System

Abstract

A system is provided.SOLUTION: A system comprising: input means for receiving a request from a user; analysis means for analyzing the request; matching means for identifying a related product or service based on a result of the analysis; and providing means for providing the identified product or service to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Many consumers and employees are unable to keep track of all the products and services offered by large companies. This makes it difficult for them to find the right products and services, and it also makes it difficult for companies to provide the right services. Therefore, there is a need for a method that allows users to quickly and accurately find the service that best suits their needs. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: a system including an input means for receiving a request from a user, an analysis means for analyzing the request, a matching means for identifying related products and services based on the analysis results, and a provision means for providing the identified products and services to the user. This system receives the user's request using an input means including text input and voice input, analyzes the request using a generative AI model, and identifies related products and services. These identified products and services are then provided to the user, allowing the user to quickly find the most suitable service.

[0006] "Input means" refers to the interface for receiving requests from the user, including text input and voice input.

[0007] "Analysis means" refers to systems and algorithms for analyzing requests received from users, such as generative AI models.

[0008] "Matching means" refers to a process for identifying related products and services based on the results of analysis by the analysis means.

[0009] "Delivery means" refers to the mechanism by which the identified goods or services are presented to the user, including the user interface and display system.

[0010] A "generative AI model" refers to a model that uses artificial intelligence to analyze data and make decisions based on the results.

[0011] "Products and services" refers to the general term for products, services, plans, etc. offered by a company. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

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

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

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

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

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0029] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

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

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0033] The present invention is a system that enables a user to easily find products and services that best meet his or her needs. The following describes in detail the embodiments of the present invention.

[0034] This system mainly consists of a client terminal, a server, and a user. The client terminal inputs the user's requests and sends them to the server. The server analyzes the received requests, identifies relevant products and services, and recommends them.

[0035] User request input

[0036] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[0037] Sending data

[0038] The client device sends the data entered by the user to the server using an HTTP request or similar.

[0039] Receiving and analyzing data

[0040] The server receives the user's request data sent from the client device. After receiving it, the server analyzes the received data using a generative AI model. Through this analysis, the request is recognized as a "new mobile plan."

[0041] Matching products and services

[0042] The server then identifies relevant products and services based on the analysis, searching its database to find multiple plans and services that best fit the user's needs.

[0043] Providing recommendation results

[0044] The server then formats the identified products and services into an appropriate format and sends it to the client device. This format is designed to be easy for the user to understand. For example, it is displayed as a "great value data plan" or "family discount plan."

[0045] Displaying recommendation results

[0046] Finally, the client device displays the recommendation results received from the server to the user, who can then select the products and services that best suit their needs.

[0047] Specific examples

[0048] As a concrete example, consider the case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. The entered data is sent to the server and analyzed by the generative AI model. Based on the analysis results, "value data plans" and "family discount plans" are identified and sent from the server to the client device. The client device displays these results to the user, allowing the user to select an appropriate plan.

[0049] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] User request input

[0053] The user enters their request using a text box or voice input field on the client device. For example, the user enters "I'm looking for a new mobile plan."

[0054] Step 2:

[0055] Sending data

[0056] The client device sends the data entered by the user to the server using a communication method such as an HTTP request, thereby passing the user's request to the server.

[0057] Step 3:

[0058] Receiving data

[0059] The server receives the user's requested data sent from the client terminal, and passes the received data to an internal processing engine.

[0060] Step 4:

[0061] Data analysis

[0062] The server passes the received request data to the generative AI model for analysis. The generative AI model classifies the user's request into appropriate categories and extracts specific needs. For example, from the request for "a new mobile plan," the "mobile plan" category is recognized.

[0063] Step 5:

[0064] Identifying related products and services

[0065] The server then searches the database for relevant products and services based on the analyzed data. This search identifies multiple options based on the user's needs, such as "great data plans" or "family discount plans."

[0066] Step 6:

[0067] Recommendation result format

[0068] The server formats the results in a user-friendly format to provide the identified products and services to the user, for example, a list containing multiple plan names and brief descriptions.

[0069] Step 7:

[0070] Sending recommendations

[0071] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[0072] Step 8:

[0073] Displaying the results

[0074] The client device displays the recommendation results received from the server to the user, including details such as "value data plans" and "family discount plans."

[0075] This series of steps allows users to easily find the products and services that best suit their needs.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] In today's market, users often have difficulty finding the products and services that best suit their needs. In particular, users often become confused due to the lack of an efficient way to find the best product among the many options. Therefore, there is a need for a system that allows users to easily and quickly find products and services that meet their needs.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes input means for receiving requests from users, transmission means for transmitting the requests to the server, analysis means for analyzing the requests, matching means for identifying related products and services based on the analysis results, provision means for providing the identified products and services to the user, and display means for displaying the provided products and services on the user terminal, thereby enabling users to quickly and easily find products and services that best suit their requests.

[0081] "Input means" refers to an interface or device for receiving requests from a user.

[0082] "Transmission means" refers to the protocol or function for transmitting the received request to the server.

[0083] "Analysis means" refers to the technology or model used to analyze the request data received on the server side and understand the content of the request.

[0084] "Matching means" refers to algorithms and database search functions that identify related products and services based on the analysis results.

[0085] "Means of provision" refers to the method or mechanism for providing information about specified products or services to users.

[0086] "Display means" refers to the technology or interface used to display information about the products or services provided on the user's device.

[0087] "Generative AI models" refer to artificial intelligence techniques used to analyze user requests, often utilizing natural language processing models.

[0088] The present invention relates to a system that allows users to easily find products and services that best suit their needs. This system is mainly composed of a client terminal, a server, and a user.

[0089] Hardware and software used

[0090] Client devices are personal computers (PCs) or smartphones. The client devices function as an input means for inputting user requests. The server is the central hardware for receiving, analyzing, processing, and providing data, and runs various software components. A generative AI model (e.g., GPT-3) is used for analysis. A database (e.g., an SQL database or NoSQL database) is used to store information about products and services.

[0091] System Operation

[0092] First, the user enters their request through a text box or voice input field on the client device. For example, the user may enter "I'm looking for a new mobile plan."

[0093] The client device then sends this request data to the server using an HTTP POST request or similar, and the data is transferred to the server in JSON format or similar.

[0094] The server analyzes the received data using an analytical means. This analysis is performed using a generative AI model. The generative AI model uses natural language processing technology to understand the received data and recognize the request, for example, that the user is looking for a "new mobile plan."

[0095] Once the analysis is complete, the server uses matching methods to search the database to identify relevant products and services, such as "great data plans" or "family discount plans."

[0096] The server then formats the identified products and services into JSON format or similar and provides them to the client terminal, which receives this data, converts it into a visually easy-to-understand display format, and presents it to the user.

[0097] Specific examples

[0098] For example, consider a user looking for a new mobile plan using this system. When the user types "I'm looking for a new mobile plan" into the client device, the data is sent to the server in JSON format. The server analyzes this data using a generative AI model and recognizes the request for a "new mobile plan." It then searches for relevant plans, identifying options such as "value data plans" and "family discount plans," and sends them in JSON format to the client device. Finally, the client device displays this information to the user, who can choose the option that best suits them.

[0099] Prompt Sentence Examples

[0100] For example, the following prompts are given to a generative AI model:

[0101] "I'm looking for a new mobile plan. Which plan is best for me?"

