system

Abstract

We provide the system. [Solution] A communication method that accepts multiple condition inputs from the user, An analysis means for extracting data from a database related to a mobile information terminal device based on the received conditions, A generation means for selecting the optimal mobile information terminal device using the extracted data, A notification means for notifying the user of information about the selected mobile information terminal device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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] At present, as communication devices, especially mobile information terminal devices, have become more advanced and diversified, there are many options in the market. Therefore, it is difficult for users to select a mobile information terminal device that is optimal for their usage purposes and budget. Under such circumstances, means for efficiently selecting an optimal terminal device that meets user requirements are demanded.

Means for Solving the Problems

[0005] This invention provides a system that includes an analysis means for receiving condition input from a user and extracting data on the corresponding mobile information terminal device from a database. Furthermore, it includes a generation means for selecting the optimal terminal device using the extracted data and a means for notifying the user of the selected device information. This makes it possible for the user to easily find the terminal device that is best suited to them from a variety of options.

[0006] "Communication means" refers to a function that receives information entered by the user and transmits it to other components within the system.

[0007] "Analysis means" refers to a device or software that has the function of analyzing conditions received from the user and extracting corresponding information from a database.

[0008] "Generation means" refers to a function that determines the optimal option based on the analyzed information and generates results to inform the user of that option.

[0009] "Notification means" refers to a function that provides users with information selected as optimal, enabling them to easily obtain results.

[0010] "Portable information terminal device" refers to a portable electronic device used for communication and information processing, and usually includes devices such as smartphones and tablets. [Brief explanation of the drawing]

[0011] [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, when an emotion engine is combined. [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]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is implemented as a system with a series of functions to efficiently select a personal digital information terminal (PDI) device. First, the user inputs their requirements for purchasing a PDI device through a dedicated interface on the terminal. The input information is diverse, including, for example, budget, required functions, desired screen size, and camera performance.

[0033] The terminal sends the entered information to the server. The server searches the database based on the received information and extracts data for mobile information terminal devices that match the criteria. The database contains detailed information on various types of terminals, including information on the latest models.

[0034] The extracted data is further analyzed by a generation mechanism within the server, and the device that best meets the user's requirements is selected. Here, the server can use machine learning algorithms to make selections that take into account past user selection trends and market trends.

[0035] The server then sends a list of the most suitable personal digital assistant (PDAs) devices to the terminal. The terminal displays this list to the user, allowing them to review the suggested options in detail and make a purchase decision.

[0036] For example, if a user enters conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible," the system will select the most appropriate personal digital assistant (PDCA) device based on these conditions and notify the user. The user can then purchase the device that best suits their needs based on the presented options.

[0037] As described above, the present invention is implemented as a system that can flexibly respond to diverse inputs from users and provide the optimal portable information terminal device quickly and accurately.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users use their devices to input requirements such as budget, necessary features, desired screen size, and camera performance. Input is done through a dedicated interface, providing detailed information through selections and free-text descriptions for each item.

[0041] Step 2:

[0042] The terminal converts the information entered by the user into a data format and sends it to the server. A secure communication protocol is used for transmission to maintain the confidentiality of the information.

[0043] Step 3:

[0044] The server analyzes the received user data and searches the database based on each condition item. First, it narrows down the results based on basic conditions such as budget and OS, and then filters further based on required functions and special specifications.

[0045] Step 4:

[0046] The server uses a machine learning algorithm to select the mobile information terminal device that best matches user needs from the filtered list of candidates. Past user data and market trends are also taken into consideration to improve the accuracy of the selection.

[0047] Step 5:

[0048] The server generates information on the selected optimal mobile device and sends it to the user's device. The information sent includes the device name, main functions, price, and a link to purchase it.

[0049] Step 6:

[0050] The terminal displays information received from the server in a format that is easy for the user to understand. Based on the displayed information, the user can check the details of each terminal and make a purchase decision.

[0051] (Example 1)

[0052] 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."

[0053] In today's information society, a wide variety of electronic devices exist on the market, making it difficult for users to efficiently select products that meet their needs. In particular, when users have diverse requirements, gathering and analyzing information to make the optimal choice becomes complex, and there is a need for a system that enables rapid and accurate selection.

[0054] 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.

[0055] In this invention, the server includes a receiving means for receiving multiple requirements from a user, a querying means for retrieving information on relevant electronic devices from an information set based on the received requirements, and a selection means for selecting the optimal electronic device using the retrieved information. This makes it possible to quickly and accurately analyze the diverse requirements of users and provide the most suitable device.

[0056] A "reception method" is a system that receives user requirements as input and records them as data that can be processed in digital format.

[0057] A "request method" is a tool that searches for appropriate information from an information set based on the received requirements and extracts relevant results.

[0058] A "selection mechanism" is a system that analyzes the retrieved information and processes data to determine which electronic device best meets the user's requirements.

[0059] "Presentation means" refers to technologies for conveying information from selected electronic devices to users through visual or auditory means.

[0060] A "learning model" is an algorithmic system that analyzes specific patterns and trends based on past data to make predictions or selections regarding newly appearing data.

[0061] An "information collection" is a database or data store in which diverse data related to electronic devices is gathered, structured, and stored.

[0062] This invention is implemented as a system for efficiently selecting electronic equipment. This system recommends the most suitable electronic equipment based on the requirements specified by the user. Specific embodiments are described below.

[0063] The user first launches a dedicated application using a mobile device. This application provides an interface for entering user requirements, allowing users to input information such as budget, necessary functions, desired screen size, and camera performance. Intuitive UI elements such as dropdown lists, sliders, and text boxes are provided for information input.

[0064] The terminal converts the information entered by the user into JSON format data and securely sends it to the server using the HTTPS protocol. The transmitted information is then parsed on the server via an intermediary.

[0065] The server uses query tools to search an information database based on the received requirements. This information database contains detailed information about electronic devices, including the latest models and their features.

[0066] Furthermore, the server uses generative AI models and learning models as selection tools. This allows it to analyze and score the most appropriate electronic devices based on past selection history and market trends. The analysis utilizes TENSORFLOW® and other machine learning libraries.

[0067] The server then encrypts the list of selected electronic devices and sends it to the terminal in JSON format.

[0068] The terminal uses a presentation method to display the selection results to the user. The results are visualized as a card view or list view, making it easy for the user to compare devices and make a purchase decision.

[0069] For example, users can specify their requirements using prompt statements such as "I'm looking for the latest smartphone with a high-resolution camera and a budget of under 50,000 yen" or "Tell me about the latest smartphone models with a large screen that fit my budget." In this way, the present invention assists users in quickly and accurately selecting the electronic device they want.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user launches a dedicated application on their mobile device and enters their requirements using a requirements input interface. Specifically, they select and enter information such as budget, features, screen size, and camera performance. This information becomes the input data. The entered data is converted into JSON format within the application.

[0073] Step 2:

[0074] The terminal sends the converted JSON data to the server. Since the transmission method uses the HTTPS protocol, data security is maintained. The transmitted JSON data becomes the server's input data, and after reception, it is processed by the receiving mechanism for analysis.

[0075] Step 3:

[0076] Based on the received input data, the server initiates a query to the information set in order to perform a database search. It generates an SQL query and uses this query to extract information about electronic devices that match the user's requirements. The extracted information becomes intermediate output data within the server and is used for the next processing step.

[0077] Step 4:

[0078] The server further analyzes the extracted information using generative AI models and learning models. It applies a scoring algorithm that considers past user data and market trends to select the most suitable electronic device. The output here is a list of electronic devices best suited to the user's requirements.

[0079] Step 5:

[0080] The server repackages the selected electronic device list in JSON format, encrypts it, and sends it to the terminal. This list becomes the input data for the terminal and is visually displayed to the user via a presentation method.

[0081] Step 6:

[0082] The device displays the received selection list on the UI. Using card view or list view formats, users can easily compare and select items. The design format is particularly important here, requiring a user-friendly and easy-to-understand layout. This display is the device's final output, and users use it as a reference to make their purchase decision.

[0083] (Application Example 1)

[0084] 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."

[0085] When users purchase information equipment, they often face the challenge of quickly and easily selecting the product that best suits their needs from a wide range of options. There is a need for systems that efficiently assist users in making the optimal choice.

[0086] 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.

[0087] In this invention, the server includes communication means for receiving multiple condition inputs from a user, analysis means for extracting data on relevant information devices from a database based on the received conditions, and generation means for selecting the optimal information device using the extracted data. This makes it possible to propose information devices based on precise conditions.

[0088] A "user" is an individual or corporation looking for a product that meets their needs when purchasing information equipment.

[0089] "Condition input" refers to information that indicates the selection criteria specified by the user, such as budget, performance, and feature requirements.

[0090] "Communication means" refers to methods and technologies for transmitting user input conditions to a system.

[0091] "Information equipment" is a general term for electronic devices such as mobile phones and tablets.

[0092] "Analysis means" refers to a technology for extracting data from a database of relevant information devices based on received condition inputs.

[0093] A "database" is a collection of data that systematically stores detailed information about information devices.

[0094] "Generation means" refers to methods and technologies for identifying the optimal information device from the extracted information.

[0095] "Notification means" refers to methods and technologies for informing users of information about selected information devices.

[0096] A "generative artificial intelligence model" is an artificial intelligence technology used to automate product recommendations based on user criteria.

[0097] The system implementing this invention uses a communication device that accepts user input conditions, a server that analyzes information in a database, and a generative artificial intelligence model as a generation means. The user inputs their conditions on a smartphone application or web platform. For example, they might input conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible."

[0098] The server searches the database based on user conditions received via the communication device and extracts data for relevant information devices. The database contains detailed information on a wide variety of information devices, and the server generates a list that best matches the user's conditions. This generation uses a machine learning algorithm that has learned past selection trends and market trends.

[0099] The generative artificial intelligence model, used as a generation tool, uses extracted data to list the most suitable information devices for the user. In this process, it generates text prompts to provide the necessary conditions for product recommendations. An example of a prompt would be: "The user is looking for a new smartphone with a high-resolution camera for under 50,000 yen. Please suggest the most appropriate options based on these conditions."

[0100] Users can then review the list of available information devices notified by the server on their devices and select and purchase a product that meets their needs. This enables accurate and rapid selection of information devices, significantly improving the user's purchasing experience.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] Users enter their requirements using a smartphone application or web platform. These requirements include budget, necessary features, and specific usage purposes. The device then formats this input data and prepares it for transmission to the server.

[0104] Step 2:

[0105] The server parses the user's input criteria. In this parsing, an efficient search algorithm is used to match the user criteria with information in the database. This generates a list of information devices that match the criteria.

[0106] Step 3:

[0107] The server further analyzes the information device data extracted based on user criteria using a generative artificial intelligence model. In this process, machine learning algorithms take into account past selection trends and market trends to identify the product that best fits the selection criteria. Here, appropriate prompt statements generated by the generative AI model are utilized to improve recommendation accuracy.

[0108] Step 4:

[0109] The server generates a list of optimized information devices and notifies the user's terminal. This notification includes detailed information and ratings of recommended products. The terminal displays this information visually through its user interface, making it easy for the user to understand.

[0110] Step 5:

[0111] The user compares the options based on the provided information and decides which information device to purchase. The terminal feeds the user's selection back to the server, preparing it to be used as data to improve the accuracy of recommendations in the future.

[0112] 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.

[0113] This invention is implemented as a system that combines an emotion engine to make the selection process for personal information terminal devices more sophisticated. First, the user inputs their wishes and requirements for a personal information terminal device through the terminal. Here, the user sets specific requirements such as budget, desired functions, screen size, and camera performance. During this input, the emotion engine installed in the terminal analyzes the user's emotions from the input content, voice, and facial expressions.

