processing engine to analyze the input. This analysis extracts keywords such as "image" and "convert to anime style." Next, the server selects the most suitable machine learning engine and generates and sends an appropriate prompt message to it. If a generative AI model is used, the prompt will include instructions such as "convert the image of the dog to an anime style."

[0537] For example, a prompt to a generative AI model might include a natural language instruction such as, "Convert the following dog photo into an anime style." This prompt is crucial for the generative AI model to perform the expected processing.

[0538] The server receives results from the machine learning engine and presents them to the user via the terminal. When the user provides feedback on the results, the server reprocesses them based on that feedback. This allows for flexible responses based on user requests and enables the task to be performed with higher accuracy.

[0539] This embodiment allows users to easily perform a variety of tasks utilizing AI technology through natural instructions, even without specialized knowledge.

[0540] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0541] Step 1:

[0542] The user inputs a specific task into the terminal using natural language. For example, they might input a request like, "I want to convert a picture of a dog into an anime style." This input is then passed to the terminal.

[0543] Step 2:

[0544] The terminal receives input from the user and performs natural language processing. It analyzes the input text and extracts necessary keywords such as "dog photos" and "convert to anime style." This processing generates structured data that allows the server to understand the request.

[0545] Step 3:

[0546] The terminal sends the pre-processed data to the server. Here, the structured data is passed to the server and handed over to the next processing step.

[0547] Step 4:

[0548] The server analyzes the data received from the terminal. Using a natural language processing engine, it determines the appropriate machine learning engine based on the identified keywords. Based on the analysis results, it generates prompt messages and prepares the system.

[0549] Step 5:

[0550] The server uses the generated prompt to send a request to the machine learning engine. By selecting a generating AI model and sending a prompt such as "Convert dog photos into an anime style," it starts the corresponding data calculation.

[0551] Step 6:

[0552] The server receives the computation results from the machine learning engine. For example, it retrieves converted anime-style image data. This result is held in preparation for being presented to the user in the next step.

[0553] Step 7:

[0554] The server formats the acquired calculation results and sends them to the terminal. The formatted data is then ready to be presented to the user.

[0555] Step 8:

[0556] The terminal receives data sent from the server and presents the results to the user visually. The user can review the presented results and rate their satisfaction level.

[0557] Step 9:

[0558] The user provides feedback on the results presented. For example, they can enter specific correction instructions such as, "I want it a little brighter."

[0559] Step 10:

[0560] The server generates a new prompt based on user feedback and resubmits the request to the machine learning engine. This process is repeated until the user is satisfied with the results.

[0561] (Application Example 1)

[0562] 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."

[0563] The problem that this invention aims to solve is to enable users in commercial facilities to quickly and intuitively obtain product information and inventory status. Conventional methods have the problem that product searches and inventory checks take time, and the convenience for users is not fully realized.

[0564] 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.

[0565] In this invention, the server includes means for receiving operation requests from users in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for receiving product search requests from users in natural language at commercial facilities and extracting product information. This enables users to instantly grasp product information and inventory status.

[0566] A "user" is an entity that makes operational requests or inquiries to a system and obtains information using natural language.

[0567] "Natural language operation requests" refer to operational instructions and inquiries using language that people use on a daily basis, and include specific requests to a system.

[0568] "Analysis" is the process of processing received natural language manipulation requests and extracting necessary information and keywords.

[0569] "Artificial intelligence services" refer to a group of AI technologies used to perform specific tasks, providing functions such as image processing, speech recognition, and text analysis.

[0570] A "commercial facility" is a place that provides goods and services, and includes shops, supermarkets, department stores, and so on.

[0571] A "product search request" is an operation request made by a user to obtain information about a specific product, such as specifying the product name or category to check inventory information.

[0572] "Inventory information" refers to information regarding the quantity, location, and replenishment status of products within a commercial facility.

[0573] A "database" is a collection of information in which a system stores data about products and inventory, and which can be accessed as needed.

[0574] The system implementing this invention is an application designed to expedite the provision of product information in commercial facilities.

[0575] The server receives product search requests in natural language sent from users via their devices. These requests are analyzed using a natural language processing engine to extract keywords and intent. This analysis can utilize Google Cloud Natural Language API or similar natural language processing technologies.

[0576] Based on the analyzed information, the server accesses the commercial facility's database to retrieve relevant product and inventory information. This database stores information such as product name, category, price, and inventory quantity, and is optimized for quick searching.

[0577] The acquired information is then sent back from the server to the user's terminal and presented to the user visually through a dedicated application. The application provides a responsive user interface, enabling intuitive operation for the user.

[0578] For example, if a user enters "Do you have organic fruit in stock?" into a smartphone app while in a store, the server analyzes the request and quickly retrieves the relevant information from the database. The app then displays a list of available fruits and related information on the screen.

[0579] Examples of prompts to input into a generative AI model:

[0580] "Analyze the user's input, 'Do you have {}_product_{}_in stock?', retrieve product inventory information from the relevant database, and return the result."

[0581] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0582] Step 1:

[0583] The terminal receives product search requests from the user in natural language. This input is obtained through the user interface. The terminal standardizes the input and prepares to send the request to the server over the network.

[0584] Step 2:

[0585] The server parses natural language requests received from the terminal. It receives the request text as input and performs analysis using a natural language processing engine such as the Google Cloud Natural Language API. Through this analysis, it extracts keywords related to product names and categories, and understands the intent. Based on these analysis results, it proceeds to the next step.

[0586] Step 3:

[0587] The server sends queries to the database based on the analyzed information to retrieve relevant product and inventory information. The database stores information such as product name, category, and inventory status. The input is keywords obtained from the analysis results, and the output is detailed information about the relevant product. The server extracts the most suitable product data based on the search criteria.

[0588] Step 4:

[0589] The server sends the retrieved product information to the terminal. The output product information is converted into a format that is easy for the user to understand. When sending to the terminal, the data is appropriately formatted via the communication protocol.

[0590] Step 5:

[0591] The terminal displays product information received from the server on the user interface. The information is visually organized and presented to the user in an easy-to-understand manner. For example, if a user checks the "stock of organic fruit," the terminal will display a list of the types of fruit and their stock status.