[0102] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1:

[0105] The user uses the client terminal to input his / her request. The input is made through a text box or a voice input field. For example, the user may input "I'm looking for a new mobile plan." The input data is in text format.

[0106] Step 2:

[0107] The client terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is in text format, but is converted into JSON format when sent to the server.

[0108] Step 3:

[0109] The server receives JSON data sent from the client device. The received data first goes through a data validation process to ensure it is in the correct format. Here, the input is JSON data, and the output is the validated data.

[0110] Step 4:

[0111] The server uses a generative AI model to analyze the received data. The analysis automatically understands the user's request and recognizes, for example, that they are looking for a "new mobile plan." The input is the verified data, and the output is the analysis result of the request.

[0112] Step 5:

[0113] The server searches the database based on the analysis results to identify related products and services. In this case, products and services such as "value data plans" and "family discount plans" are extracted from the database. The input is the analysis results of the request, and the output is a list of identified products and services.

[0114] Step 6:

[0115] The server formats the information on the identified products and services in JSON format and provides it to the client terminal. The input is a list of products and services, and the output is JSON format data.

[0116] Step 7:

[0117] The client device parses the JSON data received from the server and displays it visually to the user. For example, it may be presented as part of a web page or application, containing detailed product or service information and links. The input is the JSON data sent from the server, and the output is a list of products or services that the user can view on the screen.

[0118] In this way, each processing step realizes a series of steps from receiving a user request to identifying related products and services and providing them to the user.

[0119] (Application example 1)

[0120] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] In today's information-saturated society, it is difficult for users to easily find the products and services that best meet their needs. As a result, users have to spend a lot of time and effort trying to find the right product or service from the vast number of options. Furthermore, existing systems require a complicated process from when users enter their needs to when the optimal product or service is recommended, which can result in a poor user experience.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0123] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying the identified products and services on the smartphone, thereby enabling the user to easily and quickly find products and services that best match their requests.

[0124] "Input means" refers to a means provided for receiving requests from a user, and includes text input and voice input.

[0125] "Analysis means" means a means used to analyze requests received from users, and analyzes requests using a generative AI model.

[0126] The "matching means" is a means used to identify related products and services based on the analysis results obtained by the analysis means.

[0127] "Means of provision" refers to the means established to provide specified products or services to users.

[0128] The "display means" is a means for providing a visual interface for displaying identified products and services on a smartphone.

[0129] A "database" is a storage device for storing information on multiple products, and is used to search for and list products and services.

[0130] A "generative AI model" is an artificial intelligence model used to analyze a user's natural language requests, for example, using generative AI.

[0131] overview

[0132] This invention is a system that allows users to easily find products and services that best suit their needs. The system includes an input means for receiving requests from users, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying them on a smartphone.

[0133] System Configuration

[0134] Input Method

[0135] Users can provide their requests to the system through text input or voice input, which is made possible by using a smartphone application.

[0136] Analysis means

[0137] The server analyzes the requests received from users using a generative AI model, such as an advanced natural language processing model like OpenAI's GPT-3.

[0138] Matching Method

[0139] The server searches a database based on the analysis results obtained by the analysis means to identify related products and services. This database stores, for example, a number of communication plans and product information.

[0140] Means of provision and display

[0141] The identified products and services are sent from the server to the user's smartphone and displayed visually, allowing the user to select the most appropriate product or service.

[0142] Specific processing flow

[0143] User request input

[0144] The user opens the application on their smartphone and types "I'm looking for a new laptop" into the input field. Voice input is also possible.

[0145] Data transmission and analysis

[0146] The smartphone sends the input data to the server using an HTTP request, and the server uses a generative AI model to analyze the data and understand the user's request.

[0147] Product identification and recommendations

[0148] The server then searches the database based on the analysis results to identify relevant products, such as "high-performance laptops" or "laptops at great prices."

[0149] Displaying recommendation results

[0150] The server sends the compressed results back to the smartphone, which displays the results to the user using the smartphone's display means.

[0151] Hardware and software used

[0152] Hardware: Smartphone (e.g. iPhone, Android)

[0153] Software: Python, requests library, JSON, generative AI model (e.g., OpenAI GPT-3)

[0154] Examples and prompts

[0155] Specific examples

[0156] When a user types, "I'm looking for a new laptop," the system recommends the most suitable laptop, using a generative AI model to analyze the natural language request, identify relevant products from a database, and display them on the smartphone.

[0157] Prompt Sentence Examples

[0158] A user types, "I'm looking for a new laptop."

[0159] Please recommend the most suitable product from the options below:

[0160] 1. High-performance laptop

[0161] 2. Great value laptops

[0162] 3. Lightweight model

[0163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0164] Step 1:

[0165] The user opens the application on their smartphone and enters their request into an input field, for example, "I'm looking for a new laptop," either by typing it in text or by using voice input to communicate their request to the system. The data obtained from this input field becomes the initial data.

[0166] Input: User's requested text or voice data

[0167] Output: User request data on the client terminal

[0168] Specific behavior: A smartphone application provides a text box or voice input interface to allow users to input requests in natural language.

[0169] Step 2:

[0170] The client terminal sends the data entered by the user to the server as an HTTP request, formatted in JSON.

[0171] Input: User request data on the client device

[0172] Output: JSON data sent in the HTTP request

[0173] What happens: The smartphone application uses the requests library to send an HTTP POST request to the server, which includes the user's input data.

[0174] Step 3:

[0175] The server receives the user's request data sent from the client device, then inputs the data into the generative AI model and analyzes the request.

[0176] Input: JSON data sent from the client terminal

[0177] Output: Parsed request data

[0178] How it works: The server uses a generative AI model, such as OpenAI's GPT-3, to parse the natural language request it receives. For example, the request might be "I'm looking for a new laptop."

[0179] Step 4:

[0180] Based on the analysis results, the server searches the database to identify related products and services. Multiple matching candidates are found and information about each is retrieved.

[0181] Input: Parsed request data

[0182] Output: Multiple identified product information

[0183] Specific operation: The server searches a database (e.g., MySQL or SQLite) based on the analysis results and identifies related products such as "high-performance laptops" and "laptops at great prices."

[0184] Step 5:

[0185] The server generates a list of recommendations for the identified products and services to offer to the user and sends it back to the client device in JSON format.

[0186] Input: Multiple identified product information

[0187] Output: JSON data of the recommendation list

[0188] Specific operation: The server formats the data of the identified products, generates a recommendation list, converts it into JSON format data, and sends it to the client device.

[0189] Step 6:

[0190] The client terminal visually displays the received recommendation list to the user, who can then select the product that best meets their needs based on the displayed information.

[0191] Input: JSON data of the recommendation list received from the server

[0192] Output: Product list displayed on the client device

[0193] Specific operation: The smartphone application parses the received JSON data and displays a list of recommended products in the user interface. The user can then select from the displayed list of products.

[0194] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0195] The present invention is a system for analyzing a user's needs and emotions and recommending optimal products and services. Hereinafter, an embodiment of the present invention will be specifically described.

[0196] This system mainly consists of a client device, a server, a user, and an emotion engine. The client device inputs the user's requests and sends them to the server. The server analyzes the received requests and emotions, and identifies and recommends related products and services. The emotion engine recognizes the user's emotions and uses this emotional information for analysis.