[0114] The terminal sends collected conditional data and sentiment data to the server. The server receives this data and searches the database using an analysis tool. The analysis tool first extracts terminal information that matches the user's conditions, and then, taking sentiment data into consideration, generates customized results that reflect the user's expectations and preferences.

[0115] The server uses machine learning algorithms to evaluate candidate mobile devices and select the model that is likely to provide the highest user satisfaction. The emotion engine plays a crucial role in this selection process, enabling suggestions that reflect the user's latent needs and interests.

[0116] The server then sends the selected information to the terminal. The terminal presents the results to the user in an easy-to-understand format. Here, the user can understand how their emotions are reflected and why a particular terminal was recommended.

[0117] For example, if a user prioritizes camera functionality but feels stressed because they don't understand the latest technology, the emotion engine can recognize this and suggest the most suitable device based on the user's knowledge level, along with a simple and easy-to-understand explanation to alleviate the stress.

[0118] Thus, the present invention is implemented as a system that enables the selection of more personalized mobile information terminal devices through the use of an emotion engine, thereby improving user satisfaction.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The user inputs specific requirements for a mobile information terminal (PDCA) device via the terminal. These include budget, necessary functions, screen size, and camera performance. The system also captures the user's emotions through facial expressions and voice.

[0122] Step 2:

[0123] The terminal converts the input condition data and captured emotion data into a data format and sends it to the server using a secure communication method.

[0124] Step 3:

[0125] The server analyzes the received data, searches the database, and extracts candidate mobile information terminal devices that match the user's criteria.

[0126] Step 4:

[0127] The server's emotion engine analyzes user emotion data and evaluates stress levels, expectations, preferences, and other factors.

[0128] Step 5:

[0129] The server uses the results of sentiment analysis to select the mobile information terminal device that best matches the user's needs from among the candidates using a machine learning algorithm. During this process, it also provides customized suggestions that take into account the user's emotional state.

[0130] Step 6:

[0131] The server sends detailed information about the selected mobile information terminal device to the terminal.

[0132] Step 7:

[0133] The terminal visually displays the received information to the user. Based on the presented information, the user can select the optimal mobile information terminal device while reviewing the reasons for the suggestions and the results of sentiment analysis.

[0134] (Example 2)

[0135] 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 will be referred to as the "terminal."

[0136] When users select information processing equipment, they want to consider subjective feelings in addition to demand, budget, and functionality. However, conventional systems do not reflect these emotional elements, resulting in insufficient improvement of the user experience.

[0137] 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.

[0138] In this invention, the server includes communication means for receiving multiple conditional and emotional data from the user, analysis means for extracting data from a data aggregation unit for relevant information processing devices based on the received conditional and emotional data, and generation means for selecting the optimal information processing device using the extracted data. This makes it possible to select a more personalized information processing device that reflects the user's emotions.

[0139] "Multiple user requirements" refer to the specifications and constraints that users desire regarding the selection of an information processing device, specifically including requirements such as budget, functionality, screen size, and camera performance.

[0140] "Emotional data" refers to information about a user's subjective psychological state, analyzed from their facial expressions and tone of voice, and reflects their unconscious expectations and preferences.

[0141] A "communication method" is a system equipped with interfaces and protocols for sending user conditions and emotional data to a server, and is a means for smoothly receiving and transmitting data.

[0142] "Analysis means" refers to a means for performing a process of searching a data storage unit based on conditional and emotional data received from the user and narrowing down the candidates for related information processing devices.

[0143] The "Data Accumulation Unit" is a database that stores various types of information related to the information processing device, and this system is used as a search target during the selection process.

[0144] "Generation means" refers to the means of executing processes and algorithms used to select the optimal information processing device from the candidates narrowed down by the analysis means, and in particular, the selection is carried out by applying machine learning algorithms.

[0145] A "notification means" is a system equipped with an interface or protocol for conveying the results of a selected information processing device to the user, and is a means of presenting the results in a way that is easy for the user to understand.

[0146] This invention is specifically implemented as a system that combines multiple conditions and sentiment data to highly personalize the selection process for information processing equipment.

[0147] The user first uses a device to input their requests and requirements. These requests are specific functional preferences, such as screen size and camera performance. During input, the device has a function to capture the user's voice and facial expressions, thereby acquiring the user's emotional data. The device is equipped with an advanced algorithm called an emotion engine, which is used to analyze the emotional data in real time. This analysis allows us to understand how emotions, in addition to the user's requirements, influence their selection.

[0148] The terminal sends collected conditional and sentiment data to the server. The server receives this data and extracts relevant information from its internal data aggregation unit. Machine learning algorithms are used at this stage, and the server analyzes the data using multiple methods such as random forests and neural networks. The analysis results are generated as a list of optimal information processing devices. This list is constructed considering not only functional requirements but also emotional aspects.

[0149] As a result, the server sends the selected model to the terminal. The terminal displays details of the proposed device to the user through the user interface. At this point, the user can understand the reasons for the selection and gain satisfaction with their choice. For example, if the user is in a state where "camera performance is important, but I feel uneasy because I don't understand the technical details," the system will recommend an easy-to-understand explanation and an appropriate information processing device to alleviate that anxiety.

[0150] An example of a prompt message might be: "The user values ​​camera performance but is apprehensive about advanced technical specifications. Please provide a concise and persuasive proposal and outline the next selection steps."

[0151] This allows users to confidently choose the information processing device that best suits their needs, leading to improved accuracy in product selection and increased customer satisfaction.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The user inputs their requests and requirements for the information processing device into the terminal. This input includes specific items such as screen size, camera performance, and budget. Furthermore, the user can express emotions through voice input or facial expressions. At this stage, the terminal treats this information as an initial dataset and records it for subsequent processing.

[0155] Step 2:

[0156] The device uses a built-in emotion engine to analyze the user's voice tone and facial expression data in real time. The resulting emotion data is combined with the user's condition data into a single composite dataset. Here, the degree of stress and excitement is quantified from the voice and added to the text-based request data.

[0157] Step 3:

[0158] The terminal securely transmits the prepared composite dataset to the server. SSL communication and other methods are used for transmission to protect data confidentiality. The input requests and analyzed sentiment data are integrated and arrive at the server, becoming the base data for the next stage of processing.

[0159] Step 4:

[0160] The server processes the received data using an analysis tool and extracts relevant information processing device data from the data aggregation unit. Specifically, it uses a machine learning algorithm to generate a list of devices that match the user's criteria. Here, the analysis tool uses SQL queries and other methods to quickly identify candidates from a large amount of data.

[0161] Step 5:

[0162] The server applies machine learning algorithms (e.g., random forest, neural network) to evaluate candidate devices, taking into account user sentiment data. This evaluation identifies the device predicted to provide the greatest user satisfaction. The generated recommendation results take all conditions and sentiment factors into consideration.

[0163] Step 6:

[0164] The server returns the results of the selection of the optimal information processing device to the terminal. The terminal presents the user with the features and reasons for the selected device through an intuitive and easy-to-understand interface. For example, it summarizes the image, advantages, and sentiment analysis results of the recommended device and displays them in a way that is easy for the user to understand. Based on these results, the user can decide whether to purchase or conduct further research.

[0165] (Application Example 2)

[0166] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0167] In today's online shopping and information device selection environment, users are often overwhelmed by a vast number of choices, and finding products that match their emotions and preferences is particularly difficult. Therefore, there is a need for a system that allows users to select devices and products that best suit their needs without stress. Personalized suggestions based on emotions could significantly improve user satisfaction.

[0168] 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.

[0169] In this invention, the server includes a communication configuration means for receiving multiple conditional information from a user, an analysis configuration means for extracting a group of data from an information set relating to an information processing terminal based on the received conditional information, a generation configuration means for selecting the optimal information processing terminal using the extracted data group, and an emotion analysis configuration means for recognizing the user's emotional state and making product suggestions based on that emotion. This makes it possible to suggest terminals and products optimized for the user's emotions and individual conditions.

[0170] "Users" refer to individuals or groups who use a system or device, and who influence the system's operation through their actions and requests.

[0171] "Multiple conditional information" refers to the preferences and requirements that users set when selecting a device or product, and includes information such as pricing, feature selection, and intended use.

[0172] "Communication configuration" refers to the mechanism by which a system receives input from users, and is a means of transmitting data through a network or interface.

[0173] An "information processing terminal" is a device that processes digital data and provides information to users, and includes smartphones, tablets, and computers.

[0174] A "data set" is a collection of data related to an information processing terminal, including information such as specifications, performance, price, and evaluation.

[0175] An "information collection" refers to a large-scale information resource such as a database or cloud storage where various types of data are accumulated.

[0176] An "analysis configuration" is a system component used to analyze data received from users and extract information that meets the required criteria.

[0177] A "generative configuration" is a system component that has the function of selecting the most suitable information processing terminal based on the analyzed data set.

[0178] A "notification structure" is a mechanism for conveying selected information to users, and it is a means of presenting information through visual and auditory means.

[0179] "Emotional state" refers to the mental and sensory condition of the user, and includes the aspects of their emotions inferred from their facial expressions, voice, etc.

[0180] "Emotional analysis configuration" refers to the components of a system that recognizes the emotional state of users and reflects the results in product recommendations.

[0181] This invention is implemented as an online shopping and information terminal selection system incorporating emotion analysis. This system consists of an emotion engine, a server capable of communicating with it, a terminal that accepts user input, and a database.

[0182] When a user uses a device to input selection criteria for the device and products, the device sends these criteria to the server. During this process, the device uses its camera and microphone to record the user's facial expressions and voice, and analyzes their emotional state through an emotion engine. Software such as Google® Cloud Speech-to-Text API and Microsoft® Azure® Emotion API may be used for this analysis.

[0183] The server selects the optimal information processing terminal from the information set based on the received conditional information and analyzed sentiment data. The selection process uses a generative engine employing learning algorithms such as TensorFlow or PyTorch. Data on the selected terminal and products is sent to the terminal via a notification configuration.

[0184] For example, if a user is looking for a new smartphone, in addition to the entered price range and desired features, a list of products the user has shown interest in will be displayed. Furthermore, if the system senses interest or anxiety from the user's facial expressions, it will present product recommendations and explanations tailored to those emotions.

[0185] An example of a prompt for the generative AI model would be written in the format of, "The user's current emotional state is <emotion>. Please suggest five products that are best suited to this user." This allows for product suggestions that are tailored to the individual user's preferences and emotions.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The user uses a device to enter information about the product or service they wish to purchase. This information includes price, features, brand, etc. The entered information is collected by the device and prepared for the next processing step.

[0189] Step 2:

[0190] The device uses its built-in camera and microphone to record the user's voice and facial expressions. This data is used to reveal the user's emotional state. The voice data is converted to text using the Google Cloud Speech-to-Text API, and the facial expression data is analyzed using the Microsoft Azure Emotion API. This analyzes the user's emotional state and outputs it as numerical data.

[0191] Step 3:

[0192] The terminal sends the conditional information collected in step 1 and the emotion data analyzed in step 2 to the server. The server receives this data and begins preparing to extract relevant data sets from the information collection.

[0193] Step 4:

[0194] The server extracts appropriate product and information processing terminal data sets from the information set based on the received conditional information and sentiment data. Database search technology and an analysis engine are used for this process. The extracted data sets are prepared to best match the user's needs and sentiments.

[0195] Step 5:

[0196] The server evaluates the extracted data using a generative AI model to generate information on the most appropriate product. Here, machine learning algorithms such as TensorFlow and PyTorch calculate suitability based on the user's emotional state, and the optimal product is selected.