[0592] Step 6:

[0593] The user makes a decision based on the displayed information. Alternatively, they may enter further requests or feedback into the terminal to update information or check for other products. This input may return to step 1 and initiate a new process.

[0594] 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.

[0595] This invention is a system that interprets user requests in natural language, selects and executes appropriate artificial intelligence services accordingly, and further recognizes the user's emotions to optimize the response. This system has the following components.

[0596] First, the user inputs operation requests to the system in natural language through the interface. For example, a specific request might be, "I'm feeling down today, so please generate some relaxing music."

[0597] The terminal receives user input, formats the data, and then sends it to the server.

[0598] When the server receives user input, it uses a natural language processing engine to analyze the request and extract the necessary information. During this process, it uses an emotion recognition engine to identify emotions from the user's speech and text. For example, it might determine that the user is seeking relaxation.

[0599] Next, the server selects an appropriate artificial intelligence service based on the extracted information and emotional data. This allows the server to utilize a music generation service under defined conditions to generate music that suits the user's mood.

[0600] Once the AI ​​service processes the data, the server sends it to the terminal, presenting the results to the user. The user can then listen to or play the generated music.

[0601] Furthermore, users can provide feedback on the results. For example, they might request, "I'd like the song to be a bit more upbeat." The server receives this feedback and, along with the emotion recognition data, sends instructions to the artificial intelligence service again to adjust the output.

[0602] This invention allows users to obtain customized output according to their emotional state and to intuitively utilize a variety of artificial intelligence services. This improves the user experience and enables the provision of more personalized services.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "Please generate relaxing images."

[0606] Step 2:

[0607] The terminal receives input from the user, formats the text appropriately, and prepares it for transmission to the server.

[0608] Step 3:

[0609] The server receives text data sent from the terminal. Then, using natural language processing, it analyzes the text and extracts information related to the requested task (in this case, image generation).

[0610] Step 4:

[0611] The server uses an emotion recognition engine to analyze the emotional tone (such as relaxation) contained in the user's input. This allows the server to understand the user's current feelings and state.

[0612] Step 5:

[0613] The server selects an appropriate artificial intelligence service based on the analyzed task information and emotion data. In this case, it selects an image generation service that can produce images that emphasize relaxation.

[0614] Step 6:

[0615] The server sends a request to the selected artificial intelligence service via an API. The request includes user sentiment information as a generation condition.

[0616] Step 7:

[0617] The server receives the results generated from the artificial intelligence service. For example, the service provides an image that creates a relaxed atmosphere.

[0618] Step 8:

[0619] The server sends the generated image to the terminal and prepares the content to be presented.

[0620] Step 9:

[0621] The user reviews the generated image through their device. They can provide feedback requesting changes or adjustments as needed.

[0622] Step 10:

[0623] The server receives feedback from the user and sends instructions to the artificial intelligence service again, along with the emotion recognition results. The results are regenerated based on the new parameters and presented to the user.

[0624] (Example 2)

[0625] 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."

[0626] It is difficult to appropriately select diverse artificial intelligence functions based on natural language manipulation requests from users and to provide personalized output according to the user's emotional state. In addition, it is also a challenge to effectively reflect user requests for corrections and feedback on the generated results and improve the quality of the service. These challenges need to be addressed.

[0627] 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.

[0628] In this invention, the server includes means for receiving operation requests in natural language from the user, analyzing the operation requests to extract the content of the requests and the emotional state, selecting an appropriate artificial intelligence function based on the extracted information and emotional data, and using the artificial intelligence function to generate output that matches the user's emotional state, and presenting the processing results obtained from the artificial intelligence function to the user, accepting requests for corrections and additions from the user, and adjusting prompt sentences to the generated AI model. This enables the selection of an appropriate artificial intelligence function in response to the user's natural language requests, the generation of personalized output according to emotions, and continuous improvement of the output based on user feedback.

[0629] "User" refers to the person who operates the system and inputs requests using natural language.

[0630] "Natural language" refers to the language that people use on a daily basis, not a specific programming language, but the linguistic forms that humans normally use in conversation.

[0631] An "operation request" refers to a natural language expression of the specific actions or processes that a user wants the system to perform.

[0632] "Analysis" refers to the process by which a system syntactically and semantically understands a natural language request it receives and extracts the necessary information.

[0633] "Emotional state" refers to data that identifies the user's emotions, such as excitement, relaxation, or sadness.

[0634] "Artificial intelligence functions" refer to computer programs and algorithms that perform natural language processing or data generation according to specific purposes.

[0635] "Generation" refers to the process of creating new data or content using artificial intelligence capabilities.

[0636] "Requests for modification and addition" refers to new instructions from the user requesting changes or additions to existing processing results.

[0637] A "generative AI model" refers to an algorithm that is trained on large datasets, understands natural language, and is capable of responding to and generating responses.

[0638] A "prompt statement" refers to an instruction given to a generative AI model to cause it to perform a specific response or generate something.

[0639] This invention is a system that receives operation requests from the user in natural language, analyzes them to identify the user's emotional state, and generates personalized output. The system mainly consists of the user's terminal, a server that handles central processing, and a set of necessary algorithms.

[0640] The user first uses the terminal's interface to input requests to the system in natural language. This input is performed using a text-based user interface or speech recognition software. For example, a "general speech recognition engine" can be used as the speech recognition software.

[0641] When a user's request is received by the terminal, the data is formatted and converted into a format suitable for natural language processing. The formatted data is then sent to the server via digital communication technology. The server uses an advanced natural language processing engine and emotion recognition algorithms to analyze the request and determine the user's emotions. This allows the system to understand the user's emotional state and select the most appropriate service.

[0642] Next, the server selects the appropriate artificial intelligence function and generates output that matches the user's emotions. For example, if the user requests to "relax," it uses a music generation algorithm to generate relaxation music. The generated music is sent back to the terminal and presented to the user.