[0197] User request input

[0198] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[0199] Sending data

[0200] The client device sends the data entered by the user to the server using an HTTP request. At this stage, the emotion engine also simultaneously analyzes the user's emotions and sends that information to the server.

[0201] Receiving and analyzing data

[0202] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server uses a generative AI model and emotion engine to analyze the request and emotion. This analysis identifies the content of the request and the user's emotion. For example, the request for a "new mobile plan" and the emotion of "excitement" or "anxiety" are analyzed.

[0203] Matching products and services

[0204] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account the user's emotional data. For example, the recommended products and services may differ depending on whether the user is "excited" or "anxious."

[0205] Recommendation result format

[0206] The server formats the results to present the identified products and services to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, a "great data plan" might be especially highlighted to a user in an "excited" state.

[0207] Sending recommendations

[0208] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[0209] Displaying recommendation results

[0210] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[0211] Specific examples

[0212] Consider a case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. At the same time, the emotion engine recognizes the user's "excitement" state. The entered data and emotion data are sent to the server and analyzed by the generative AI model and emotion engine. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted in a format appropriate for the user's excitement state. Finally, the recommendation results are displayed on the client device, allowing the user to choose the plan that best suits them.

[0213] In this way, the system of the present invention makes it possible to quickly provide optimal products and services taking into consideration the user's needs and emotions.

[0214] The processing flow will be explained below.

[0215] Step 1:

[0216] User request input

[0217] The user enters their request using a text box or voice input field on the client device, for example, "I'm looking for a new mobile plan."

[0218] Step 2:

[0219] Sending data

[0220] The client device sends the request data entered by the user to the server using a communication method such as an HTTP request. The emotion engine also analyzes the user's emotions, and this emotion data is also sent to the server at the same time.

[0221] Step 3:

[0222] Receiving data

[0223] The server receives the user's request data and emotion data sent from the client's terminal, and passes the received data to an internal processing engine.

[0224] Step 4:

[0225] Data analysis

[0226] The server passes the received request data to the generative AI model for analysis. At the same time, the emotion engine analyzes the received emotion data. The generative AI model classifies the user's request into an appropriate category, and the emotion engine recognizes the user's emotional state (e.g., "excited" or "anxious").

[0227] Step 5:

[0228] Reflecting emotional data

[0229] The server integrates the analyzed request data with the emotional data and adjusts the recommended products and services based on the user's emotional state. For example, if the user is feeling anxious, it will recommend services that will make them feel more secure.

[0230] Step 6:

[0231] Identifying related products and services

[0232] The server then searches the database for relevant products and services based on the combined data, identifying the most suitable options based on the analysis results.

[0233] Step 7:

[0234] Recommendation result format

[0235] The server formats the results in an easy-to-view format to provide the identified products and services to the user, including product details that are sensitive to the user's emotional state.

[0236] Step 8:

[0237] Sending recommendations

[0238] The server sends the formatted recommendation results to the client's device, which prepares them for display to the user.

[0239] Step 9:

[0240] Displaying the results

[0241] The client device receives the recommendation results from the server and displays them to the user, including details that correspond to the user's emotional state, such as "value data plans" and "family discount plans."

[0242] This series of steps makes it easy for users to find the products and services that best suit their needs and emotions.

[0243] Example 2

[0244] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0245] Conventional recommendation systems simply analyze users' requests and provide relevant products and services. However, because these systems do not take into account the user's emotional state, they often fail to recommend products and services that are truly optimal for the user. Furthermore, if the input request is interpreted subtly differently depending on the user's emotions, analysis results that do not take this into account will not meet the user's expectations. For this reason, there is a need for a system that can analyze requests and recommend products and services based on the user's emotional state.

[0246] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for receiving a request from a user, an analysis means for analyzing the request, the analysis means including an emotion analysis means for analyzing the user's emotion, a matching means for identifying related products and services based on the analysis results, and a provision means for providing the identified products and services to the user. This makes it possible to analyze the request taking into consideration the user's emotional state and to recommend optimal products and services based on the results.

[0247] "Input means" refers to a method or device for receiving requests from a user, including text input and voice input.

[0248] The "analysis means" is a method or device for analyzing requests received from users.

[0249] "Emotion analysis means" refers to a method or device for analyzing the emotional state of a user when analyzing the user's needs.

[0250] The "matching means" is a method or device for identifying related products or services based on the results obtained by the analysis means.

[0251] "Provision means" refers to a method or device for providing a specified product or service to a user.

[0252] "Generative AI model" refers to an artificial intelligence model used to analyze user requests.

[0253] A "prompt" is a sentence to be input into a generative AI model, and is a document that contains a specific request or emotion.

[0254] This invention is a system that analyzes user needs and emotions and recommends optimal products and services. This system is mainly composed of a client terminal, a server, users, and an emotion analysis engine.

[0255] User request input

[0256] A user uses a client device to input their request into the system. This input is done through an interface such as a text box or voice input field. For example, a user might input, "I'm looking for a new mobile plan."

[0257] Sending data

[0258] The client device sends the data entered by the user to the server using an HTTP request or similar. At the same time, the emotion analysis engine also analyzes the user's emotional state and sends that information to the server. At this stage, the user's request and emotional information are combined into a single data packet.

[0259] Receiving and analyzing data

[0260] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server analyzes the data using the generative AI model and emotion analysis engine. Specifically, it generates a prompt sentence to input the data into the generative AI model. For example, if the request is "Looking for a new mobile plan" and the emotion is "Excited," the server inputs the following prompt sentence into the generative AI model:

[0261] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0262] A generative AI model analyzes this prompt and generates a list of related products and services.

[0263] Matching products and services

[0264] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account emotional data. For example, different products and services may be recommended if the user is "excited" or "anxious."

[0265] Recommendation result format

[0266] The server then formats the identified products and services to present to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, "great data plans" might be especially emphasized for an excited user.

[0267] Sending recommendations

[0268] The server sends the formatted recommendation results to the client device as an HTTP response, allowing the client device to display the recommended products and services to the user.

[0269] Displaying recommendation results

[0270] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[0271] Specific examples

[0272] Consider a case where a user looking for a new mobile plan uses this system. The user types "I'm looking for a new mobile plan" into the client device, and the emotion analysis engine recognizes the "excited" state. This data is sent to the server, which prompts the generative AI model with the following sentence:

[0273] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0274] The input is analyzed. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted to suit the user's excitement level. Finally, the recommendation results are displayed on the client's device, allowing the user to choose the plan that best suits them.

[0275] In this way, the system of the present invention can quickly provide optimal products and services taking into account the user's needs and emotional state.

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1:

[0278] The user enters a request

[0279] The user uses the client device to enter their request into a text box or voice input field, for example, by typing "I'm looking for a new mobile plan." Once this input is complete, the device temporarily stores the data.

[0280] Step 2:

[0281] The device sends the data to the server

[0282] The client device sends input data to the server in the form of an HTTP request. At the same time, the emotion analysis engine analyzes the user's emotional state and sends that information in the same request to the server. For example, the user's request and emotional information such as "excitement" are packaged in a data packet.

[0283] Step 3:

[0284] The server receives the data

[0285] The server receives the HTTP request sent from the client device. The received data includes the user's desired text and emotional state information. The server converts this data into the format required for analysis.

[0286] Step 4:

[0287] The server generates a prompt for parsing.