[0197] Step 6:

[0198] Information about the selected products is transmitted from the server to the terminal. The terminal receives this information and displays it to the user in a visually easy-to-understand format. This allows the user to understand how their criteria and preferences were taken into consideration when suggesting products.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] This invention is implemented as a system with a series of functions to efficiently select a personal digital information terminal (PDI) device. First, the user inputs their requirements for purchasing a PDI device through a dedicated interface on the terminal. The input information is diverse, including, for example, budget, required functions, desired screen size, and camera performance.

[0216] The terminal sends the entered information to the server. The server searches the database based on the received information and extracts data for mobile information terminal devices that match the criteria. The database contains detailed information on various types of terminals, including information on the latest models.

[0217] The extracted data is further analyzed by a generation mechanism within the server, and the device that best meets the user's requirements is selected. Here, the server can use machine learning algorithms to make selections that take into account past user selection trends and market trends.

[0218] The server then sends a list of the most suitable personal digital assistant (PDAs) devices to the terminal. The terminal displays this list to the user, allowing them to review the suggested options in detail and make a purchase decision.

[0219] For example, if a user enters conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible," the system will select the most appropriate personal digital assistant (PDCA) device based on these conditions and notify the user. The user can then purchase the device that best suits their needs based on the presented options.

[0220] As described above, the present invention is implemented as a system that can flexibly respond to diverse inputs from users and provide the optimal portable information terminal device quickly and accurately.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] Users use their devices to input requirements such as budget, necessary features, desired screen size, and camera performance. Input is done through a dedicated interface, providing detailed information through selections and free-text descriptions for each item.

[0224] Step 2:

[0225] The terminal converts the information entered by the user into a data format and sends it to the server. A secure communication protocol is used for transmission to maintain the confidentiality of the information.

[0226] Step 3:

[0227] The server analyzes the received user data and searches the database based on each condition item. First, it narrows down the results based on basic conditions such as budget and OS, and then filters further based on required functions and special specifications.

[0228] Step 4:

[0229] The server uses a machine learning algorithm to select the mobile information terminal device that best matches user needs from the filtered list of candidates. Past user data and market trends are also taken into consideration to improve the accuracy of the selection.

[0230] Step 5:

[0231] The server generates information on the selected optimal mobile device and sends it to the user's device. The information sent includes the device name, main functions, price, and a link to purchase it.

[0232] Step 6:

[0233] The terminal displays information received from the server in a format that is easy for the user to understand. Based on the displayed information, the user can check the details of each terminal and make a purchase decision.

[0234] (Example 1)

[0235] 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."

[0236] In today's information society, a wide variety of electronic devices exist on the market, making it difficult for users to efficiently select products that meet their needs. In particular, when users have diverse requirements, gathering and analyzing information to make the optimal choice becomes complex, and there is a need for a system that enables rapid and accurate selection.

[0237] 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.

[0238] In this invention, the server includes a receiving means for receiving multiple requirements from a user, a querying means for retrieving information on relevant electronic devices from an information set based on the received requirements, and a selection means for selecting the optimal electronic device using the retrieved information. This makes it possible to quickly and accurately analyze the diverse requirements of users and provide the most suitable device.

[0239] A "reception method" is a system that receives user requirements as input and records them as data that can be processed in digital format.

[0240] A "request method" is a tool that searches for appropriate information from an information set based on the received requirements and extracts relevant results.

[0241] A "selection mechanism" is a system that analyzes the retrieved information and processes data to determine which electronic device best meets the user's requirements.

[0242] "Presentation means" refers to technologies for conveying information from selected electronic devices to users through visual or auditory means.

[0243] A "learning model" is an algorithmic system that analyzes specific patterns and trends based on past data to make predictions or selections regarding newly appearing data.

[0244] An "information collection" is a database or data store in which diverse data related to electronic devices is gathered, structured, and stored.

[0245] This invention is implemented as a system for efficiently selecting electronic equipment. This system recommends the most suitable electronic equipment based on the requirements specified by the user. Specific embodiments are described below.

[0246] The user first launches a dedicated application using a mobile device. This application provides an interface for entering user requirements, allowing users to input information such as budget, necessary functions, desired screen size, and camera performance. Intuitive UI elements such as dropdown lists, sliders, and text boxes are provided for information input.

[0247] The terminal converts the information entered by the user into JSON format data and securely sends it to the server using the HTTPS protocol. The transmitted information is then parsed on the server via an intermediary.

[0248] The server uses query tools to search an information database based on the received requirements. This information database contains detailed information about electronic devices, including the latest models and their features.

[0249] Furthermore, the server uses generative AI models and learning models as selection tools. This allows it to analyze and score the most appropriate electronic devices based on past selection history and market trends. TensorFlow and other machine learning libraries are utilized for the analysis.

[0250] The server then encrypts the list of selected electronic devices and sends it to the terminal in JSON format.

[0251] The terminal uses a presentation method to display the selection results to the user. The results are visualized as a card view or list view, making it easy for the user to compare devices and make a purchase decision.

[0252] For example, users can specify their requirements using prompt statements such as "I'm looking for the latest smartphone with a high-resolution camera and a budget of under 50,000 yen" or "Tell me about the latest smartphone models with a large screen that fit my budget." In this way, the present invention assists users in quickly and accurately selecting the electronic device they want.

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

[0254] Step 1:

[0255] The user launches a dedicated application on their mobile device and enters their requirements using a requirements input interface. Specifically, they select and enter information such as budget, features, screen size, and camera performance. This information becomes the input data. The entered data is converted into JSON format within the application.

[0256] Step 2:

[0257] The terminal sends the converted JSON data to the server. Since the transmission method uses the HTTPS protocol, data security is maintained. The transmitted JSON data becomes the server's input data, and after reception, it is processed by the receiving mechanism for analysis.

[0258] Step 3:

[0259] Based on the received input data, the server initiates a query to the information set in order to perform a database search. It generates an SQL query and uses this query to extract information about electronic devices that match the user's requirements. The extracted information becomes intermediate output data within the server and is used for the next processing step.

[0260] Step 4:

[0261] The server further analyzes the extracted information using generative AI models and learning models. It applies a scoring algorithm that considers past user data and market trends to select the most suitable electronic device. The output here is a list of electronic devices best suited to the user's requirements.

[0262] Step 5:

[0263] The server repackages the selected electronic device list in JSON format, encrypts it, and sends it to the terminal. This list becomes the input data for the terminal and is visually displayed to the user via a presentation method.

[0264] Step 6:

[0265] The device displays the received selection list on the UI. Using card view or list view formats, users can easily compare and select items. The design format is particularly important here, requiring a user-friendly and easy-to-understand layout. This display is the device's final output, and users use it as a reference to make their purchase decision.

[0266] (Application Example 1)

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

[0268] When users purchase information equipment, they often face the challenge of quickly and easily selecting the product that best suits their needs from a wide range of options. There is a need for systems that efficiently assist users in making the optimal choice.

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

[0270] In this invention, the server includes communication means for receiving multiple condition inputs from a user, analysis means for extracting data on relevant information devices from a database based on the received conditions, and generation means for selecting the optimal information device using the extracted data. This makes it possible to propose information devices based on precise conditions.

[0271] A "user" is an individual or corporation looking for a product that meets their needs when purchasing information equipment.

[0272] "Condition input" refers to information that indicates the selection criteria specified by the user, such as budget, performance, and feature requirements.

[0273] "Communication means" refers to methods and technologies for transmitting user input conditions to a system.

[0274] "Information equipment" is a general term for electronic devices such as mobile phones and tablets.

[0275] "Analysis means" refers to a technology for extracting data from a database of relevant information devices based on received condition inputs.

[0276] A "database" is a collection of data that systematically stores detailed information about information devices.

[0277] "Generation means" refers to methods and technologies for identifying the optimal information device from the extracted information.

[0278] "Notification means" refers to methods and technologies for informing users of information about selected information devices.

[0279] A "generative artificial intelligence model" is an artificial intelligence technology used to automate product recommendations based on user criteria.

[0280] The system implementing this invention uses a communication device that accepts user input conditions, a server that analyzes information in a database, and a generative artificial intelligence model as a generation means. The user inputs their conditions on a smartphone application or web platform. For example, they might input conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible."

[0281] The server searches the database based on the user's conditions received via the communication device and extracts the data of the corresponding information devices. The database stores detailed information on a variety of information devices, and the server generates a list that best matches the user's conditions from this. For this generation, a machine learning algorithm that has learned past selection tendencies and market trends is used.

[0282] The generation artificial intelligence model as the generation means uses the extracted data to list up the information devices optimal for the user. In this process, a text prompt is generated to prepare the conditions necessary for product recommendation. An example of the prompt sentence is "The user is looking for the latest smartphone with a high-resolution camera within 50,000 yen. Please propose the most appropriate options based on this condition."

[0283] The user can finally check the candidates of the information devices notified from the server on the terminal and select and purchase a satisfactory product. In this way, accurate and rapid selection of information devices becomes possible, and the user's purchase experience can be greatly improved.

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

[0285] Step 1:

[0286] The user inputs conditions using a smartphone application or a web platform. The inputs include the budget, necessary functions, specific usage purposes, etc. The terminal formats these input data and prepares to send them to the server.

[0287] Step 2:

[0288] The server analyzes the received user condition input. In this analysis, an efficient search algorithm is used to match the user conditions with the information in the database. As a result, a list of information devices that meet the conditions is generated by the server.

[0289] Step 3:

[0290] The server further analyzes the information device data extracted based on user criteria using a generative artificial intelligence model. In this process, machine learning algorithms take into account past selection trends and market trends to identify the product that best fits the selection criteria. Here, appropriate prompt statements generated by the generative AI model are utilized to improve recommendation accuracy.

[0291] Step 4:

[0292] The server generates a list of optimized information devices and notifies the user's terminal. This notification includes detailed information and ratings of recommended products. The terminal displays this information visually through its user interface, making it easy for the user to understand.

[0293] Step 5:

[0294] The user compares the options based on the provided information and decides which information device to purchase. The terminal feeds the user's selection back to the server, preparing it to be used as data to improve the accuracy of recommendations in the future.

[0295] 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.

[0296] This invention is implemented as a system that combines an emotion engine to make the selection process for personal information terminal devices more sophisticated. First, the user inputs their wishes and requirements for a personal information terminal device through the terminal. Here, the user sets specific requirements such as budget, desired functions, screen size, and camera performance. During this input, the emotion engine installed in the terminal analyzes the user's emotions from the input content, voice, and facial expressions.

[0297] The terminal sends collected conditional data and sentiment data to the server. The server receives this data and searches the database using an analysis tool. The analysis tool first extracts terminal information that matches the user's conditions, and then, taking sentiment data into consideration, generates customized results that reflect the user's expectations and preferences.

[0298] The server uses machine learning algorithms to evaluate candidate mobile devices and select the model that is likely to provide the highest user satisfaction. The emotion engine plays a crucial role in this selection process, enabling suggestions that reflect the user's latent needs and interests.

[0299] The server then sends the selected information to the terminal. The terminal presents the results to the user in an easy-to-understand format. Here, the user can understand how their emotions are reflected and why a particular terminal was recommended.

[0300] For example, if a user prioritizes camera functionality but feels stressed because they don't understand the latest technology, the emotion engine can recognize this and suggest the most suitable device based on the user's knowledge level, along with a simple and easy-to-understand explanation to alleviate the stress.

[0301] Thus, the present invention is implemented as a system that enables the selection of more personalized mobile information terminal devices through the use of an emotion engine, thereby improving user satisfaction.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The user inputs specific requirements for a mobile information terminal (PDCA) device via the terminal. These include budget, necessary functions, screen size, and camera performance. The system also captures the user's emotions through facial expressions and voice.