[0643] Furthermore, users can provide feedback on the results. For example, they might input feedback such as, "I'd like to hear more lively music." This feedback is sent to the generating AI model as a prompt. A concrete example of a prompt might be, "The user wants relaxing music. Please generate music using melodies and tempos that reduce stress." The server analyzes this feedback and readjusts the output using the generating AI model.

[0644] Thus, the present invention aims to provide users with an optimal experience by offering a variety of artificial intelligence services based on their emotions.

[0645] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0646] Step 1:

[0647] The user inputs operation requests in natural language through the terminal's interface. These inputs include specific requests such as "Please generate relaxing music." The input data is stored on the terminal as text data.

[0648] Step 2:

[0649] The terminal formats the input natural language data. Specifically, it performs noise reduction and standardizes character encoding formats, converting it into a format that can be parsed by a natural language processing engine. The formatted data is then ready to be sent to the next step.

[0650] Step 3:

[0651] The terminal sends the formatted data to the server. This transmission is performed using cloud communication technology, and the data reaches the server in real time. The server then passes the received data to a natural language processing engine.

[0652] Step 4:

[0653] The server analyzes the received natural language data using a natural language processing engine, understanding the content of the operation request through grammatical and contextual analysis. During this process, it extracts necessary information using a generative AI model and identifies the user's intent. The analyzed request content is then moved on to the next processing step.

[0654] Step 5:

[0655] The server uses an emotion recognition engine to determine the user's emotional state. Based on keywords and context extracted from natural language data, it applies an emotion analysis algorithm to determine what the user wants. The output emotion data is used for service selection.

[0656] Step 6:

[0657] The server selects the appropriate artificial intelligence function based on the analyzed request content and emotion data. For example, if it determines that the user is seeking "relaxation," it will select a music generation algorithm. The selected artificial intelligence function will then generate output that corresponds to the user's request.

[0658] Step 7:

[0659] The server executes selected artificial intelligence functions to generate output that matches the user's emotional state. Specifically, it uses a generative AI model to create relaxation music. In this process, pre-configured prompt sentences are given to the generative AI model as input, and music data is obtained as output.

[0660] Step 8:

[0661] The server sends the generated music data to the terminal. The terminal presents the received music data to the user and prepares the system for music playback.

[0662] Step 9:

[0663] Users listen to the presented music and provide feedback as needed. This feedback may include specific requests such as, "I'd like the music to be more lively."

[0664] Step 10:

[0665] The device sends the user feedback back to the server. The server receives this feedback, adjusts the prompt text, and inputs it back into the AI ​​model to obtain a new output. This process provides a more optimized output tailored to the user's requests.

[0666] (Application Example 2)

[0667] 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."

[0668] Traditional AI-powered systems sometimes struggled to analyze users' natural language requests and provide appropriate services. Furthermore, they lacked the ability to generate and deliver media that considered user emotions. As a result, personalized services that users desired were not adequately provided, leading to decreased user satisfaction.

[0669] 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.

[0670] In this invention, the server includes means for receiving operation requests from a user in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for analyzing the user's emotions and selecting media types based on the emotion data. This makes it possible to generate and provide media that responds to the user's emotions.

[0671] "Natural language" refers to the language that humans use on a daily basis, and is a means of conveying intentions to a system through speech or text.

[0672] An "operation request" is a request made by a user to a system to perform a specific action or process.

[0673] An "artificial intelligence service" is software or a system that uses machine learning and data processing technologies to analyze data or perform specific tasks based on user input.

[0674] "Sentiment analysis" is the process of identifying emotions from a user's speech or text and recognizing them as digital data.

[0675] "Media type" refers to different forms of content, such as music and videos, and is selected based on user needs and emotions.

[0676] The system for implementing this invention consists of three components: a server, a terminal, and a user. First, the user inputs emotions and requests into the terminal using natural language. The terminal then formats these requests into digital data and sends it to the server.

[0677] The server analyzes the received data and extracts the necessary information using a natural language processing engine. The sentiment analysis engine identifies emotions from the user's input and selects the most suitable artificial intelligence service based on that data. This selection uses a generative AI model to generate or recommend media types such as music and videos that match the user's emotions.

[0678] Media content provided by the server is presented to the user via the terminal. The user provides feedback on this content, and this feedback is sent back to the server. Based on the feedback, the server adjusts the parameters of the generated media and repeatedly provides content optimized for the user.

[0679] For example, if a user types "I want to listen to relaxing music today" into their smartphone, the server receives this request, generates relaxing music through sentiment analysis and natural language processing, and presents it to the user. Furthermore, if the user provides feedback such as "Please speed up the tempo a bit," the server incorporates this feedback, adjusts the music's tempo, and provides the content again.

[0680] Examples of prompt statements include the following:

[0681] "How are you feeling?", "I want to relax today." → Generated prompt: "Generating relaxing music and recommending videos that match your mood."

[0682] The main software used includes natural language processing engines and sentiment analysis engines, and by integrating these, it is possible to generate and deliver media that meets user requirements.

[0683] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0684] Step 1:

[0685] The user uses a device such as a smartphone or tablet to input a request in natural language, such as requesting relaxing music. This request is sent to the device as voice or text data.

[0686] Step 2:

[0687] The terminal converts user input into digital data and formats it for transmission to the server. Specifically, in the case of voice input, it performs speech recognition and converts it into text. The formatted text data is then sent to the server.

[0688] Step 3:

[0689] The server analyzes the received user request data using a natural language processing engine. At this stage, it extracts important keywords and phrases from the request to clarify the user's intent. As output, it generates data that indicates the intent of the request.

[0690] Step 4:

[0691] The server uses an emotion analysis engine based on the intent of the request to identify the user's emotions. The emotion data output will include emotional states such as "seeking relaxation."

[0692] Step 5:

[0693] Based on the extracted intent and emotion data, the server uses a generative AI model to select and generate the optimal media type (e.g., music). Music data that matches the user's emotions is output.

[0694] Step 6:

[0695] Music data generated from the server is sent to the terminal and played back by the user. By listening to the presented music, the user can enjoy content that matches their emotions.