[0288] The server converts the received data into prompts to be fed into the generative AI model. For example, based on the request "I'm looking for a new mobile plan" and the emotional state "excited," it generates the following prompt:

[0289] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0290] Step 5:

[0291] The server analyzes the data using a generative AI model

[0292] The server inputs the generated prompt into a generative AI model, which analyzes it based on the customer's request and sentiment. The generative AI model processes the prompt and outputs a list of related products and services.

[0293] Step 6:

[0294] The server uses sentiment analysis techniques to adjust the results.

[0295] The server uses sentiment analysis to tailor the results from the generative AI model to the user's emotional state, highlighting, for example, special offers and discounts to excited users.

[0296] Step 7:

[0297] The server cleans and formats the search results

[0298] The server then formats the tailored product or service information into an easy-to-read format, adding design and wording that matches the user's excitement level.

[0299] Step 8:

[0300] The server sends the results to the device

[0301] The formatted recommendation results are sent to the client device as an HTTP response, including details of, for example, "value data plans."

[0302] Step 9:

[0303] Your device will display recommended results

[0304] The client device displays the recommendation results received from the server to the user, providing information to help the user make the best choice, such as details such as "value data plans" and "family discount plans."

[0305] In this way, it becomes possible to recommend optimal products and services taking into account the user's needs and emotions.

[0306] (Application example 2)

[0307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] There is a demand for systems that can analyze users' needs and emotions and, based on that, quickly and accurately recommend the most appropriate products and services. Furthermore, it is important that the recommendation results are presented to users in an intuitively easy-to-understand format. However, conventional systems cannot fully meet these requirements, and improving the user experience is a challenge.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0310] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests and the user's emotions, a matching means for identifying relevant products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a means for the provision means to display recommendation results using a head-mounted display, smart glasses, or a smartphone. This not only enables optimal products and services to be recommended based on the user's requests and emotions, but also makes it possible to present the recommendation results visually and intuitively.

[0311] "User" refers to a person who uses this system to input requests and receive recommendations for products and services.

[0312] "Requests" refer to requests or wishes regarding products or services that users desire from the system.

[0313] "Input means" refers to a means for providing an interface for users to input requests into the system, and includes text input and voice input.

[0314] "Analysis means" refers to the algorithms and engines used to analyze the requests and emotions received from users.

[0315] "Emotion" refers to the mental state or sensations experienced when a user inputs a request.

[0316] The "matching means" refers to a means for identifying relevant products and services based on the results of the desires and emotions analyzed by the analysis means.

[0317] "Means for providing" refers to the means for providing identified products or services to users, including displaying recommendation results using a head-mounted display, smart glasses, or a smartphone.

[0318] A "head-mounted display" is a display device worn by a user on the head, and is a means for displaying visual information.

[0319] "Smart glasses" refers to a device in the shape of glasses that displays information and has interactive functions.

[0320] A "smartphone" is a type of mobile phone, a digital device equipped with internet connectivity and a variety of applications.

[0321] "Recommendation Results" refers to information about products and services identified based on an analysis of needs and sentiment.

[0322] "Generative AI model" refers to an artificial intelligence model that analyzes user needs and emotions to identify the most suitable products and services.

[0323] The present invention is a system that analyzes user requests and emotions and recommends optimal products and services based on the results. This system is mainly composed of an input means, an analysis means, a matching means, and a provision means.

[0324] User request input

[0325] The user uses an input device to input their request into the system by text or voice. This input device works in conjunction with a smartphone or a robot in a virtual store. For example, imagine a user says to the robot, "I'm looking for a new smartphone."

[0326] Sending data

[0327] The client device (here, a robot in a virtual store) sends the data entered by the user to the server. This is done using HTTP requests, WebSocket communication, etc. At the same time, the emotion engine analyzes the user's emotions and sends this information to the server.

[0328] Receiving and analyzing data

[0329] The server receives user input data and emotion data sent by the client. After receiving the data, the server uses the generative AI model to analyze the request and emotion. For example, the request for a "new smartphone" and emotions such as "excitement" and "anticipation" are analyzed.

[0330] Matching products and services

[0331] The server then searches the database for relevant products and services based on the analysis results. The user's emotional data is also taken into account in this search. For example, different products and services may be recommended if the user is feeling "excited" or "anxious."

[0332] Recommendation result format

[0333] The server then formats the results in an easy-to-view format to offer the identified products and services to the user. The formatted results include product details adapted to the user's emotional state. For example, a "smartphone with the latest camera features" might be recommended to a user in an "expecting" state.

[0334] Sending recommendations

[0335] The server sends the formatted recommendation results to the client's terminal, which then displays the recommended products and services to the user.

[0336] Displaying recommendation results

[0337] The client device then displays the received recommendations to the user, who can view them visually using a head-mounted display, smart glasses, or smartphone, along with additional product reviews and purchase links.

[0338] Specific examples

[0339] For example, if a user looking for a new smartphone uses the system:

[0340] Example prompt: "I'm looking for a new smartphone," say the robot.

[0341] Example response: Robot: "Here's the perfect smartphone for you! It's the latest model with a great camera!"

[0342] In this way, the system of the present invention can quickly and accurately provide optimal products and services taking into account the user's needs and emotions, which is expected to improve the user experience.

[0343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0344] Step 1:

[0345] The user inputs their request to the robot or smartphone in the virtual store. The input method is text input or voice input. At this stage, the user inputs a specific request such as "I'm looking for a new smartphone." The input data is saved in text format. The input amount is expressed as follows:

[0346] Input: User's request text "Looking for a new smartphone"

[0347] Output: Text data "Looking for a new smartphone"

[0348] Step 2:

[0349] The input means sends the user's request text to the analysis means. At the same time, the emotion engine analyzes the user's emotions from their voice and facial expression data and sends the results to the server. The emotion data is expressed in the form of, for example, "excitement" or "expectation." The input data is sent together with the emotion data.

[0350] Input: User's voice data, facial expression data

[0351] Output: Emotion data "Excitement"

[0352] Step 3:

[0353] The server analyzes the user's request text and emotional data received from the client device. It uses a generative AI model to analyze the request content and emotions and extracts the features necessary for specific product recommendations. The request text and emotional data are then input into the analysis model to obtain the analysis results.

[0354] Input: Text data "Looking for a new smartphone", Emotion data "Excitement"

[0355] Output: Analysis results (request "smartphone", emotion "excitement")

[0356] Step 4:

[0357] Based on the analysis results, the server searches the database for related products and services. Here, it generates a search query to identify the most suitable products, taking into account the user's needs and emotions, and searches the database. It creates a list of results and selects them as recommendation candidates.

[0358] Input: Analysis results (request "smartphone", emotion "excitement")

[0359] Output: Product list (e.g., "Latest smartphone model A")

[0360] Step 5:

[0361] The server formats the identified product and service information in a way that is appropriate for the user, shapes the display of the recommendation results to correspond to the user's emotions, and prepares the output as a visually appealing interface.

[0362] Input: Product list (e.g., "Latest Smartphone Model A")

[0363] Output: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[0364] Step 6:

[0365] The server sends the formatted recommendation results to the client device, which can then display details of the recommended products or services to the user. The sending method is HTTP responses or WebSocket messages.