[0305] Step 2:

[0306] The terminal converts the input condition data and the captured emotion data into a data format and transmits them to the server using secure communication means.

[0307] Step 3:

[0308] The server analyzes the received data, searches the database, and extracts candidate mobile information terminal devices that meet the user's conditions.

[0309] Step 4:

[0310] The server's emotion engine analyzes the user's emotion data and evaluates stress, expected value, preferences, etc.

[0311] Step 5:

[0312] The server uses the result of the emotion analysis to select the mobile information terminal device that best meets the user's needs from the candidates through a machine learning algorithm. At this time, a customized proposal considering the user's emotional state is made.

[0313] Step 6:

[0314] The server transmits the detailed information of the selected mobile information terminal device to the terminal.

[0315] Step 7:

[0316] The terminal visually displays the received information to the user. Based on the presented information, the user can select the optimal mobile information terminal device while checking the reasons for the proposal and the results of the emotion analysis.

[0317] (Example 2)

[0318] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0319] When users select information processing equipment, they want to consider subjective feelings in addition to demand, budget, and functionality. However, conventional systems do not reflect these emotional elements, resulting in insufficient improvement of the user experience.

[0320] 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.

[0321] In this invention, the server includes communication means for receiving multiple conditional and emotional data from the user, analysis means for extracting data from a data aggregation unit for relevant information processing devices based on the received conditional and emotional data, and generation means for selecting the optimal information processing device using the extracted data. This makes it possible to select a more personalized information processing device that reflects the user's emotions.

[0322] "Multiple user requirements" refer to the specifications and constraints that users desire regarding the selection of an information processing device, specifically including requirements such as budget, functionality, screen size, and camera performance.

[0323] "Emotional data" refers to information about a user's subjective psychological state, analyzed from their facial expressions and tone of voice, and reflects their unconscious expectations and preferences.

[0324] A "communication method" is a system equipped with interfaces and protocols for sending user conditions and emotional data to a server, and is a means for smoothly receiving and transmitting data.

[0325] "Analysis means" refers to a means for performing a process of searching a data storage unit based on conditional and emotional data received from the user and narrowing down the candidates for related information processing devices.

[0326] The "Data Accumulation Unit" is a database that stores various types of information related to the information processing device, and this system is used as a search target during the selection process.

[0327] "Generation means" refers to the means of executing processes and algorithms used to select the optimal information processing device from the candidates narrowed down by the analysis means, and in particular, the selection is carried out by applying machine learning algorithms.

[0328] A "notification means" is a system equipped with an interface or protocol for conveying the results of a selected information processing device to the user, and is a means of presenting the results in a way that is easy for the user to understand.

[0329] This invention is specifically implemented as a system that combines multiple conditions and sentiment data to highly personalize the selection process for information processing equipment.

[0330] The user first uses a device to input their requests and requirements. These requests are specific functional preferences, such as screen size and camera performance. During input, the device has a function to capture the user's voice and facial expressions, thereby acquiring the user's emotional data. The device is equipped with an advanced algorithm called an emotion engine, which is used to analyze the emotional data in real time. This analysis allows us to understand how emotions, in addition to the user's requirements, influence their selection.

[0331] The terminal sends collected conditional and sentiment data to the server. The server receives this data and extracts relevant information from its internal data aggregation unit. Machine learning algorithms are used at this stage, and the server analyzes the data using multiple methods such as random forests and neural networks. The analysis results are generated as a list of optimal information processing devices. This list is constructed considering not only functional requirements but also emotional aspects.

[0332] As a result, the server sends the selected model to the terminal. The terminal displays details of the proposed device to the user through the user interface. At this point, the user can understand the reasons for the selection and gain satisfaction with their choice. For example, if the user is in a state where "camera performance is important, but I feel uneasy because I don't understand the technical details," the system will recommend an easy-to-understand explanation and an appropriate information processing device to alleviate that anxiety.

[0333] An example of a prompt message might be: "The user values ​​camera performance but is apprehensive about advanced technical specifications. Please provide a concise and persuasive proposal and outline the next selection steps."

[0334] This allows users to confidently choose the information processing device that best suits their needs, leading to improved accuracy in product selection and increased customer satisfaction.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The user inputs their requests and requirements for the information processing device into the terminal. This input includes specific items such as screen size, camera performance, and budget. Furthermore, the user can express emotions through voice input or facial expressions. At this stage, the terminal treats this information as an initial dataset and records it for subsequent processing.

[0338] Step 2:

[0339] The device uses a built-in emotion engine to analyze the user's voice tone and facial expression data in real time. The resulting emotion data is combined with the user's condition data into a single composite dataset. Here, the degree of stress and excitement is quantified from the voice and added to the text-based request data.

[0340] Step 3:

[0341] The terminal securely transmits the prepared composite dataset to the server. SSL communication and other methods are used for transmission to protect data confidentiality. The input requests and analyzed sentiment data are integrated and arrive at the server, becoming the base data for the next stage of processing.

[0342] Step 4:

[0343] The server processes the received data using an analysis tool and extracts relevant information processing device data from the data aggregation unit. Specifically, it uses a machine learning algorithm to generate a list of devices that match the user's criteria. Here, the analysis tool uses SQL queries and other methods to quickly identify candidates from a large amount of data.

[0344] Step 5:

[0345] The server applies machine learning algorithms (e.g., random forest, neural network) to evaluate candidate devices, taking into account user sentiment data. This evaluation identifies the device predicted to provide the greatest user satisfaction. The generated recommendation results take all conditions and sentiment factors into consideration.

[0346] Step 6:

[0347] The server returns the results of the selection of the optimal information processing device to the terminal. The terminal presents the user with the features and reasons for the selected device through an intuitive and easy-to-understand interface. For example, it summarizes the image, advantages, and sentiment analysis results of the recommended device and displays them in a way that is easy for the user to understand. Based on these results, the user can decide whether to purchase or conduct further research.

[0348] (Application Example 2)

[0349] 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."

[0350] In today's online shopping and information device selection environment, users are often overwhelmed by a vast number of choices, and finding products that match their emotions and preferences is particularly difficult. Therefore, there is a need for a system that allows users to select devices and products that best suit their needs without stress. Personalized suggestions based on emotions could significantly improve user satisfaction.

[0351] 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.

[0352] In this invention, the server includes a communication configuration means for receiving multiple conditional information from a user, an analysis configuration means for extracting a group of data from an information set relating to an information processing terminal based on the received conditional information, a generation configuration means for selecting the optimal information processing terminal using the extracted data group, and an emotion analysis configuration means for recognizing the user's emotional state and making product suggestions based on that emotion. This makes it possible to suggest terminals and products optimized for the user's emotions and individual conditions.

[0353] "Users" refer to individuals or groups who use a system or device, and who influence the system's operation through their actions and requests.

[0354] "Multiple conditional information" refers to the preferences and requirements that users set when selecting a device or product, and includes information such as pricing, feature selection, and intended use.

[0355] "Communication configuration" refers to the mechanism by which a system receives input from users, and is a means of transmitting data through a network or interface.

[0356] An "information processing terminal" is a device that processes digital data and provides information to users, and includes smartphones, tablets, and computers.

[0357] A "data set" is a collection of data related to an information processing terminal, including information such as specifications, performance, price, and evaluation.

[0358] An "information collection" refers to a large-scale information resource such as a database or cloud storage where various types of data are accumulated.

[0359] An "analysis configuration" is a system component used to analyze data received from users and extract information that meets the required criteria.

[0360] A "generative configuration" is a system component that has the function of selecting the most suitable information processing terminal based on the analyzed data set.

[0361] A "notification structure" is a mechanism for conveying selected information to users, and it is a means of presenting information through visual and auditory means.

[0362] "Emotional state" refers to the mental and sensory condition of the user, and includes the aspects of their emotions inferred from their facial expressions, voice, etc.

[0363] "Emotional analysis configuration" refers to the components of a system that recognizes the emotional state of users and reflects the results in product recommendations.

[0364] This invention is implemented as an online shopping and information terminal selection system incorporating emotion analysis. This system consists of an emotion engine, a server capable of communicating with it, a terminal that accepts user input, and a database.

[0365] When a user uses a device to input selection criteria for the device and products, the device sends these criteria to the server. During this process, the device uses its camera and microphone to record the user's facial expressions and voice, and analyzes their emotional state through an emotion engine. Software such as the Google Cloud Speech-to-Text API or the Microsoft Azure Emotion API may be used for this analysis.

[0366] The server selects the optimal information processing terminal from the information set based on the received conditional information and analyzed sentiment data. The selection process uses a generative engine employing learning algorithms such as TensorFlow or PyTorch. Data on the selected terminal and products is sent to the terminal via a notification configuration.

[0367] For example, if a user is looking for a new smartphone, in addition to the entered price range and desired features, a list of products the user has shown interest in will be displayed. Furthermore, if the system senses interest or anxiety from the user's facial expressions, it will present product recommendations and explanations tailored to those emotions.

[0368] An example of a prompt for the generative AI model would be written in the format of, "The user's current emotional state is <emotion>. Please suggest five products that are best suited to this user." This allows for product suggestions that are tailored to the individual user's preferences and emotions.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The user uses a device to enter information about the product or service they wish to purchase. This information includes price, features, brand, etc. The entered information is collected by the device and prepared for the next processing step.

[0372] Step 2:

[0373] The device uses its built-in camera and microphone to record the user's voice and facial expressions. This data is used to reveal the user's emotional state. The voice data is converted to text using the Google Cloud Speech-to-Text API, and the facial expression data is analyzed using the Microsoft Azure Emotion API. This analyzes the user's emotional state and outputs it as numerical data.

[0374] Step 3:

[0375] The terminal sends the conditional information collected in step 1 and the emotion data analyzed in step 2 to the server. The server receives this data and begins preparing to extract relevant data sets from the information collection.

[0376] Step 4:

[0377] The server extracts appropriate product and information processing terminal data sets from the information set based on the received conditional information and sentiment data. Database search technology and an analysis engine are used for this process. The extracted data sets are prepared to best match the user's needs and sentiments.

[0378] Step 5:

[0379] The server evaluates the extracted data using a generative AI model to generate information on the most appropriate product. Here, machine learning algorithms such as TensorFlow and PyTorch calculate suitability based on the user's emotional state, and the optimal product is selected.

[0380] Step 6:

[0381] Information about the selected products is transmitted from the server to the terminal. The terminal receives this information and displays it to the user in a visually easy-to-understand format. This allows the user to understand how their criteria and preferences were taken into consideration when suggesting products.

[0382] 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.

[0383] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0384] 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.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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".

[0398] This invention is implemented as a system with a series of functions to efficiently select a personal digital information terminal (PDI) device. First, the user inputs their requirements for purchasing a PDI device through a dedicated interface on the terminal. The input information is diverse, including, for example, budget, required functions, desired screen size, and camera performance.

[0399] The terminal sends the entered information to the server. The server searches the database based on the received information and extracts data for mobile information terminal devices that match the criteria. The database contains detailed information on various types of terminals, including information on the latest models.

[0400] The extracted data is further analyzed by a generation mechanism within the server, and the device that best meets the user's requirements is selected. Here, the server can use machine learning algorithms to make selections that take into account past user selection trends and market trends.

[0401] The server then sends a list of the most suitable personal digital assistant (PDAs) devices to the terminal. The terminal displays this list to the user, allowing them to review the suggested options in detail and make a purchase decision.

[0402] For example, if a user enters conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible," the system will select the most appropriate personal digital assistant (PDCA) device based on these conditions and notify the user. The user can then purchase the device that best suits their needs based on the presented options.