[0696] Step 7:

[0697] When a user enters feedback about music, the device sends that feedback to the server. The specific feedback (for example, "Please speed up the tempo") is then formatted as data.

[0698] Step 8:

[0699] The server analyzes user feedback and regenerates music data based on the feedback data. The generation AI model generates the newly adjusted music data and sends it to the device.

[0700] Step 9:

[0701] The device provides the user with regenerated music data, allowing them to enjoy music that has been adjusted based on their feedback.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] [Fourth Embodiment]

[0706] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0707] 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.

[0708] 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).

[0709] 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.

[0710] 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.

[0711] 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).

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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".

[0719] This invention is a system that interprets operation requests entered by users in natural language and executes the requested tasks using appropriate artificial intelligence services. This system mainly consists of the following components.

[0720] First, users can instruct the system on specific tasks using natural language. For example, they can input a specific request such as "I want to turn a picture of my dog ​​into an anime style" into the user interface. This user interface is simple and intuitive, designed to allow users to operate it without stress.

[0721] Next, the terminal receives this input and sends it to the central server. The terminal preprocesses the input as needed and passes it to the server in the appropriate format.

[0722] The server analyzes the received input. It uses a natural language processing engine to understand the user's request and extract relevant elements, such as keywords like "image" and "convert to anime style."

[0723] Next, the server identifies an appropriate artificial intelligence service based on the analysis results and sends a request to it via an API. It then retrieves the processing results. This process could involve various AI services, such as image generation, speech translation, and text generation.

[0724] Once the service returns results, the server sends them to the terminal and presents them to the user. At this time, the server accepts user feedback on the results and makes corrections or reruns as needed.

[0725] For example, if a user provides feedback saying, "I don't like the result. I want the colors to be brighter," the server will reprocess the data based on that instruction and generate a new result.

[0726] Thus, the present invention enables flexible and rapid responses to user needs and the effective use of multiple artificial intelligence services. As a result, users can perform a variety of tasks with natural operation without having to understand the technical details of each service.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "I want to convert a picture of a cat into an oil painting style."

[0730] Step 2:

[0731] The terminal receives input from the user, formats and transforms the data, and then sends it to the server.

[0732] Step 3:

[0733] The server receives the user's request sent from the terminal.

[0734] Step 4:

[0735] The server uses a natural language processing engine to analyze the user's request. This extracts the necessary elements and parameters (keywords such as "cat image" and "oil painting style").

[0736] Step 5:

[0737] The server identifies the appropriate artificial intelligence service based on the analyzed information. In this case, it selects a service that supports image conversion.

[0738] Step 6:

[0739] The server sends a request to the identified artificial intelligence service via API. This request includes the original image file as necessary data and specifies the conversion parameters.

[0740] Step 7:

[0741] The server receives the processing results from the artificial intelligence service. Specifically, it retrieves image data that has been converted to an oil painting style.

[0742] Step 8:

[0743] The server sends the acquired results to the terminal and instructs it to present them to the user.

[0744] Step 9:

[0745] The user reviews the presented results and, if necessary, communicates correction requests to the server via their device.

[0746] Step 10:

[0747] The server receives feedback and correction instructions from the user and sends a request to the artificial intelligence service again with the new parameters. The process is completed by presenting the reprocessed results to the user again.

[0748] (Example 1)

[0749] 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".

[0750] Current systems utilizing artificial intelligence technology struggle to accurately analyze user input in natural language and select the optimal machine learning engine. Furthermore, they lack the ability to automatically adjust processes based on real-time user feedback and opinions, making it difficult to meet user expectations. Additionally, the lack of a way for users to inquire about machine learning engine costs beforehand can lead to financial concerns.

[0751] 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.

[0752] In this invention, the server includes means for receiving instructions in natural language from a user and analyzing those instructions; means for identifying multiple different machine learning engines based on the extracted elements and instructing them to perform calculations; and means for presenting the calculation results and instructing them to perform calculations again based on correction instructions from the user. This makes it possible to accurately recognize user requests and provide optimal processing. Furthermore, by generating prompt statements and sending them in a format suitable for machine learning engines, flexible processing based on user instructions becomes possible, thereby increasing user satisfaction.

[0753] A "user" refers to an individual or group that operates a system and inputs requests using natural language.

[0754] "Natural language" refers to the language that humans use on a daily basis, including instructions and requests expressed in speech and text.

[0755] "Instructions" refer to requests from a user to perform a specific task or action on the system.

[0756] "Analysis" refers to the process of understanding natural language instructions received from the user and extracting necessary elements and keywords.

[0757] An "element" is a piece of information extracted from natural language during the analysis process, and it is useful for selecting a machine learning engine.

[0758] A "machine learning engine" refers to a platform or model that uses artificial intelligence technology to automatically perform specific tasks.

[0759] "Computation" refers to the calculations and processes that a machine learning engine performs to carry out a given task.

[0760] "Presentation" refers to the act of displaying the calculation result to the user visually or audibly.

[0761] A "correction instruction" refers to a request from a user to modify or change the results presented.

[0762] A "prompt" refers to a sentence structured to provide instructions or requests in a format suitable for a machine learning engine.

[0763] This system consists of three main elements: the user, the terminal, and the server. First, the user inputs tasks into the terminal using natural language. For example, they can naturally describe specific requests such as "I want to turn a picture of my dog ​​into an anime style."

[0764] The terminal receives input from the user and sends it to the server. Here, the terminal preprocesses and formats the data. Natural language processing techniques are used for this preprocessing, particularly to extract specific elements intended by the user. A general-purpose computer, smartphone, or natural language processing library can be used for this.

[0765] When the server receives data sent from the terminal, it uses a natural language processing engine to analyze the input. This analysis extracts keywords such as "image" and "convert to anime style." Next, the server selects the most suitable machine learning engine and generates and sends an appropriate prompt message to it. If a generative AI model is used, the prompt will include instructions such as "convert the image of the dog to an anime style."

[0766] For example, a prompt to a generative AI model might include a natural language instruction such as, "Convert the following dog photo into an anime style." This prompt is crucial for the generative AI model to perform the expected processing.