[0366] Input: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[0367] Output: Recommendation result data to client terminal

[0368] Step 7:

[0369] The client device then displays the received recommendations to the user, who can visually confirm the recommended products and services using a head-mounted display, smart glasses, or smartphone, along with additional information such as product reviews and purchase links.

[0370] Input: Recommendation result data

[0371] Output: Visual recommendation results displayed to the user

[0372] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0373] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0374] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0375] [Second embodiment]

[0376] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0377] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0378] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0379] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0380] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0381] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0382] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0383] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0384] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0385] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0386] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0387] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0388] The present invention is a system that enables a user to easily find products and services that best meet his or her needs. The following describes in detail the embodiments of the present invention.

[0389] This system mainly consists of a client terminal, a server, and a user. The client terminal inputs the user's requests and sends them to the server. The server analyzes the received requests, identifies relevant products and services, and recommends them.

[0390] User request input

[0391] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[0392] Sending data

[0393] The client device sends the data entered by the user to the server using an HTTP request or similar.

[0394] Receiving and analyzing data

[0395] The server receives the user's request data sent from the client device. After receiving it, the server analyzes the received data using a generative AI model. Through this analysis, the request is recognized as a "new mobile plan."

[0396] Matching products and services

[0397] The server then identifies relevant products and services based on the analysis, searching its database to find multiple plans and services that best fit the user's needs.

[0398] Providing recommendation results

[0399] The server then formats the identified products and services into an appropriate format and sends it to the client device. This format is designed to be easy for the user to understand. For example, it is displayed as a "great value data plan" or "family discount plan."

[0400] Displaying recommendation results

[0401] Finally, the client device displays the recommendation results received from the server to the user, who can then select the products and services that best suit their needs.

[0402] Specific examples

[0403] As a concrete example, consider the case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. The entered data is sent to the server and analyzed by the generative AI model. Based on the analysis results, "value data plans" and "family discount plans" are identified and sent from the server to the client device. The client device displays these results to the user, allowing the user to select an appropriate plan.

[0404] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[0405] The processing flow will be explained below.

[0406] Step 1:

[0407] User request input

[0408] The user enters their request using a text box or voice input field on the client device. For example, the user enters "I'm looking for a new mobile plan."

[0409] Step 2:

[0410] Sending data

[0411] The client device sends the data entered by the user to the server using a communication method such as an HTTP request, thereby passing the user's request to the server.

[0412] Step 3:

[0413] Receiving data

[0414] The server receives the user's requested data sent from the client terminal, and passes the received data to an internal processing engine.

[0415] Step 4:

[0416] Data analysis

[0417] The server passes the received request data to the generative AI model for analysis. The generative AI model classifies the user's request into appropriate categories and extracts specific needs. For example, from the request for "a new mobile plan," the "mobile plan" category is recognized.

[0418] Step 5:

[0419] Identifying related products and services

[0420] The server then searches the database for relevant products and services based on the analyzed data. This search identifies multiple options based on the user's needs, such as "great data plans" or "family discount plans."

[0421] Step 6:

[0422] Recommendation result format

[0423] The server formats the results in a user-friendly format to provide the identified products and services to the user, for example, a list containing multiple plan names and brief descriptions.

[0424] Step 7:

[0425] Sending recommendations

[0426] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[0427] Step 8:

[0428] Displaying the results

[0429] The client device displays the recommendation results received from the server to the user, including details such as "value data plans" and "family discount plans."

[0430] This series of steps allows users to easily find the products and services that best suit their needs.

[0431] Example 1

[0432] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0433] In today's market, users often have difficulty finding the products and services that best suit their needs. In particular, users often become confused due to the lack of an efficient way to find the best product among the many options. Therefore, there is a need for a system that allows users to easily and quickly find products and services that meet their needs.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0435] In this invention, the server includes input means for receiving requests from users, transmission means for transmitting the requests to the server, analysis means for analyzing the requests, matching means for identifying related products and services based on the analysis results, provision means for providing the identified products and services to the user, and display means for displaying the provided products and services on the user terminal, thereby enabling users to quickly and easily find products and services that best suit their requests.

[0436] "Input means" refers to an interface or device for receiving requests from a user.

[0437] "Transmission means" refers to the protocol or function for transmitting the received request to the server.

[0438] "Analysis means" refers to the technology or model used to analyze the request data received on the server side and understand the content of the request.

[0439] "Matching means" refers to algorithms and database search functions that identify related products and services based on the analysis results.

[0440] "Means of provision" refers to the method or mechanism for providing information about specified products or services to users.

[0441] "Display means" refers to the technology or interface used to display information about the products or services provided on the user's device.

[0442] "Generative AI models" refer to artificial intelligence techniques used to analyze user requests, often utilizing natural language processing models.

[0443] The present invention relates to a system that allows users to easily find products and services that best suit their needs. This system is mainly composed of a client terminal, a server, and a user.

[0444] Hardware and software used

[0445] Client devices are personal computers (PCs) or smartphones. The client devices function as an input means for inputting user requests. The server is the central hardware for receiving, analyzing, processing, and providing data, and runs various software components. A generative AI model (e.g., GPT-3) is used for analysis. A database (e.g., an SQL database or NoSQL database) is used to store information about products and services.

[0446] System Operation

[0447] First, the user enters their request through a text box or voice input field on the client device. For example, the user may enter "I'm looking for a new mobile plan."

[0448] The client device then sends this request data to the server using an HTTP POST request or similar, and the data is transferred to the server in JSON format or similar.

[0449] The server analyzes the received data using an analytical means. This analysis is performed using a generative AI model. The generative AI model uses natural language processing technology to understand the received data and recognize the request, for example, that the user is looking for a "new mobile plan."

[0450] Once the analysis is complete, the server uses matching methods to search the database to identify relevant products and services, such as "great data plans" or "family discount plans."

[0451] The server then formats the identified products and services into JSON format or similar and provides them to the client terminal, which receives this data, converts it into a visually easy-to-understand display format, and presents it to the user.

[0452] Specific examples

[0453] For example, consider a user looking for a new mobile plan using this system. When the user types "I'm looking for a new mobile plan" into the client device, the data is sent to the server in JSON format. The server analyzes this data using a generative AI model and recognizes the request for a "new mobile plan." It then searches for relevant plans, identifying options such as "value data plans" and "family discount plans," and sends them in JSON format to the client device. Finally, the client device displays this information to the user, who can choose the option that best suits them.

[0454] Prompt Sentence Examples

[0455] For example, the following prompts are given to a generative AI model:

[0456] "I'm looking for a new mobile plan. Which plan is best for me?"

[0457] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[0458] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0459] Step 1:

[0460] The user uses the client terminal to input his / her request. The input is made through a text box or a voice input field. For example, the user may input "I'm looking for a new mobile plan." The input data is in text format.

[0461] Step 2:

[0462] The client terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is in text format, but is converted into JSON format when sent to the server.

[0463] Step 3:

[0464] The server receives JSON data sent from the client device. The received data first goes through a data validation process to ensure it is in the correct format. Here, the input is JSON data, and the output is the validated data.

[0465] Step 4:

[0466] The server uses a generative AI model to analyze the received data. The analysis automatically understands the user's request and recognizes, for example, that they are looking for a "new mobile plan." The input is the verified data, and the output is the analysis result of the request.