[0403] As described above, the present invention is implemented as a system that can flexibly respond to diverse inputs from users and provide the optimal portable information terminal device quickly and accurately.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] Users use their devices to input requirements such as budget, necessary features, desired screen size, and camera performance. Input is done through a dedicated interface, providing detailed information through selections and free-text descriptions for each item.

[0407] Step 2:

[0408] The terminal converts the information entered by the user into a data format and sends it to the server. A secure communication protocol is used for transmission to maintain the confidentiality of the information.

[0409] Step 3:

[0410] The server analyzes the received user data and searches the database based on each condition item. First, it narrows down the results based on basic conditions such as budget and OS, and then filters further based on required functions and special specifications.

[0411] Step 4:

[0412] The server uses a machine learning algorithm to select the mobile information terminal device that best matches user needs from the filtered list of candidates. Past user data and market trends are also taken into consideration to improve the accuracy of the selection.

[0413] Step 5:

[0414] The server generates information on the selected optimal mobile device and sends it to the user's device. The information sent includes the device name, main functions, price, and a link to purchase it.

[0415] Step 6:

[0416] The terminal displays information received from the server in a format that is easy for the user to understand. Based on the displayed information, the user can check the details of each terminal and make a purchase decision.

[0417] (Example 1)

[0418] 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."

[0419] In today's information society, a wide variety of electronic devices exist on the market, making it difficult for users to efficiently select products that meet their needs. In particular, when users have diverse requirements, gathering and analyzing information to make the optimal choice becomes complex, and there is a need for a system that enables rapid and accurate selection.

[0420] 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.

[0421] In this invention, the server includes a receiving means for receiving multiple requirements from a user, a querying means for retrieving information on relevant electronic devices from an information set based on the received requirements, and a selection means for selecting the optimal electronic device using the retrieved information. This makes it possible to quickly and accurately analyze the diverse requirements of users and provide the most suitable device.

[0422] A "reception method" is a system that receives user requirements as input and records them as data that can be processed in digital format.

[0423] A "request method" is a tool that searches for appropriate information from an information set based on the received requirements and extracts relevant results.

[0424] A "selection mechanism" is a system that analyzes the retrieved information and processes data to determine which electronic device best meets the user's requirements.

[0425] "Presentation means" refers to technologies for conveying information from selected electronic devices to users through visual or auditory means.

[0426] A "learning model" is an algorithmic system that analyzes specific patterns and trends based on past data to make predictions or selections regarding newly appearing data.

[0427] An "information collection" is a database or data store in which diverse data related to electronic devices is gathered, structured, and stored.

[0428] This invention is implemented as a system for efficiently selecting electronic equipment. This system recommends the most suitable electronic equipment based on the requirements specified by the user. Specific embodiments are described below.

[0429] The user first launches a dedicated application using a mobile device. This application provides an interface for entering user requirements, allowing users to input information such as budget, necessary functions, desired screen size, and camera performance. Intuitive UI elements such as dropdown lists, sliders, and text boxes are provided for information input.

[0430] The terminal converts the information entered by the user into JSON format data and securely sends it to the server using the HTTPS protocol. The transmitted information is then parsed on the server via an intermediary.

[0431] The server uses query tools to search an information database based on the received requirements. This information database contains detailed information about electronic devices, including the latest models and their features.

[0432] Furthermore, the server uses generative AI models and learning models as selection tools. This allows it to analyze and score the most appropriate electronic devices based on past selection history and market trends. TensorFlow and other machine learning libraries are utilized for the analysis.

[0433] The server then encrypts the list of selected electronic devices and sends it to the terminal in JSON format.

[0434] The terminal uses a presentation method to display the selection results to the user. The results are visualized as a card view or list view, making it easy for the user to compare devices and make a purchase decision.

[0435] For example, users can specify their requirements using prompt statements such as "I'm looking for the latest smartphone with a high-resolution camera and a budget of under 50,000 yen" or "Tell me about the latest smartphone models with a large screen that fit my budget." In this way, the present invention assists users in quickly and accurately selecting the electronic device they want.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The user launches a dedicated application on their mobile device and enters their requirements using a requirements input interface. Specifically, they select and enter information such as budget, features, screen size, and camera performance. This information becomes the input data. The entered data is converted into JSON format within the application.

[0439] Step 2:

[0440] The terminal sends the converted JSON data to the server. Since the transmission method uses the HTTPS protocol, data security is maintained. The transmitted JSON data becomes the server's input data, and after reception, it is processed by the receiving mechanism for analysis.

[0441] Step 3:

[0442] Based on the received input data, the server initiates a query to the information set in order to perform a database search. It generates an SQL query and uses this query to extract information about electronic devices that match the user's requirements. The extracted information becomes intermediate output data within the server and is used for the next processing step.

[0443] Step 4:

[0444] The server further analyzes the extracted information using generative AI models and learning models. It applies a scoring algorithm that considers past user data and market trends to select the most suitable electronic device. The output here is a list of electronic devices best suited to the user's requirements.

[0445] Step 5:

[0446] The server repackages the selected electronic device list in JSON format, encrypts it, and sends it to the terminal. This list becomes the input data for the terminal and is visually displayed to the user via a presentation method.

[0447] Step 6:

[0448] The device displays the received selection list on the UI. Using card view or list view formats, users can easily compare and select items. The design format is particularly important here, requiring a user-friendly and easy-to-understand layout. This display is the device's final output, and users use it as a reference to make their purchase decision.

[0449] (Application Example 1)

[0450] 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."

[0451] When users purchase information equipment, they often face the challenge of quickly and easily selecting the product that best suits their needs from a wide range of options. There is a need for systems that efficiently assist users in making the optimal choice.

[0452] 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.

[0453] In this invention, the server includes communication means for receiving multiple condition inputs from a user, analysis means for extracting data on relevant information devices from a database based on the received conditions, and generation means for selecting the optimal information device using the extracted data. This makes it possible to propose information devices based on precise conditions.

[0454] A "user" is an individual or corporation looking for a product that meets their needs when purchasing information equipment.

[0455] "Condition input" refers to information that indicates the selection criteria specified by the user, such as budget, performance, and feature requirements.

[0456] "Communication means" refers to methods and technologies for transmitting user input conditions to a system.

[0457] "Information equipment" is a general term for electronic devices such as mobile phones and tablets.

[0458] "Analysis means" refers to a technology for extracting data from a database of relevant information devices based on received condition inputs.

[0459] A "database" is a collection of data that systematically stores detailed information about information devices.

[0460] "Generation means" refers to methods and technologies for identifying the optimal information device from the extracted information.

[0461] "Notification means" refers to methods and technologies for informing users of information about selected information devices.

[0462] A "generative artificial intelligence model" is an artificial intelligence technology used to automate product recommendations based on user criteria.

[0463] The system implementing this invention uses a communication device that accepts user input conditions, a server that analyzes information in a database, and a generative artificial intelligence model as a generation means. The user inputs their conditions on a smartphone application or web platform. For example, they might input conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible."

[0464] The server searches the database based on user conditions received via the communication device and extracts data for relevant information devices. The database contains detailed information on a wide variety of information devices, and the server generates a list that best matches the user's conditions. This generation uses a machine learning algorithm that has learned past selection trends and market trends.

[0465] The generative artificial intelligence model, used as a generation tool, uses extracted data to list the most suitable information devices for the user. In this process, it generates text prompts to provide the necessary conditions for product recommendations. An example of a prompt would be: "The user is looking for a new smartphone with a high-resolution camera for under 50,000 yen. Please suggest the most appropriate options based on these conditions."

[0466] Users can then review the list of available information devices notified by the server on their devices and select and purchase a product that meets their needs. This enables accurate and rapid selection of information devices, significantly improving the user's purchasing experience.

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] Users enter their requirements using a smartphone application or web platform. These requirements include budget, necessary features, and specific usage purposes. The device then formats this input data and prepares it for transmission to the server.

[0470] Step 2:

[0471] The server parses the user's input criteria. In this parsing, an efficient search algorithm is used to match the user criteria with information in the database. This generates a list of information devices that match the criteria.

[0472] Step 3:

[0473] The server further analyzes the information device data extracted based on user criteria using a generative artificial intelligence model. In this process, machine learning algorithms take into account past selection trends and market trends to identify the product that best fits the selection criteria. Here, appropriate prompt statements generated by the generative AI model are utilized to improve recommendation accuracy.

[0474] Step 4:

[0475] The server generates a list of optimized information devices and notifies the user's terminal. This notification includes detailed information and ratings of recommended products. The terminal displays this information visually through its user interface, making it easy for the user to understand.

[0476] Step 5:

[0477] The user compares the options based on the provided information and decides which information device to purchase. The terminal feeds the user's selection back to the server, preparing it to be used as data to improve the accuracy of recommendations in the future.

[0478] 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.

[0479] This invention is implemented as a system that combines an emotion engine to make the selection process for personal information terminal devices more sophisticated. First, the user inputs their wishes and requirements for a personal information terminal device through the terminal. Here, the user sets specific requirements such as budget, desired functions, screen size, and camera performance. During this input, the emotion engine installed in the terminal analyzes the user's emotions from the input content, voice, and facial expressions.

[0480] The terminal sends collected conditional data and sentiment data to the server. The server receives this data and searches the database using an analysis tool. The analysis tool first extracts terminal information that matches the user's conditions, and then, taking sentiment data into consideration, generates customized results that reflect the user's expectations and preferences.

[0481] The server uses machine learning algorithms to evaluate candidate mobile devices and select the model that is likely to provide the highest user satisfaction. The emotion engine plays a crucial role in this selection process, enabling suggestions that reflect the user's latent needs and interests.

[0482] The server then sends the selected information to the terminal. The terminal presents the results to the user in an easy-to-understand format. Here, the user can understand how their emotions are reflected and why a particular terminal was recommended.

[0483] For example, if a user prioritizes camera functionality but feels stressed because they don't understand the latest technology, the emotion engine can recognize this and suggest the most suitable device based on the user's knowledge level, along with a simple and easy-to-understand explanation to alleviate the stress.

[0484] Thus, the present invention is implemented as a system that enables the selection of more personalized mobile information terminal devices through the use of an emotion engine, thereby improving user satisfaction.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The user inputs specific requirements for a mobile information terminal (PDCA) device via the terminal. These include budget, necessary functions, screen size, and camera performance. The system also captures the user's emotions through facial expressions and voice.

[0488] Step 2:

[0489] The terminal converts the input condition data and captured emotion data into a data format and sends it to the server using a secure communication method.

[0490] Step 3:

[0491] The server analyzes the received data, searches the database, and extracts candidate mobile information terminal devices that match the user's criteria.

[0492] Step 4:

[0493] The server's emotion engine analyzes user emotion data and evaluates stress levels, expectations, preferences, and other factors.

[0494] Step 5:

[0495] The server uses the results of sentiment analysis to select the mobile information terminal device that best matches the user's needs from among the candidates using a machine learning algorithm. During this process, it also provides customized suggestions that take into account the user's emotional state.

[0496] Step 6:

[0497] The server sends detailed information about the selected mobile information terminal device to the terminal.

[0498] Step 7:

[0499] The terminal visually displays the received information to the user. Based on the presented information, the user can select the optimal mobile information terminal device while reviewing the reasons for the suggestions and the results of sentiment analysis.

[0500] (Example 2)

[0501] 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."

[0502] When users select information processing equipment, they want to consider subjective feelings in addition to demand, budget, and functionality. However, conventional systems do not reflect these emotional elements, resulting in insufficient improvement of the user experience.

[0503] 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.

[0504] In this invention, the server includes communication means for receiving multiple conditional and emotional data from the user, analysis means for extracting data from a data aggregation unit for relevant information processing devices based on the received conditional and emotional data, and generation means for selecting the optimal information processing device using the extracted data. This makes it possible to select a more personalized information processing device that reflects the user's emotions.