[0767] The server receives results from the machine learning engine and presents them to the user via the terminal. When the user provides feedback on the results, the server reprocesses them based on that feedback. This allows for flexible responses based on user requests and enables the task to be performed with higher accuracy.

[0768] This embodiment allows users to easily perform a variety of tasks utilizing AI technology through natural instructions, even without specialized knowledge.

[0769] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0770] Step 1:

[0771] The user inputs a specific task into the terminal using natural language. For example, they might input a request like, "I want to convert a picture of a dog into an anime style." This input is then passed to the terminal.

[0772] Step 2:

[0773] The terminal receives input from the user and performs natural language processing. It analyzes the input text and extracts necessary keywords such as "dog photos" and "convert to anime style." This processing generates structured data that allows the server to understand the request.

[0774] Step 3:

[0775] The terminal sends the pre-processed data to the server. Here, the structured data is passed to the server and handed over to the next processing step.

[0776] Step 4:

[0777] The server analyzes the data received from the terminal. Using a natural language processing engine, it determines the appropriate machine learning engine based on the identified keywords. Based on the analysis results, it generates prompt messages and prepares the system.

[0778] Step 5:

[0779] The server uses the generated prompt to send a request to the machine learning engine. By selecting a generating AI model and sending a prompt such as "Convert dog photos into an anime style," it starts the corresponding data calculation.

[0780] Step 6:

[0781] The server receives the computation results from the machine learning engine. For example, it retrieves converted anime-style image data. This result is held in preparation for being presented to the user in the next step.

[0782] Step 7:

[0783] The server formats the acquired calculation results and sends them to the terminal. The formatted data is then ready to be presented to the user.

[0784] Step 8:

[0785] The terminal receives data sent from the server and presents the results to the user visually. The user can review the presented results and rate their satisfaction level.

[0786] Step 9:

[0787] The user provides feedback on the results presented. For example, they can enter specific correction instructions such as, "I want it a little brighter."

[0788] Step 10:

[0789] The server generates a new prompt based on user feedback and resubmits the request to the machine learning engine. This process is repeated until the user is satisfied with the results.

[0790] (Application Example 1)

[0791] 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".

[0792] The problem that this invention aims to solve is to enable users in commercial facilities to quickly and intuitively obtain product information and inventory status. Conventional methods have the problem that product searches and inventory checks take time, and the convenience for users is not fully realized.

[0793] 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.

[0794] In this invention, the server includes means for receiving operation requests from users in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for receiving product search requests from users in natural language at commercial facilities and extracting product information. This enables users to instantly grasp product information and inventory status.

[0795] A "user" is an entity that makes operational requests or inquiries to a system and obtains information using natural language.

[0796] "Natural language operation requests" refer to operational instructions and inquiries using language that people use on a daily basis, and include specific requests to a system.

[0797] "Analysis" is the process of processing received natural language manipulation requests and extracting necessary information and keywords.

[0798] "Artificial intelligence services" refer to a group of AI technologies used to perform specific tasks, providing functions such as image processing, speech recognition, and text analysis.

[0799] A "commercial facility" is a place that provides goods and services, and includes shops, supermarkets, department stores, and so on.

[0800] A "product search request" is an operation request made by a user to obtain information about a specific product, such as specifying the product name or category to check inventory information.

[0801] "Inventory information" refers to information regarding the quantity, location, and replenishment status of products within a commercial facility.

[0802] A "database" is a collection of information in which a system stores data about products and inventory, and which can be accessed as needed.

[0803] The system implementing this invention is an application designed to expedite the provision of product information in commercial facilities.

[0804] The server receives product search requests in natural language sent from users via their devices. These requests are analyzed using a natural language processing engine to extract keywords and intent. This analysis can utilize Google Cloud Natural Language API or similar natural language processing technologies.

[0805] Based on the analyzed information, the server accesses the commercial facility's database to retrieve relevant product and inventory information. This database stores information such as product name, category, price, and inventory quantity, and is optimized for quick searching.

[0806] The acquired information is then sent back from the server to the user's terminal and presented to the user visually through a dedicated application. The application provides a responsive user interface, enabling intuitive operation for the user.

[0807] For example, if a user enters "Do you have organic fruit in stock?" into a smartphone app while in a store, the server analyzes the request and quickly retrieves the relevant information from the database. The app then displays a list of available fruits and related information on the screen.

[0808] Examples of prompts to input into a generative AI model:

[0809] "Analyze the user's input, 'Do you have {}_product_{}_in stock?', retrieve product inventory information from the relevant database, and return the result."

[0810] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0811] Step 1:

[0812] The terminal receives product search requests from the user in natural language. This input is obtained through the user interface. The terminal standardizes the input and prepares to send the request to the server over the network.

[0813] Step 2:

[0814] The server parses natural language requests received from the terminal. It receives the request text as input and performs analysis using a natural language processing engine such as the Google Cloud Natural Language API. Through this analysis, it extracts keywords related to product names and categories, and understands the intent. Based on these analysis results, it proceeds to the next step.

[0815] Step 3:

[0816] The server sends queries to the database based on the analyzed information to retrieve relevant product and inventory information. The database stores information such as product name, category, and inventory status. The input is keywords obtained from the analysis results, and the output is detailed information about the relevant product. The server extracts the most suitable product data based on the search criteria.

[0817] Step 4:

[0818] The server sends the retrieved product information to the terminal. The output product information is converted into a format that is easy for the user to understand. When sending to the terminal, the data is appropriately formatted via the communication protocol.

[0819] Step 5:

[0820] The terminal displays product information received from the server on the user interface. The information is visually organized and presented to the user in an easy-to-understand manner. For example, if a user checks the "stock of organic fruit," the terminal will display a list of the types of fruit and their stock status.

[0821] Step 6:

[0822] The user makes a decision based on the displayed information. Alternatively, they may enter further requests or feedback into the terminal to update information or check for other products. This input may return to step 1 and initiate a new process.

[0823] 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.

[0824] This invention is a system that interprets user requests in natural language, selects and executes appropriate artificial intelligence services accordingly, and further recognizes the user's emotions to optimize the response. This system has the following components.