[0467] Step 5:

[0468] The server searches the database based on the analysis results to identify related products and services. In this case, products and services such as "value data plans" and "family discount plans" are extracted from the database. The input is the analysis results of the request, and the output is a list of identified products and services.

[0469] Step 6:

[0470] The server formats the information on the identified products and services in JSON format and provides it to the client terminal. The input is a list of products and services, and the output is JSON format data.

[0471] Step 7:

[0472] The client device parses the JSON data received from the server and displays it visually to the user. For example, it may be presented as part of a web page or application, containing detailed product or service information and links. The input is the JSON data sent from the server, and the output is a list of products or services that the user can view on the screen.

[0473] In this way, each processing step realizes a series of steps from receiving a user request to identifying related products and services and providing them to the user.

[0474] (Application example 1)

[0475] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0476] In today's information-saturated society, it is difficult for users to easily find the products and services that best meet their needs. As a result, users have to spend a lot of time and effort trying to find the right product or service from the vast number of options. Furthermore, existing systems require a complicated process from when users enter their needs to when the optimal product or service is recommended, which can result in a poor user experience.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0478] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying the identified products and services on the smartphone, thereby enabling the user to easily and quickly find products and services that best match their requests.

[0479] "Input means" refers to a means provided for receiving requests from a user, and includes text input and voice input.

[0480] "Analysis means" means a means used to analyze requests received from users, and analyzes requests using a generative AI model.

[0481] The "matching means" is a means used to identify related products and services based on the analysis results obtained by the analysis means.

[0482] "Means of provision" refers to the means established to provide specified products or services to users.

[0483] The "display means" is a means for providing a visual interface for displaying identified products and services on a smartphone.

[0484] A "database" is a storage device for storing information on multiple products, and is used to search for and list products and services.

[0485] A "generative AI model" is an artificial intelligence model used to analyze a user's natural language requests, for example, using generative AI.

[0486] overview

[0487] This invention is a system that allows users to easily find products and services that best suit their needs. The system includes an input means for receiving requests from users, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying them on a smartphone.

[0488] System Configuration

[0489] Input Method

[0490] Users can provide their requests to the system through text input or voice input, which is made possible by using a smartphone application.

[0491] Analysis means

[0492] The server analyzes the requests received from users using a generative AI model, such as an advanced natural language processing model like OpenAI's GPT-3.

[0493] Matching Method

[0494] The server searches a database based on the analysis results obtained by the analysis means to identify related products and services. This database stores, for example, a number of communication plans and product information.

[0495] Means of provision and display

[0496] The identified products and services are sent from the server to the user's smartphone and displayed visually, allowing the user to select the most appropriate product or service.

[0497] Specific processing flow

[0498] User request input

[0499] The user opens the application on their smartphone and types "I'm looking for a new laptop" into the input field. Voice input is also possible.

[0500] Data transmission and analysis

[0501] The smartphone sends the input data to the server using an HTTP request, and the server uses a generative AI model to analyze the data and understand the user's request.

[0502] Product identification and recommendations

[0503] The server then searches the database based on the analysis results to identify relevant products, such as "high-performance laptops" or "laptops at great prices."

[0504] Displaying recommendation results

[0505] The server sends the compressed results back to the smartphone, which displays the results to the user using the smartphone's display means.

[0506] Hardware and software used

[0507] Hardware: Smartphone (e.g. iPhone, Android)

[0508] Software: Python, requests library, JSON, generative AI model (e.g., OpenAI GPT-3)

[0509] Examples and prompts

[0510] Specific examples

[0511] When a user types, "I'm looking for a new laptop," the system recommends the most suitable laptop, using a generative AI model to analyze the natural language request, identify relevant products from a database, and display them on the smartphone.

[0512] Prompt Sentence Examples

[0513] A user types, "I'm looking for a new laptop."

[0514] Please recommend the most suitable product from the options below:

[0515] 1. High-performance laptop

[0516] 2. Great value laptops

[0517] 3. Lightweight model

[0518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0519] Step 1:

[0520] The user opens the application on their smartphone and enters their request into an input field, for example, "I'm looking for a new laptop," either by typing it in text or by using voice input to communicate their request to the system. The data obtained from this input field becomes the initial data.

[0521] Input: User's requested text or voice data

[0522] Output: User request data on the client terminal

[0523] Specific behavior: A smartphone application provides a text box or voice input interface to allow users to input requests in natural language.

[0524] Step 2:

[0525] The client terminal sends the data entered by the user to the server as an HTTP request, formatted in JSON.

[0526] Input: User request data on the client device

[0527] Output: JSON data sent in the HTTP request

[0528] What happens: The smartphone application uses the requests library to send an HTTP POST request to the server, which includes the user's input data.

[0529] Step 3:

[0530] The server receives the user's request data sent from the client device, then inputs the data into the generative AI model and analyzes the request.

[0531] Input: JSON data sent from the client terminal

[0532] Output: Parsed request data

[0533] How it works: The server uses a generative AI model, such as OpenAI's GPT-3, to parse the natural language request it receives. For example, the request might be "I'm looking for a new laptop."

[0534] Step 4:

[0535] Based on the analysis results, the server searches the database to identify related products and services. Multiple matching candidates are found and information about each is retrieved.

[0536] Input: Parsed request data

[0537] Output: Multiple identified product information

[0538] Specific operation: The server searches a database (e.g., MySQL or SQLite) based on the analysis results and identifies related products such as "high-performance laptops" and "laptops at great prices."

[0539] Step 5:

[0540] The server generates a list of recommendations for the identified products and services to offer to the user and sends it back to the client device in JSON format.

[0541] Input: Multiple identified product information

[0542] Output: JSON data of the recommendation list

[0543] Specific operation: The server formats the data of the identified products, generates a recommendation list, converts it into JSON format data, and sends it to the client device.

[0544] Step 6:

[0545] The client terminal visually displays the received recommendation list to the user, who can then select the product that best meets their needs based on the displayed information.

[0546] Input: JSON data of the recommendation list received from the server

[0547] Output: Product list displayed on the client device

[0548] Specific operation: The smartphone application parses the received JSON data and displays a list of recommended products in the user interface. The user can then select from the displayed list of products.

[0549] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0550] The present invention is a system for analyzing a user's needs and emotions and recommending optimal products and services. Hereinafter, an embodiment of the present invention will be specifically described.

[0551] This system mainly consists of a client device, a server, a user, and an emotion engine. The client device inputs the user's requests and sends them to the server. The server analyzes the received requests and emotions, and identifies and recommends related products and services. The emotion engine recognizes the user's emotions and uses this emotional information for analysis.

[0552] User request input

[0553] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[0554] Sending data

[0555] The client device sends the data entered by the user to the server using an HTTP request. At this stage, the emotion engine also simultaneously analyzes the user's emotions and sends that information to the server.

[0556] Receiving and analyzing data

[0557] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server uses a generative AI model and emotion engine to analyze the request and emotion. This analysis identifies the content of the request and the user's emotion. For example, the request for a "new mobile plan" and the emotion of "excitement" or "anxiety" are analyzed.

[0558] Matching products and services

[0559] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account the user's emotional data. For example, the recommended products and services may differ depending on whether the user is "excited" or "anxious."

[0560] Recommendation result format

[0561] The server formats the results to present the identified products and services to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, a "great data plan" might be especially highlighted to a user in an "excited" state.