[0505] "Multiple user requirements" refer to the specifications and constraints that users desire regarding the selection of an information processing device, specifically including requirements such as budget, functionality, screen size, and camera performance.

[0506] "Emotional data" refers to information about a user's subjective psychological state, analyzed from their facial expressions and tone of voice, and reflects their unconscious expectations and preferences.

[0507] A "communication method" is a system equipped with interfaces and protocols for sending user conditions and emotional data to a server, and is a means for smoothly receiving and transmitting data.

[0508] "Analysis means" refers to a means for performing a process of searching a data storage unit based on conditional and emotional data received from the user and narrowing down the candidates for related information processing devices.

[0509] The "Data Accumulation Unit" is a database that stores various types of information related to the information processing device, and this system is used as a search target during the selection process.

[0510] "Generation means" refers to the means of executing processes and algorithms used to select the optimal information processing device from the candidates narrowed down by the analysis means, and in particular, the selection is carried out by applying machine learning algorithms.

[0511] A "notification means" is a system equipped with an interface or protocol for conveying the results of a selected information processing device to the user, and is a means of presenting the results in a way that is easy for the user to understand.

[0512] This invention is specifically implemented as a system that combines multiple conditions and sentiment data to highly personalize the selection process for information processing equipment.

[0513] The user first uses a device to input their requests and requirements. These requests are specific functional preferences, such as screen size and camera performance. During input, the device has a function to capture the user's voice and facial expressions, thereby acquiring the user's emotional data. The device is equipped with an advanced algorithm called an emotion engine, which is used to analyze the emotional data in real time. This analysis allows us to understand how emotions, in addition to the user's requirements, influence their selection.

[0514] The terminal sends collected conditional and sentiment data to the server. The server receives this data and extracts relevant information from its internal data aggregation unit. Machine learning algorithms are used at this stage, and the server analyzes the data using multiple methods such as random forests and neural networks. The analysis results are generated as a list of optimal information processing devices. This list is constructed considering not only functional requirements but also emotional aspects.

[0515] As a result, the server sends the selected model to the terminal. The terminal displays details of the proposed device to the user through the user interface. At this point, the user can understand the reasons for the selection and gain satisfaction with their choice. For example, if the user is in a state where "camera performance is important, but I feel uneasy because I don't understand the technical details," the system will recommend an easy-to-understand explanation and an appropriate information processing device to alleviate that anxiety.

[0516] An example of a prompt message might be: "The user values ​​camera performance but is apprehensive about advanced technical specifications. Please provide a concise and persuasive proposal and outline the next selection steps."

[0517] This allows users to confidently choose the information processing device that best suits their needs, leading to improved accuracy in product selection and increased customer satisfaction.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The user inputs their requests and requirements for the information processing device into the terminal. This input includes specific items such as screen size, camera performance, and budget. Furthermore, the user can express emotions through voice input or facial expressions. At this stage, the terminal treats this information as an initial dataset and records it for subsequent processing.

[0521] Step 2:

[0522] The device uses a built-in emotion engine to analyze the user's voice tone and facial expression data in real time. The resulting emotion data is combined with the user's condition data into a single composite dataset. Here, the degree of stress and excitement is quantified from the voice and added to the text-based request data.

[0523] Step 3:

[0524] The terminal securely transmits the prepared composite dataset to the server. SSL communication and other methods are used for transmission to protect data confidentiality. The input requests and analyzed sentiment data are integrated and arrive at the server, becoming the base data for the next stage of processing.

[0525] Step 4:

[0526] The server processes the received data using an analysis tool and extracts relevant information processing device data from the data aggregation unit. Specifically, it uses a machine learning algorithm to generate a list of devices that match the user's criteria. Here, the analysis tool uses SQL queries and other methods to quickly identify candidates from a large amount of data.

[0527] Step 5:

[0528] The server applies machine learning algorithms (e.g., random forest, neural network) to evaluate candidate devices, taking into account user sentiment data. This evaluation identifies the device predicted to provide the greatest user satisfaction. The generated recommendation results take all conditions and sentiment factors into consideration.

[0529] Step 6:

[0530] The server returns the results of the selection of the optimal information processing device to the terminal. The terminal presents the user with the features and reasons for the selected device through an intuitive and easy-to-understand interface. For example, it summarizes the image, advantages, and sentiment analysis results of the recommended device and displays them in a way that is easy for the user to understand. Based on these results, the user can decide whether to purchase or conduct further research.

[0531] (Application Example 2)

[0532] 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."

[0533] In today's online shopping and information device selection environment, users are often overwhelmed by a vast number of choices, and finding products that match their emotions and preferences is particularly difficult. Therefore, there is a need for a system that allows users to select devices and products that best suit their needs without stress. Personalized suggestions based on emotions could significantly improve user satisfaction.

[0534] 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.

[0535] In this invention, the server includes a communication configuration means for receiving multiple conditional information from a user, an analysis configuration means for extracting a group of data from an information set relating to an information processing terminal based on the received conditional information, a generation configuration means for selecting the optimal information processing terminal using the extracted data group, and an emotion analysis configuration means for recognizing the user's emotional state and making product suggestions based on that emotion. This makes it possible to suggest terminals and products optimized for the user's emotions and individual conditions.

[0536] "Users" refer to individuals or groups who use a system or device, and who influence the system's operation through their actions and requests.

[0537] "Multiple conditional information" refers to the preferences and requirements that users set when selecting a device or product, and includes information such as pricing, feature selection, and intended use.

[0538] "Communication configuration" refers to the mechanism by which a system receives input from users, and is a means of transmitting data through a network or interface.

[0539] An "information processing terminal" is a device that processes digital data and provides information to users, and includes smartphones, tablets, and computers.

[0540] A "data set" is a collection of data related to an information processing terminal, including information such as specifications, performance, price, and evaluation.

[0541] An "information collection" refers to a large-scale information resource such as a database or cloud storage where various types of data are accumulated.

[0542] An "analysis configuration" is a system component used to analyze data received from users and extract information that meets the required criteria.

[0543] A "generative configuration" is a system component that has the function of selecting the most suitable information processing terminal based on the analyzed data set.

[0544] A "notification structure" is a mechanism for conveying selected information to users, and it is a means of presenting information through visual and auditory means.

[0545] "Emotional state" refers to the mental and sensory condition of the user, and includes the aspects of their emotions inferred from their facial expressions, voice, etc.

[0546] "Emotional analysis configuration" refers to the components of a system that recognizes the emotional state of users and reflects the results in product recommendations.

[0547] This invention is implemented as an online shopping and information terminal selection system incorporating emotion analysis. This system consists of an emotion engine, a server capable of communicating with it, a terminal that accepts user input, and a database.

[0548] When a user uses a device to input selection criteria for the device and products, the device sends these criteria to the server. During this process, the device uses its camera and microphone to record the user's facial expressions and voice, and analyzes their emotional state through an emotion engine. Software such as the Google Cloud Speech-to-Text API or the Microsoft Azure Emotion API may be used for this analysis.

[0549] The server selects the optimal information processing terminal from the information set based on the received conditional information and analyzed sentiment data. The selection process uses a generative engine employing learning algorithms such as TensorFlow or PyTorch. Data on the selected terminal and products is sent to the terminal via a notification configuration.

[0550] For example, if a user is looking for a new smartphone, in addition to the entered price range and desired features, a list of products the user has shown interest in will be displayed. Furthermore, if the system senses interest or anxiety from the user's facial expressions, it will present product recommendations and explanations tailored to those emotions.

[0551] An example of a prompt for the generative AI model would be written in the format of, "The user's current emotional state is <emotion>. Please suggest five products that are best suited to this user." This allows for product suggestions that are tailored to the individual user's preferences and emotions.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The user uses a device to enter information about the product or service they wish to purchase. This information includes price, features, brand, etc. The entered information is collected by the device and prepared for the next processing step.

[0555] Step 2:

[0556] The device uses its built-in camera and microphone to record the user's voice and facial expressions. This data is used to reveal the user's emotional state. The voice data is converted to text using the Google Cloud Speech-to-Text API, and the facial expression data is analyzed using the Microsoft Azure Emotion API. This analyzes the user's emotional state and outputs it as numerical data.

[0557] Step 3:

[0558] The terminal sends the conditional information collected in step 1 and the emotion data analyzed in step 2 to the server. The server receives this data and begins preparing to extract relevant data sets from the information collection.

[0559] Step 4:

[0560] The server extracts appropriate product and information processing terminal data sets from the information set based on the received conditional information and sentiment data. Database search technology and an analysis engine are used for this process. The extracted data sets are prepared to best match the user's needs and sentiments.

[0561] Step 5:

[0562] The server evaluates the extracted data using a generative AI model to generate information on the most appropriate product. Here, machine learning algorithms such as TensorFlow and PyTorch calculate suitability based on the user's emotional state, and the optimal product is selected.

[0563] Step 6:

[0564] Information about the selected products is transmitted from the server to the terminal. The terminal receives this information and displays it to the user in a visually easy-to-understand format. This allows the user to understand how their criteria and preferences were taken into consideration when suggesting products.

[0565] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0566] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0567] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0568] [Fourth Embodiment]

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

[0570] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0571] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0572] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0573] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0574] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0575] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0576] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0577] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0578] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0579] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0580] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0581] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0582] This invention is implemented as a system with a series of functions to efficiently select a personal digital information terminal (PDI) device. First, the user inputs their requirements for purchasing a PDI device through a dedicated interface on the terminal. The input information is diverse, including, for example, budget, required functions, desired screen size, and camera performance.

[0583] The terminal sends the entered information to the server. The server searches the database based on the received information and extracts data for mobile information terminal devices that match the criteria. The database contains detailed information on various types of terminals, including information on the latest models.

[0584] The extracted data is further analyzed by a generation mechanism within the server, and the device that best meets the user's requirements is selected. Here, the server can use machine learning algorithms to make selections that take into account past user selection trends and market trends.

[0585] The server then sends a list of the most suitable personal digital assistant (PDAs) devices to the terminal. The terminal displays this list to the user, allowing them to review the suggested options in detail and make a purchase decision.

[0586] For example, if a user enters conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible," the system will select the most appropriate personal digital assistant (PDCA) device based on these conditions and notify the user. The user can then purchase the device that best suits their needs based on the presented options.

[0587] As described above, the present invention is implemented as a system that can flexibly respond to diverse inputs from users and provide the optimal portable information terminal device quickly and accurately.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] Users use their devices to input requirements such as budget, necessary features, desired screen size, and camera performance. Input is done through a dedicated interface, providing detailed information through selections and free-text descriptions for each item.

[0591] Step 2:

[0592] The terminal converts the information entered by the user into a data format and sends it to the server. A secure communication protocol is used for transmission to maintain the confidentiality of the information.

[0593] Step 3:

[0594] The server analyzes the received user data and searches the database based on each condition item. First, it narrows down the results based on basic conditions such as budget and OS, and then filters further based on required functions and special specifications.

[0595] Step 4:

[0596] The server uses a machine learning algorithm to select the mobile information terminal device that best matches user needs from the filtered list of candidates. Past user data and market trends are also taken into consideration to improve the accuracy of the selection.

[0597] Step 5:

[0598] The server generates information on the selected optimal mobile device and sends it to the user's device. The information sent includes the device name, main functions, price, and a link to purchase it.

[0599] Step 6:

[0600] The terminal displays information received from the server in a format that is easy for the user to understand. Based on the displayed information, the user can check the details of each terminal and make a purchase decision.