[0825] First, the user inputs operation requests to the system in natural language through the interface. For example, a specific request might be, "I'm feeling down today, so please generate some relaxing music."

[0826] The terminal receives user input, formats the data, and then sends it to the server.

[0827] When the server receives user input, it uses a natural language processing engine to analyze the request and extract the necessary information. During this process, it uses an emotion recognition engine to identify emotions from the user's speech and text. For example, it might determine that the user is seeking relaxation.

[0828] Next, the server selects an appropriate artificial intelligence service based on the extracted information and emotional data. This allows the server to utilize a music generation service under defined conditions to generate music that suits the user's mood.

[0829] Once the AI ​​service processes the data, the server sends it to the terminal, presenting the results to the user. The user can then listen to or play the generated music.

[0830] Furthermore, users can provide feedback on the results. For example, they might request, "I'd like the song to be a bit more upbeat." The server receives this feedback and, along with the emotion recognition data, sends instructions to the artificial intelligence service again to adjust the output.

[0831] This invention allows users to obtain customized output according to their emotional state and to intuitively utilize a variety of artificial intelligence services. This improves the user experience and enables the provision of more personalized services.

[0832] The following describes the processing flow.

[0833] Step 1:

[0834] The user inputs their request into the user interface using natural language. For example, they might input a request such as, "Please generate relaxing images."

[0835] Step 2:

[0836] The terminal receives input from the user, formats the text appropriately, and prepares it for transmission to the server.

[0837] Step 3:

[0838] The server receives text data sent from the terminal. Then, using natural language processing, it analyzes the text and extracts information related to the requested task (in this case, image generation).

[0839] Step 4:

[0840] The server uses an emotion recognition engine to analyze the emotional tone (such as relaxation) contained in the user's input. This allows the server to understand the user's current feelings and state.

[0841] Step 5:

[0842] The server selects an appropriate artificial intelligence service based on the analyzed task information and emotion data. In this case, it selects an image generation service that can produce images that emphasize relaxation.

[0843] Step 6:

[0844] The server sends a request to the selected artificial intelligence service via an API. The request includes user sentiment information as a generation condition.

[0845] Step 7:

[0846] The server receives the results generated from the artificial intelligence service. For example, the service provides an image that creates a relaxed atmosphere.

[0847] Step 8:

[0848] The server sends the generated image to the terminal and prepares the content to be presented.

[0849] Step 9:

[0850] The user reviews the generated image through their device. They can provide feedback requesting changes or adjustments as needed.

[0851] Step 10:

[0852] The server receives feedback from the user and sends instructions to the artificial intelligence service again, along with the emotion recognition results. The results are regenerated based on the new parameters and presented to the user.

[0853] (Example 2)

[0854] 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".

[0855] It is difficult to appropriately select diverse artificial intelligence functions based on natural language manipulation requests from users and to provide personalized output according to the user's emotional state. In addition, it is also a challenge to effectively reflect user requests for corrections and feedback on the generated results and improve the quality of the service. These challenges need to be addressed.

[0856] 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.

[0857] In this invention, the server includes means for receiving operation requests in natural language from the user, analyzing the operation requests to extract the content of the requests and the emotional state, selecting an appropriate artificial intelligence function based on the extracted information and emotional data, and using the artificial intelligence function to generate output that matches the user's emotional state, and presenting the processing results obtained from the artificial intelligence function to the user, accepting requests for corrections and additions from the user, and adjusting prompt sentences to the generated AI model. This enables the selection of an appropriate artificial intelligence function in response to the user's natural language requests, the generation of personalized output according to emotions, and continuous improvement of the output based on user feedback.

[0858] "User" refers to the person who operates the system and inputs requests using natural language.

[0859] "Natural language" refers to the language that people use on a daily basis, not a specific programming language, but the linguistic forms that humans normally use in conversation.

[0860] An "operation request" refers to a natural language expression of the specific actions or processes that a user wants the system to perform.

[0861] "Analysis" refers to the process by which a system syntactically and semantically understands a natural language request it receives and extracts the necessary information.

[0862] "Emotional state" refers to data that identifies the user's emotions, such as excitement, relaxation, or sadness.

[0863] "Artificial intelligence functions" refer to computer programs and algorithms that perform natural language processing or data generation according to specific purposes.

[0864] "Generation" refers to the process of creating new data or content using artificial intelligence capabilities.

[0865] "Requests for modification and addition" refers to new instructions from the user requesting changes or additions to existing processing results.

[0866] A "generative AI model" refers to an algorithm that is trained on large datasets, understands natural language, and is capable of responding to and generating responses.

[0867] A "prompt statement" refers to an instruction given to a generative AI model to cause it to perform a specific response or generate something.

[0868] This invention is a system that receives operation requests from the user in natural language, analyzes them to identify the user's emotional state, and generates personalized output. The system mainly consists of the user's terminal, a server that handles central processing, and a set of necessary algorithms.

[0869] The user first uses the terminal's interface to input requests to the system in natural language. This input is performed using a text-based user interface or speech recognition software. For example, a "general speech recognition engine" can be used as the speech recognition software.

[0870] When a user's request is received by the terminal, the data is formatted and converted into a format suitable for natural language processing. The formatted data is then sent to the server via digital communication technology. The server uses an advanced natural language processing engine and emotion recognition algorithms to analyze the request and determine the user's emotions. This allows the system to understand the user's emotional state and select the most appropriate service.

[0871] Next, the server selects the appropriate artificial intelligence function and generates output that matches the user's emotions. For example, if the user requests to "relax," it uses a music generation algorithm to generate relaxation music. The generated music is sent back to the terminal and presented to the user.

[0872] Furthermore, users can provide feedback on the results. For example, they might input feedback such as, "I'd like to hear more lively music." This feedback is sent to the generating AI model as a prompt. A concrete example of a prompt might be, "The user wants relaxing music. Please generate music using melodies and tempos that reduce stress." The server analyzes this feedback and readjusts the output using the generating AI model.