[0562] Sending recommendations

[0563] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[0564] Displaying recommendation results

[0565] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[0566] Specific examples

[0567] Consider a case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. At the same time, the emotion engine recognizes the user's "excitement" state. The entered data and emotion data are sent to the server and analyzed by the generative AI model and emotion engine. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted in a format appropriate for the user's excitement state. Finally, the recommendation results are displayed on the client device, allowing the user to choose the plan that best suits them.

[0568] In this way, the system of the present invention makes it possible to quickly provide optimal products and services taking into consideration the user's needs and emotions.

[0569] The processing flow will be explained below.

[0570] Step 1:

[0571] User request input

[0572] The user enters their request using a text box or voice input field on the client device, for example, "I'm looking for a new mobile plan."

[0573] Step 2:

[0574] Sending data

[0575] The client device sends the request data entered by the user to the server using a communication method such as an HTTP request. The emotion engine also analyzes the user's emotions, and this emotion data is also sent to the server at the same time.

[0576] Step 3:

[0577] Receiving data

[0578] The server receives the user's request data and emotion data sent from the client's terminal, and passes the received data to an internal processing engine.

[0579] Step 4:

[0580] Data analysis

[0581] The server passes the received request data to the generative AI model for analysis. At the same time, the emotion engine analyzes the received emotion data. The generative AI model classifies the user's request into an appropriate category, and the emotion engine recognizes the user's emotional state (e.g., "excited" or "anxious").

[0582] Step 5:

[0583] Reflecting emotional data

[0584] The server integrates the analyzed request data with the emotional data and adjusts the recommended products and services based on the user's emotional state. For example, if the user is feeling anxious, it will recommend services that will make them feel more secure.

[0585] Step 6:

[0586] Identifying related products and services

[0587] The server then searches the database for relevant products and services based on the combined data, identifying the most suitable options based on the analysis results.

[0588] Step 7:

[0589] Recommendation result format

[0590] The server formats the results in an easy-to-view format to provide the identified products and services to the user, including product details that are sensitive to the user's emotional state.

[0591] Step 8:

[0592] Sending recommendations

[0593] The server sends the formatted recommendation results to the client's device, which prepares them for display to the user.

[0594] Step 9:

[0595] Displaying the results

[0596] The client device receives the recommendation results from the server and displays them to the user, including details that correspond to the user's emotional state, such as "value data plans" and "family discount plans."

[0597] This series of steps makes it easy for users to find the products and services that best suit their needs and emotions.

[0598] Example 2

[0599] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0600] Conventional recommendation systems simply analyze users' requests and provide relevant products and services. However, because these systems do not take into account the user's emotional state, they often fail to recommend products and services that are truly optimal for the user. Furthermore, if the input request is interpreted subtly differently depending on the user's emotions, analysis results that do not take this into account will not meet the user's expectations. For this reason, there is a need for a system that can analyze requests and recommend products and services based on the user's emotional state.

[0601] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for receiving a request from a user, an analysis means for analyzing the request, the analysis means including an emotion analysis means for analyzing the user's emotion, a matching means for identifying related products and services based on the analysis results, and a provision means for providing the identified products and services to the user. This makes it possible to analyze the request taking into consideration the user's emotional state and to recommend optimal products and services based on the results.

[0602] "Input means" refers to a method or device for receiving requests from a user, including text input and voice input.

[0603] The "analysis means" is a method or device for analyzing requests received from users.

[0604] "Emotion analysis means" refers to a method or device for analyzing the emotional state of a user when analyzing the user's needs.

[0605] The "matching means" is a method or device for identifying related products or services based on the results obtained by the analysis means.

[0606] "Provision means" refers to a method or device for providing a specified product or service to a user.

[0607] "Generative AI model" refers to an artificial intelligence model used to analyze user requests.

[0608] A "prompt" is a sentence to be input into a generative AI model, and is a document that contains a specific request or emotion.

[0609] This invention is a system that analyzes user needs and emotions and recommends optimal products and services. This system is mainly composed of a client terminal, a server, users, and an emotion analysis engine.

[0610] User request input

[0611] A user uses a client device to input their request into the system. This input is done through an interface such as a text box or voice input field. For example, a user might input, "I'm looking for a new mobile plan."

[0612] Sending data

[0613] The client device sends the data entered by the user to the server using an HTTP request or similar. At the same time, the emotion analysis engine also analyzes the user's emotional state and sends that information to the server. At this stage, the user's request and emotional information are combined into a single data packet.

[0614] Receiving and analyzing data

[0615] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server analyzes the data using the generative AI model and emotion analysis engine. Specifically, it generates a prompt sentence to input the data into the generative AI model. For example, if the request is "Looking for a new mobile plan" and the emotion is "Excited," the server inputs the following prompt sentence into the generative AI model:

[0616] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0617] A generative AI model analyzes this prompt and generates a list of related products and services.

[0618] Matching products and services

[0619] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account emotional data. For example, different products and services may be recommended if the user is "excited" or "anxious."

[0620] Recommendation result format

[0621] The server then formats the identified products and services to present to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, "great data plans" might be especially emphasized for an excited user.

[0622] Sending recommendations

[0623] The server sends the formatted recommendation results to the client device as an HTTP response, allowing the client device to display the recommended products and services to the user.

[0624] Displaying recommendation results

[0625] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[0626] Specific examples

[0627] Consider a case where a user looking for a new mobile plan uses this system. The user types "I'm looking for a new mobile plan" into the client device, and the emotion analysis engine recognizes the "excited" state. This data is sent to the server, which prompts the generative AI model with the following sentence:

[0628] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0629] The input is analyzed. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted to suit the user's excitement level. Finally, the recommendation results are displayed on the client's device, allowing the user to choose the plan that best suits them.

[0630] In this way, the system of the present invention can quickly provide optimal products and services taking into account the user's needs and emotional state.

[0631] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0632] Step 1:

[0633] The user enters a request

[0634] The user uses the client device to enter their request into a text box or voice input field, for example, by typing "I'm looking for a new mobile plan." Once this input is complete, the device temporarily stores the data.

[0635] Step 2:

[0636] The device sends the data to the server

[0637] The client device sends input data to the server in the form of an HTTP request. At the same time, the emotion analysis engine analyzes the user's emotional state and sends that information in the same request to the server. For example, the user's request and emotional information such as "excitement" are packaged in a data packet.

[0638] Step 3:

[0639] The server receives the data

[0640] The server receives the HTTP request sent from the client device. The received data includes the user's desired text and emotional state information. The server converts this data into the format required for analysis.

[0641] Step 4:

[0642] The server generates a prompt for parsing.

[0643] The server converts the received data into prompts to be fed into the generative AI model. For example, based on the request "I'm looking for a new mobile plan" and the emotional state "excited," it generates the following prompt:

[0644] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0645] Step 5:

[0646] The server analyzes the data using a generative AI model

[0647] The server inputs the generated prompt into a generative AI model, which analyzes it based on the customer's request and sentiment. The generative AI model processes the prompt and outputs a list of related products and services.

[0648] Step 6:

[0649] The server uses sentiment analysis techniques to adjust the results.

[0650] The server uses sentiment analysis to tailor the results from the generative AI model to the user's emotional state, highlighting, for example, special offers and discounts to excited users.