[0601] (Example 1)

[0602] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] In today's information society, a wide variety of electronic devices exist on the market, making it difficult for users to efficiently select products that meet their needs. In particular, when users have diverse requirements, gathering and analyzing information to make the optimal choice becomes complex, and there is a need for a system that enables rapid and accurate selection.

[0604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0605] In this invention, the server includes a receiving means for receiving multiple requirements from a user, a querying means for retrieving information on relevant electronic devices from an information set based on the received requirements, and a selection means for selecting the optimal electronic device using the retrieved information. This makes it possible to quickly and accurately analyze the diverse requirements of users and provide the most suitable device.

[0606] A "reception method" is a system that receives user requirements as input and records them as data that can be processed in digital format.

[0607] A "request method" is a tool that searches for appropriate information from an information set based on the received requirements and extracts relevant results.

[0608] A "selection mechanism" is a system that analyzes the retrieved information and processes data to determine which electronic device best meets the user's requirements.

[0609] "Presentation means" refers to technologies for conveying information from selected electronic devices to users through visual or auditory means.

[0610] A "learning model" is an algorithmic system that analyzes specific patterns and trends based on past data to make predictions or selections regarding newly appearing data.

[0611] An "information collection" is a database or data store in which diverse data related to electronic devices is gathered, structured, and stored.

[0612] This invention is implemented as a system for efficiently selecting electronic equipment. This system recommends the most suitable electronic equipment based on the requirements specified by the user. Specific embodiments are described below.

[0613] The user first launches a dedicated application using a mobile device. This application provides an interface for entering user requirements, allowing users to input information such as budget, necessary functions, desired screen size, and camera performance. Intuitive UI elements such as dropdown lists, sliders, and text boxes are provided for information input.

[0614] The terminal converts the information entered by the user into JSON format data and securely sends it to the server using the HTTPS protocol. The transmitted information is then parsed on the server via an intermediary.

[0615] The server uses query tools to search an information database based on the received requirements. This information database contains detailed information about electronic devices, including the latest models and their features.

[0616] Furthermore, the server uses generative AI models and learning models as selection tools. This allows it to analyze and score the most appropriate electronic devices based on past selection history and market trends. TensorFlow and other machine learning libraries are utilized for the analysis.

[0617] The server then encrypts the list of selected electronic devices and sends it to the terminal in JSON format.

[0618] The terminal uses a presentation method to display the selection results to the user. The results are visualized as a card view or list view, making it easy for the user to compare devices and make a purchase decision.

[0619] For example, users can specify their requirements using prompt statements such as "I'm looking for the latest smartphone with a high-resolution camera and a budget of under 50,000 yen" or "Tell me about the latest smartphone models with a large screen that fit my budget." In this way, the present invention assists users in quickly and accurately selecting the electronic device they want.

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

[0621] Step 1:

[0622] The user launches a dedicated application on their mobile device and enters their requirements using a requirements input interface. Specifically, they select and enter information such as budget, features, screen size, and camera performance. This information becomes the input data. The entered data is converted into JSON format within the application.

[0623] Step 2:

[0624] The terminal sends the converted JSON data to the server. Since the transmission method uses the HTTPS protocol, data security is maintained. The transmitted JSON data becomes the server's input data, and after reception, it is processed by the receiving mechanism for analysis.

[0625] Step 3:

[0626] Based on the received input data, the server initiates a query to the information set in order to perform a database search. It generates an SQL query and uses this query to extract information about electronic devices that match the user's requirements. The extracted information becomes intermediate output data within the server and is used for the next processing step.

[0627] Step 4:

[0628] The server further analyzes the extracted information using generative AI models and learning models. It applies a scoring algorithm that considers past user data and market trends to select the most suitable electronic device. The output here is a list of electronic devices best suited to the user's requirements.

[0629] Step 5:

[0630] The server repackages the selected electronic device list in JSON format, encrypts it, and sends it to the terminal. This list becomes the input data for the terminal and is visually displayed to the user via a presentation method.

[0631] Step 6:

[0632] The device displays the received selection list on the UI. Using card view or list view formats, users can easily compare and select items. The design format is particularly important here, requiring a user-friendly and easy-to-understand layout. This display is the device's final output, and users use it as a reference to make their purchase decision.

[0633] (Application Example 1)

[0634] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] When users purchase information equipment, they often face the challenge of quickly and easily selecting the product that best suits their needs from a wide range of options. There is a need for systems that efficiently assist users in making the optimal choice.

[0636] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0637] In this invention, the server includes communication means for receiving multiple condition inputs from a user, analysis means for extracting data on relevant information devices from a database based on the received conditions, and generation means for selecting the optimal information device using the extracted data. This makes it possible to propose information devices based on precise conditions.

[0638] A "user" is an individual or corporation looking for a product that meets their needs when purchasing information equipment.

[0639] "Condition input" refers to information that indicates the selection criteria specified by the user, such as budget, performance, and feature requirements.

[0640] "Communication means" refers to methods and technologies for transmitting user input conditions to a system.

[0641] "Information equipment" is a general term for electronic devices such as mobile phones and tablets.

[0642] "Analysis means" refers to a technology for extracting data from a database of relevant information devices based on received condition inputs.

[0643] A "database" is a collection of data that systematically stores detailed information about information devices.

[0644] "Generation means" refers to methods and technologies for identifying the optimal information device from the extracted information.

[0645] "Notification means" refers to methods and technologies for informing users of information about selected information devices.

[0646] A "generative artificial intelligence model" is an artificial intelligence technology used to automate product recommendations based on user criteria.

[0647] The system implementing this invention uses a communication device that accepts user input conditions, a server that analyzes information in a database, and a generative artificial intelligence model as a generation means. The user inputs their conditions on a smartphone application or web platform. For example, they might input conditions such as "I want a high-resolution camera," "My budget is under 50,000 yen," and "I'd prefer the latest model if possible."

[0648] The server searches the database based on user conditions received via the communication device and extracts data for relevant information devices. The database contains detailed information on a wide variety of information devices, and the server generates a list that best matches the user's conditions. This generation uses a machine learning algorithm that has learned past selection trends and market trends.

[0649] The generative artificial intelligence model, used as a generation tool, uses extracted data to list the most suitable information devices for the user. In this process, it generates text prompts to provide the necessary conditions for product recommendations. An example of a prompt would be: "The user is looking for a new smartphone with a high-resolution camera for under 50,000 yen. Please suggest the most appropriate options based on these conditions."

[0650] Users can then review the list of available information devices notified by the server on their devices and select and purchase a product that meets their needs. This enables accurate and rapid selection of information devices, significantly improving the user's purchasing experience.

[0651] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0652] Step 1:

[0653] Users enter their requirements using a smartphone application or web platform. These requirements include budget, necessary features, and specific usage purposes. The device then formats this input data and prepares it for transmission to the server.

[0654] Step 2:

[0655] The server parses the user's input criteria. In this parsing, an efficient search algorithm is used to match the user criteria with information in the database. This generates a list of information devices that match the criteria.

[0656] Step 3:

[0657] The server further analyzes the information device data extracted based on user criteria using a generative artificial intelligence model. In this process, machine learning algorithms take into account past selection trends and market trends to identify the product that best fits the selection criteria. Here, appropriate prompt statements generated by the generative AI model are utilized to improve recommendation accuracy.

[0658] Step 4:

[0659] The server generates a list of optimized information devices and notifies the user's terminal. This notification includes detailed information and ratings of recommended products. The terminal displays this information visually through its user interface, making it easy for the user to understand.

[0660] Step 5:

[0661] The user compares the options based on the provided information and decides which information device to purchase. The terminal feeds the user's selection back to the server, preparing it to be used as data to improve the accuracy of recommendations in the future.

[0662] 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.

[0663] This invention is implemented as a system that combines an emotion engine to make the selection process for personal information terminal devices more sophisticated. First, the user inputs their wishes and requirements for a personal information terminal device through the terminal. Here, the user sets specific requirements such as budget, desired functions, screen size, and camera performance. During this input, the emotion engine installed in the terminal analyzes the user's emotions from the input content, voice, and facial expressions.

[0664] The terminal sends collected conditional data and sentiment data to the server. The server receives this data and searches the database using an analysis tool. The analysis tool first extracts terminal information that matches the user's conditions, and then, taking sentiment data into consideration, generates customized results that reflect the user's expectations and preferences.

[0665] The server uses machine learning algorithms to evaluate candidate mobile devices and select the model that is likely to provide the highest user satisfaction. The emotion engine plays a crucial role in this selection process, enabling suggestions that reflect the user's latent needs and interests.

[0666] The server then sends the selected information to the terminal. The terminal presents the results to the user in an easy-to-understand format. Here, the user can understand how their emotions are reflected and why a particular terminal was recommended.

[0667] For example, if a user prioritizes camera functionality but feels stressed because they don't understand the latest technology, the emotion engine can recognize this and suggest the most suitable device based on the user's knowledge level, along with a simple and easy-to-understand explanation to alleviate the stress.

[0668] Thus, the present invention is implemented as a system that enables the selection of more personalized mobile information terminal devices through the use of an emotion engine, thereby improving user satisfaction.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The user inputs specific requirements for a mobile information terminal (PDCA) device via the terminal. These include budget, necessary functions, screen size, and camera performance. The system also captures the user's emotions through facial expressions and voice.

[0672] Step 2:

[0673] The terminal converts the input condition data and captured emotion data into a data format and sends it to the server using a secure communication method.

[0674] Step 3:

[0675] The server analyzes the received data, searches the database, and extracts candidate mobile information terminal devices that match the user's criteria.

[0676] Step 4:

[0677] The server's emotion engine analyzes user emotion data and evaluates stress levels, expectations, preferences, and other factors.

[0678] Step 5:

[0679] The server uses the results of sentiment analysis to select the mobile information terminal device that best matches the user's needs from among the candidates using a machine learning algorithm. During this process, it also provides customized suggestions that take into account the user's emotional state.

[0680] Step 6:

[0681] The server sends detailed information about the selected mobile information terminal device to the terminal.

[0682] Step 7:

[0683] The terminal visually displays the received information to the user. Based on the presented information, the user can select the optimal mobile information terminal device while reviewing the reasons for the suggestions and the results of sentiment analysis.

[0684] (Example 2)

[0685] 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".

[0686] When users select information processing equipment, they want to consider subjective feelings in addition to demand, budget, and functionality. However, conventional systems do not reflect these emotional elements, resulting in insufficient improvement of the user experience.

[0687] 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.

[0688] In this invention, the server includes communication means for receiving multiple conditional and emotional data from the user, analysis means for extracting data from a data aggregation unit for relevant information processing devices based on the received conditional and emotional data, and generation means for selecting the optimal information processing device using the extracted data. This makes it possible to select a more personalized information processing device that reflects the user's emotions.

[0689] "Multiple user requirements" refer to the specifications and constraints that users desire regarding the selection of an information processing device, specifically including requirements such as budget, functionality, screen size, and camera performance.

[0690] "Emotional data" refers to information about a user's subjective psychological state, analyzed from their facial expressions and tone of voice, and reflects their unconscious expectations and preferences.

[0691] A "communication method" is a system equipped with interfaces and protocols for sending user conditions and emotional data to a server, and is a means for smoothly receiving and transmitting data.

[0692] "Analysis means" refers to a means for performing a process of searching a data storage unit based on conditional and emotional data received from the user and narrowing down the candidates for related information processing devices.

[0693] The "Data Accumulation Unit" is a database that stores various types of information related to the information processing device, and this system is used as a search target during the selection process.

[0694] "Generation means" refers to the means of executing processes and algorithms used to select the optimal information processing device from the candidates narrowed down by the analysis means, and in particular, the selection is carried out by applying machine learning algorithms.

[0695] A "notification means" is a system equipped with an interface or protocol for conveying the results of a selected information processing device to the user, and is a means of presenting the results in a way that is easy for the user to understand.