[0873] Thus, the present invention aims to provide users with an optimal experience by offering a variety of artificial intelligence services based on their emotions.

[0874] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0875] Step 1:

[0876] The user inputs operation requests in natural language through the terminal's interface. These inputs include specific requests such as "Please generate relaxing music." The input data is stored on the terminal as text data.

[0877] Step 2:

[0878] The terminal formats the input natural language data. Specifically, it performs noise reduction and standardizes character encoding formats, converting it into a format that can be parsed by a natural language processing engine. The formatted data is then ready to be sent to the next step.

[0879] Step 3:

[0880] The terminal sends the formatted data to the server. This transmission is performed using cloud communication technology, and the data reaches the server in real time. The server then passes the received data to a natural language processing engine.

[0881] Step 4:

[0882] The server analyzes the received natural language data using a natural language processing engine, understanding the content of the operation request through grammatical and contextual analysis. During this process, it extracts necessary information using a generative AI model and identifies the user's intent. The analyzed request content is then moved on to the next processing step.

[0883] Step 5:

[0884] The server uses an emotion recognition engine to determine the user's emotional state. Based on keywords and context extracted from natural language data, it applies an emotion analysis algorithm to determine what the user wants. The output emotion data is used for service selection.

[0885] Step 6:

[0886] The server selects the appropriate artificial intelligence function based on the analyzed request content and emotion data. For example, if it determines that the user is seeking "relaxation," it will select a music generation algorithm. The selected artificial intelligence function will then generate output that corresponds to the user's request.

[0887] Step 7:

[0888] The server executes selected artificial intelligence functions to generate output that matches the user's emotional state. Specifically, it uses a generative AI model to create relaxation music. In this process, pre-configured prompt sentences are given to the generative AI model as input, and music data is obtained as output.

[0889] Step 8:

[0890] The server sends the generated music data to the terminal. The terminal presents the received music data to the user and prepares the system for music playback.

[0891] Step 9:

[0892] Users listen to the presented music and provide feedback as needed. This feedback may include specific requests such as, "I'd like the music to be more lively."

[0893] Step 10:

[0894] The device sends the user feedback back to the server. The server receives this feedback, adjusts the prompt text, and inputs it back into the AI ​​model to obtain a new output. This process provides a more optimized output tailored to the user's requests.

[0895] (Application Example 2)

[0896] 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".

[0897] Traditional AI-powered systems sometimes struggled to analyze users' natural language requests and provide appropriate services. Furthermore, they lacked the ability to generate and deliver media that considered user emotions. As a result, personalized services that users desired were not adequately provided, leading to decreased user satisfaction.

[0898] 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.

[0899] In this invention, the server includes means for receiving operation requests from a user in natural language, analyzing the operation requests, and extracting necessary information; means for identifying multiple different artificial intelligence services based on the extracted information and instructing the artificial intelligence services to perform appropriate processing; and means for analyzing the user's emotions and selecting media types based on the emotion data. This makes it possible to generate and provide media that responds to the user's emotions.

[0900] "Natural language" refers to the language that humans use on a daily basis, and is a means of conveying intentions to a system through speech or text.

[0901] An "operation request" is a request made by a user to a system to perform a specific action or process.

[0902] An "artificial intelligence service" is software or a system that uses machine learning and data processing technologies to analyze data or perform specific tasks based on user input.

[0903] "Sentiment analysis" is the process of identifying emotions from a user's speech or text and recognizing them as digital data.

[0904] "Media type" refers to different forms of content, such as music and videos, and is selected based on user needs and emotions.

[0905] The system for implementing this invention consists of three components: a server, a terminal, and a user. First, the user inputs emotions and requests into the terminal using natural language. The terminal then formats these requests into digital data and sends it to the server.

[0906] The server analyzes the received data and extracts the necessary information using a natural language processing engine. The sentiment analysis engine identifies emotions from the user's input and selects the most suitable artificial intelligence service based on that data. This selection uses a generative AI model to generate or recommend media types such as music and videos that match the user's emotions.

[0907] Media content provided by the server is presented to the user via the terminal. The user provides feedback on this content, and this feedback is sent back to the server. Based on the feedback, the server adjusts the parameters of the generated media and repeatedly provides content optimized for the user.

[0908] For example, if a user types "I want to listen to relaxing music today" into their smartphone, the server receives this request, generates relaxing music through sentiment analysis and natural language processing, and presents it to the user. Furthermore, if the user provides feedback such as "Please speed up the tempo a bit," the server incorporates this feedback, adjusts the music's tempo, and provides the content again.

[0909] Examples of prompt statements include the following:

[0910] "How are you feeling?", "I want to relax today." → Generated prompt: "Generating relaxing music and recommending videos that match your mood."

[0911] The main software used includes natural language processing engines and sentiment analysis engines, and by integrating these, it is possible to generate and deliver media that meets user requirements.

[0912] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0913] Step 1:

[0914] The user uses a device such as a smartphone or tablet to input a request in natural language, such as requesting relaxing music. This request is sent to the device as voice or text data.

[0915] Step 2:

[0916] The terminal converts user input into digital data and formats it for transmission to the server. Specifically, in the case of voice input, it performs speech recognition and converts it into text. The formatted text data is then sent to the server.

[0917] Step 3:

[0918] The server analyzes the received user request data using a natural language processing engine. At this stage, it extracts important keywords and phrases from the request to clarify the user's intent. As output, it generates data that indicates the intent of the request.

[0919] Step 4:

[0920] The server uses an emotion analysis engine based on the intent of the request to identify the user's emotions. The emotion data output will include emotional states such as "seeking relaxation."

[0921] Step 5:

[0922] Based on the extracted intent and emotion data, the server uses a generative AI model to select and generate the optimal media type (e.g., music). Music data that matches the user's emotions is output.

[0923] Step 6:

[0924] Music data generated from the server is sent to the terminal and played back by the user. By listening to the presented music, the user can enjoy content that matches their emotions.

[0925] Step 7:

[0926] When a user enters feedback about music, the device sends that feedback to the server. The specific feedback (for example, "Please speed up the tempo") is then formatted as data.