[0696] This invention is specifically implemented as a system that combines multiple conditions and sentiment data to highly personalize the selection process for information processing equipment.

[0697] The user first uses a device to input their requests and requirements. These requests are specific functional preferences, such as screen size and camera performance. During input, the device has a function to capture the user's voice and facial expressions, thereby acquiring the user's emotional data. The device is equipped with an advanced algorithm called an emotion engine, which is used to analyze the emotional data in real time. This analysis allows us to understand how emotions, in addition to the user's requirements, influence their selection.

[0698] The terminal sends collected conditional and sentiment data to the server. The server receives this data and extracts relevant information from its internal data aggregation unit. Machine learning algorithms are used at this stage, and the server analyzes the data using multiple methods such as random forests and neural networks. The analysis results are generated as a list of optimal information processing devices. This list is constructed considering not only functional requirements but also emotional aspects.

[0699] As a result, the server sends the selected model to the terminal. The terminal displays details of the proposed device to the user through the user interface. At this point, the user can understand the reasons for the selection and gain satisfaction with their choice. For example, if the user is in a state where "camera performance is important, but I feel uneasy because I don't understand the technical details," the system will recommend an easy-to-understand explanation and an appropriate information processing device to alleviate that anxiety.

[0700] An example of a prompt message might be: "The user values ​​camera performance but is apprehensive about advanced technical specifications. Please provide a concise and persuasive proposal and outline the next selection steps."

[0701] This allows users to confidently choose the information processing device that best suits their needs, leading to improved accuracy in product selection and increased customer satisfaction.

[0702] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0703] Step 1:

[0704] The user inputs their requests and requirements for the information processing device into the terminal. This input includes specific items such as screen size, camera performance, and budget. Furthermore, the user can express emotions through voice input or facial expressions. At this stage, the terminal treats this information as an initial dataset and records it for subsequent processing.

[0705] Step 2:

[0706] The device uses a built-in emotion engine to analyze the user's voice tone and facial expression data in real time. The resulting emotion data is combined with the user's condition data into a single composite dataset. Here, the degree of stress and excitement is quantified from the voice and added to the text-based request data.

[0707] Step 3:

[0708] The terminal securely transmits the prepared composite dataset to the server. SSL communication and other methods are used for transmission to protect data confidentiality. The input requests and analyzed sentiment data are integrated and arrive at the server, becoming the base data for the next stage of processing.

[0709] Step 4:

[0710] The server processes the received data using an analysis tool and extracts relevant information processing device data from the data aggregation unit. Specifically, it uses a machine learning algorithm to generate a list of devices that match the user's criteria. Here, the analysis tool uses SQL queries and other methods to quickly identify candidates from a large amount of data.

[0711] Step 5:

[0712] The server applies machine learning algorithms (e.g., random forest, neural network) to evaluate candidate devices, taking into account user sentiment data. This evaluation identifies the device predicted to provide the greatest user satisfaction. The generated recommendation results take all conditions and sentiment factors into consideration.

[0713] Step 6:

[0714] The server returns the results of the selection of the optimal information processing device to the terminal. The terminal presents the user with the features and reasons for the selected device through an intuitive and easy-to-understand interface. For example, it summarizes the image, advantages, and sentiment analysis results of the recommended device and displays them in a way that is easy for the user to understand. Based on these results, the user can decide whether to purchase or conduct further research.

[0715] (Application Example 2)

[0716] 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".

[0717] In today's online shopping and information device selection environment, users are often overwhelmed by a vast number of choices, and finding products that match their emotions and preferences is particularly difficult. Therefore, there is a need for a system that allows users to select devices and products that best suit their needs without stress. Personalized suggestions based on emotions could significantly improve user satisfaction.

[0718] 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.

[0719] In this invention, the server includes a communication configuration means for receiving multiple conditional information from a user, an analysis configuration means for extracting a group of data from an information set relating to an information processing terminal based on the received conditional information, a generation configuration means for selecting the optimal information processing terminal using the extracted data group, and an emotion analysis configuration means for recognizing the user's emotional state and making product suggestions based on that emotion. This makes it possible to suggest terminals and products optimized for the user's emotions and individual conditions.

[0720] "Users" refer to individuals or groups who use a system or device, and who influence the system's operation through their actions and requests.

[0721] "Multiple conditional information" refers to the preferences and requirements that users set when selecting a device or product, and includes information such as pricing, feature selection, and intended use.

[0722] "Communication configuration" refers to the mechanism by which a system receives input from users, and is a means of transmitting data through a network or interface.

[0723] An "information processing terminal" is a device that processes digital data and provides information to users, and includes smartphones, tablets, and computers.

[0724] A "data set" is a collection of data related to an information processing terminal, including information such as specifications, performance, price, and evaluation.

[0725] An "information collection" refers to a large-scale information resource such as a database or cloud storage where various types of data are accumulated.

[0726] An "analysis configuration" is a system component used to analyze data received from users and extract information that meets the required criteria.

[0727] A "generative configuration" is a system component that has the function of selecting the most suitable information processing terminal based on the analyzed data set.

[0728] A "notification structure" is a mechanism for conveying selected information to users, and it is a means of presenting information through visual and auditory means.

[0729] "Emotional state" refers to the mental and sensory condition of the user, and includes the aspects of their emotions inferred from their facial expressions, voice, etc.

[0730] "Emotional analysis configuration" refers to the components of a system that recognizes the emotional state of users and reflects the results in product recommendations.

[0731] This invention is implemented as an online shopping and information terminal selection system incorporating emotion analysis. This system consists of an emotion engine, a server capable of communicating with it, a terminal that accepts user input, and a database.

[0732] When a user uses a device to input selection criteria for the device and products, the device sends these criteria to the server. During this process, the device uses its camera and microphone to record the user's facial expressions and voice, and analyzes their emotional state through an emotion engine. Software such as the Google Cloud Speech-to-Text API or the Microsoft Azure Emotion API may be used for this analysis.

[0733] The server selects the optimal information processing terminal from the information set based on the received conditional information and analyzed sentiment data. The selection process uses a generative engine employing learning algorithms such as TensorFlow or PyTorch. Data on the selected terminal and products is sent to the terminal via a notification configuration.

[0734] For example, if a user is looking for a new smartphone, in addition to the entered price range and desired features, a list of products the user has shown interest in will be displayed. Furthermore, if the system senses interest or anxiety from the user's facial expressions, it will present product recommendations and explanations tailored to those emotions.

[0735] An example of a prompt for the generative AI model would be written in the format of, "The user's current emotional state is <emotion>. Please suggest five products that are best suited to this user." This allows for product suggestions that are tailored to the individual user's preferences and emotions.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The user uses a device to enter information about the product or service they wish to purchase. This information includes price, features, brand, etc. The entered information is collected by the device and prepared for the next processing step.

[0739] Step 2:

[0740] The device uses its built-in camera and microphone to record the user's voice and facial expressions. This data is used to reveal the user's emotional state. The voice data is converted to text using the Google Cloud Speech-to-Text API, and the facial expression data is analyzed using the Microsoft Azure Emotion API. This analyzes the user's emotional state and outputs it as numerical data.

[0741] Step 3:

[0742] The terminal sends the conditional information collected in step 1 and the emotion data analyzed in step 2 to the server. The server receives this data and begins preparing to extract relevant data sets from the information collection.

[0743] Step 4:

[0744] The server extracts appropriate product and information processing terminal data sets from the information set based on the received conditional information and sentiment data. Database search technology and an analysis engine are used for this process. The extracted data sets are prepared to best match the user's needs and sentiments.

[0745] Step 5:

[0746] The server evaluates the extracted data using a generative AI model to generate information on the most appropriate product. Here, machine learning algorithms such as TensorFlow and PyTorch calculate suitability based on the user's emotional state, and the optimal product is selected.

[0747] Step 6:

[0748] Information about the selected products is transmitted from the server to the terminal. The terminal receives this information and displays it to the user in a visually easy-to-understand format. This allows the user to understand how their criteria and preferences were taken into consideration when suggesting products.

[0749] 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.

[0750] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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."

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] The following is further disclosed regarding the embodiments described above.

[0771] (Claim 1)

[0772] A communication method that accepts multiple condition inputs from the user,

[0773] An analysis means for extracting data from a database related to a mobile information terminal device based on the received conditions,

[0774] A generation means for selecting the optimal mobile information terminal device using the extracted data,

[0775] A notification means for notifying the user of information about the selected mobile information terminal device,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, wherein the generation means selects a mobile information terminal device using a machine learning algorithm.

[0779] (Claim 3)

[0780] The system according to claim 1, which recommends a portable information terminal device taking into account the budget, functions, and purpose of use specified by the user.

[0781] "Example 1"

[0782] (Claim 1)

[0783] A means of receiving multiple requirements from users,

[0784] A query means for retrieving information on relevant electronic devices from an information set based on the received requirements,

[0785] A selection method for choosing the optimal electronic device using the retrieved information,

[0786] A presentation means for conveying information about selected electronic devices to the user,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, wherein the selection means selects an electronic device using a learning model.

[0790] (Claim 3)

[0791] The system according to claim 1, which recommends electronic devices taking into account the budget, capabilities, and purpose of use specified by the user.

[0792] "Application Example 1"

[0793] (Claim 1)

[0794] A communication method that accepts multiple condition inputs from the user,

[0795] An analysis means for extracting data from a database of relevant information devices based on the received conditions,

[0796] A generation means for selecting the optimal information equipment using the extracted data,

[0797] A notification means for informing the user of information about the selected information equipment,

[0798] A means of using a generative artificial intelligence model for generating recommendations for a variety of information devices,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the generation means selects information equipment using a machine learning algorithm.

[0802] (Claim 3)

[0803] The system according to claim 1, which recommends information equipment taking into account the budget, functions, and purpose of use specified by the user.

[0804] "Example 2 of combining an emotion engine"

[0805] (Claim 1)

[0806] A communication method that accepts multiple conditions and sentiment data from the user,

[0807] An analysis means for extracting data from a data collection unit of a relevant information processing device based on received condition and emotion data,

[0808] A generation means for selecting the optimal information processing device using the extracted data,

[0809] A notification means for notifying the user of information about the selected information processing device,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, wherein the generation means selects an information processing device using a machine learning algorithm.

[0813] (Claim 3)

[0814] The system according to claim 1, which recommends an information processing device based on emotional data, taking into account the budget, functions, and purpose of use specified by the user.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] A communication configuration means that accepts multiple condition information from the user,

[0818] An analysis configuration means for extracting data sets from an information set that relate to the information processing terminal based on the received condition information,

[0819] A generation configuration means for selecting the optimal information processing terminal using the extracted data set,

[0820] A notification configuration means for transmitting information about the selected information processing terminal to the user,

[0821] A means for constructing an emotion analysis that recognizes the emotional state of the user and makes product suggestions based on those emotions,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the generation configuration selects an information processing terminal using a learning algorithm.

[0825] (Claim 3)

[0826] The system according to claim 1, which takes into account the cost, functions, and purpose of use specified by the user, and further analyzes the user's emotional state to recommend the most suitable information processing terminal. [Explanation of Symbols]

[0827] 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 communication method that accepts multiple condition inputs from the user, An analysis means for extracting data from a database related to a mobile information terminal device based on the received conditions, A generation means for selecting the optimal mobile information terminal device using the extracted data, A notification means for notifying the user of information about the selected mobile information terminal device, A system that includes this.

2. The system according to claim 1, wherein the generation means selects a mobile information terminal device using a machine learning algorithm.

3. The system according to claim 1, which recommends a portable information terminal device taking into account the budget, functions, and purpose of use specified by the user.

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