[0927] Step 8:

[0928] The server analyzes user feedback and regenerates music data based on the feedback data. The generation AI model generates the newly adjusted music data and sends it to the device.

[0929] Step 9:

[0930] The device provides the user with regenerated music data, allowing them to enjoy music that has been adjusted based on their feedback.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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."

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] The following is further disclosed regarding the embodiments described above.

[0953] (Claim 1)

[0954] A means for receiving operation requests from users in natural language, analyzing those operation requests, and extracting necessary information,

[0955] A means for identifying multiple different artificial intelligence services based on extracted information and for instructing those artificial intelligence services to perform appropriate processing,

[0956] A means of presenting the processing results obtained from the artificial intelligence service to the user and receiving correction instructions from the user,

[0957] A means of instructing the artificial intelligence service to process again based on correction instructions from the user,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, further comprising means for requesting confirmation and approval regarding the costs associated with using the artificial intelligence service when presenting it to the user.

[0961] (Claim 3)

[0962] The system according to claim 1, further comprising means for collecting user evaluations and feedback on the processing results provided by the artificial intelligence service, and for automatically adjusting the service parameters based on said evaluations and feedback.

[0963] "Example 1"

[0964] (Claim 1)

[0965] A device that receives instructions in natural language from a user, analyzes those instructions, and extracts the necessary elements,

[0966] A device that identifies multiple different machine learning engines based on extracted elements and instructs the machine learning engines to perform appropriate calculations,

[0967] A device that presents calculation results obtained from a machine learning engine to the user and accepts correction instructions from the user,

[0968] A device that instructs the machine learning engine to perform calculations again based on correction instructions from the user,

[0969] A device that generates prompt messages and sends them in a format suitable for a machine learning engine,

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, further comprising a device that, when presenting to the user, requests confirmation and approval regarding the costs associated with using the machine learning engine.

[0973] (Claim 3)

[0974] The system according to claim 1, further comprising a device for collecting user evaluations and opinions on the calculation results provided by the machine learning engine, and for automatically adjusting the engine parameters based on said evaluations and opinions.

[0975] "Application Example 1"

[0976] (Claim 1)

[0977] A means for receiving operation requests from users in natural language, analyzing those operation requests, and extracting necessary information,

[0978] A means for identifying multiple different artificial intelligence services based on extracted information and for instructing those artificial intelligence services to perform appropriate processing,

[0979] A means of presenting the processing results obtained from the artificial intelligence service to the user and receiving correction instructions from the user,

[0980] A means of instructing the artificial intelligence service to process again based on correction instructions from the user,

[0981] A means for receiving product search requests from users in natural language at a commercial facility and extracting the product information,

[0982] A means of obtaining product inventory information from a database and sending the obtained information to the user's terminal,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, further comprising means for requesting confirmation and approval regarding the costs associated with using the artificial intelligence service when presenting it to the user.

[0986] (Claim 3)

[0987] The system according to claim 1, further comprising means for collecting user evaluations and feedback on the processing results provided by the artificial intelligence service, and for automatically adjusting the service parameters based on said evaluations and feedback.

[0988] "Example 2 of combining an emotion engine"

[0989] (Claim 1)

[0990] A means for receiving operation requests from the user in natural language, analyzing the operation requests, and extracting the content of the requests and the emotional state,

[0991] A means for selecting an appropriate artificial intelligence function based on extracted information and emotional data, and for using the artificial intelligence function to generate output that matches the user's emotional state,

[0992] A means for presenting the processing results obtained from the artificial intelligence function to the user, accepting requests for corrections and additions from the user, and adjusting the prompt text for the generated AI model,

[0993] A means of adjusting the output to the artificial intelligence function again based on modification and additional requests from the user,

[0994] A system that includes this.

[0995] (Claim 2)

[0996] The system according to claim 1, further comprising means for requesting confirmation and approval regarding the costs associated with the use of artificial intelligence functions when presenting it to the user.

[0997] (Claim 3)

[0998] The system according to claim 1, further comprising means for collecting evaluations and feedback from users regarding the processing results provided by the artificial intelligence function, and for automatically adjusting the parameters of the function based on said evaluations and feedback.

[0999] "Application example 2 when combining with an emotional engine"

[1000] (Claim 1)

[1001] A means for receiving operation requests from users in natural language, analyzing those operation requests, and extracting necessary information,

[1002] A means for identifying multiple different artificial intelligence services based on extracted information and for instructing those artificial intelligence services to perform appropriate processing,

[1003] A means of presenting the processing results obtained from an artificial intelligence service to the user in media format and receiving correction instructions from the user,

[1004] A means of analyzing user emotions and selecting media types based on emotion data,

[1005] A means of generating media by instructing the artificial intelligence service to process again based on correction instructions from the user,

[1006] A system that includes this.

[1007] (Claim 2)

[1008] The system according to claim 1, further comprising means for requesting confirmation and approval regarding the costs associated with using the artificial intelligence service when presenting it to the user.

[1009] (Claim 3)

[1010] The system according to claim 1, further comprising means for collecting user evaluations and feedback on the processing results provided by the artificial intelligence service, automatically adjusting the service parameters based on said evaluations and feedback, and recommending media. [Explanation of symbols]

[1011] 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 means for receiving operation requests from users in natural language, analyzing those operation requests, and extracting necessary information, A means for identifying multiple different artificial intelligence services based on extracted information and for instructing those artificial intelligence services to perform appropriate processing, A means of presenting the processing results obtained from the artificial intelligence service to the user and receiving correction instructions from the user, A means of instructing the artificial intelligence service to process again based on correction instructions from the user, A means for receiving product search requests from users in natural language at a commercial facility and extracting the product information, A means of obtaining product inventory information from a database and sending the obtained information to the user's terminal, A system that includes this.

2. The system according to claim 1, further comprising means for requesting confirmation and approval regarding the costs associated with using the artificial intelligence service when presenting it to the user.

3. The system according to claim 1, further comprising means for collecting user evaluations and feedback on the processing results provided by the artificial intelligence service, and for automatically adjusting the service parameters based on said evaluations and feedback.

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