[0651] Step 7:

[0652] The server cleans and formats the search results

[0653] The server then formats the tailored product or service information into an easy-to-read format, adding design and wording that matches the user's excitement level.

[0654] Step 8:

[0655] The server sends the results to the device

[0656] The formatted recommendation results are sent to the client device as an HTTP response, including details of, for example, "value data plans."

[0657] Step 9:

[0658] Your device will display recommended results

[0659] The client device displays the recommendation results received from the server to the user, providing information to help the user make the best choice, such as details such as "value data plans" and "family discount plans."

[0660] In this way, it becomes possible to recommend optimal products and services taking into account the user's needs and emotions.

[0661] (Application example 2)

[0662] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0663] There is a demand for systems that can analyze users' needs and emotions and, based on that, quickly and accurately recommend the most appropriate products and services. Furthermore, it is important that the recommendation results are presented to users in an intuitively easy-to-understand format. However, conventional systems cannot fully meet these requirements, and improving the user experience is a challenge.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0665] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests and the user's emotions, a matching means for identifying relevant products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a means for the provision means to display recommendation results using a head-mounted display, smart glasses, or a smartphone. This not only enables optimal products and services to be recommended based on the user's requests and emotions, but also makes it possible to present the recommendation results visually and intuitively.

[0666] "User" refers to a person who uses this system to input requests and receive recommendations for products and services.

[0667] "Requests" refer to requests or wishes regarding products or services that users desire from the system.

[0668] "Input means" refers to a means for providing an interface for users to input requests into the system, and includes text input and voice input.

[0669] "Analysis means" refers to the algorithms and engines used to analyze the requests and emotions received from users.

[0670] "Emotion" refers to the mental state or sensations experienced when a user inputs a request.

[0671] The "matching means" refers to a means for identifying relevant products and services based on the results of the desires and emotions analyzed by the analysis means.

[0672] "Means for providing" refers to the means for providing identified products or services to users, including displaying recommendation results using a head-mounted display, smart glasses, or a smartphone.

[0673] A "head-mounted display" is a display device worn by a user on the head, and is a means for displaying visual information.

[0674] "Smart glasses" refers to a device in the shape of glasses that displays information and has interactive functions.

[0675] A "smartphone" is a type of mobile phone, a digital device equipped with internet connectivity and a variety of applications.

[0676] "Recommendation Results" refers to information about products and services identified based on an analysis of needs and sentiment.

[0677] "Generative AI model" refers to an artificial intelligence model that analyzes user needs and emotions to identify the most suitable products and services.

[0678] The present invention is a system that analyzes user requests and emotions and recommends optimal products and services based on the results. This system is mainly composed of an input means, an analysis means, a matching means, and a provision means.

[0679] User request input

[0680] The user uses an input device to input their request into the system by text or voice. This input device works in conjunction with a smartphone or a robot in a virtual store. For example, imagine a user says to the robot, "I'm looking for a new smartphone."

[0681] Sending data

[0682] The client device (here, a robot in a virtual store) sends the data entered by the user to the server. This is done using HTTP requests, WebSocket communication, etc. At the same time, the emotion engine analyzes the user's emotions and sends this information to the server.

[0683] Receiving and analyzing data

[0684] The server receives user input data and emotion data sent by the client. After receiving the data, the server uses the generative AI model to analyze the request and emotion. For example, the request for a "new smartphone" and emotions such as "excitement" and "anticipation" are analyzed.

[0685] Matching products and services

[0686] The server then searches the database for relevant products and services based on the analysis results. The user's emotional data is also taken into account in this search. For example, different products and services may be recommended if the user is feeling "excited" or "anxious."

[0687] Recommendation result format

[0688] The server then formats the results in an easy-to-view format to offer the identified products and services to the user. The formatted results include product details adapted to the user's emotional state. For example, a "smartphone with the latest camera features" might be recommended to a user in an "expecting" state.

[0689] Sending recommendations

[0690] The server sends the formatted recommendation results to the client's terminal, which then displays the recommended products and services to the user.

[0691] Displaying recommendation results

[0692] The client device then displays the received recommendations to the user, who can view them visually using a head-mounted display, smart glasses, or smartphone, along with additional product reviews and purchase links.

[0693] Specific examples

[0694] For example, if a user looking for a new smartphone uses the system:

[0695] Example prompt: "I'm looking for a new smartphone," say the robot.

[0696] Example response: Robot: "Here's the perfect smartphone for you! It's the latest model with a great camera!"

[0697] In this way, the system of the present invention can quickly and accurately provide optimal products and services taking into account the user's needs and emotions, which is expected to improve the user experience.

[0698] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0699] Step 1:

[0700] The user inputs their request to the robot or smartphone in the virtual store. The input method is text input or voice input. At this stage, the user inputs a specific request such as "I'm looking for a new smartphone." The input data is saved in text format. The input amount is expressed as follows:

[0701] Input: User's request text "Looking for a new smartphone"

[0702] Output: Text data "Looking for a new smartphone"

[0703] Step 2:

[0704] The input means sends the user's request text to the analysis means. At the same time, the emotion engine analyzes the user's emotions from their voice and facial expression data and sends the results to the server. The emotion data is expressed in the form of, for example, "excitement" or "expectation." The input data is sent together with the emotion data.

[0705] Input: User's voice data, facial expression data

[0706] Output: Emotion data "Excitement"

[0707] Step 3:

[0708] The server analyzes the user's request text and emotional data received from the client device. It uses a generative AI model to analyze the request content and emotions and extracts the features necessary for specific product recommendations. The request text and emotional data are then input into the analysis model to obtain the analysis results.

[0709] Input: Text data "Looking for a new smartphone", Emotion data "Excitement"

[0710] Output: Analysis results (request "smartphone", emotion "excitement")

[0711] Step 4:

[0712] Based on the analysis results, the server searches the database for related products and services. Here, it generates a search query to identify the most suitable products, taking into account the user's needs and emotions, and searches the database. It creates a list of results and selects them as recommendation candidates.

[0713] Input: Analysis results (request "smartphone", emotion "excitement")

[0714] Output: Product list (e.g., "Latest smartphone model A")

[0715] Step 5:

[0716] The server formats the identified product and service information in a way that is appropriate for the user, shapes the display of the recommendation results to correspond to the user's emotions, and prepares the output as a visually appealing interface.

[0717] Input: Product list (e.g., "Latest Smartphone Model A")

[0718] Output: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[0719] Step 6:

[0720] The server sends the formatted recommendation results to the client device, which can then display details of the recommended products or services to the user. The sending method is HTTP responses or WebSocket messages.

[0721] Input: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[0722] Output: Recommendation result data to client terminal

[0723] Step 7:

[0724] The client device then displays the received recommendations to the user, who can visually confirm the recommended products and services using a head-mounted display, smart glasses, or smartphone, along with additional information such as product reviews and purchase links.

[0725] Input: Recommendation result data

[0726] Output: Visual recommendation results displayed to the user

[0727] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0728] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0729] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0730] [Third embodiment]

[0731] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0732] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0733] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0734] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0735] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0736] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0737] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0738] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0739] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0740] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0741] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0742] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0743] The present invention is a system that enables a user to easily find products and services that best meet his or her needs. The following describes in detail the embodiments of the present invention.

[0744] This system mainly consists of a client terminal, a server, and a user. The client terminal inputs the user's requests and sends them to the server. The server analyzes the received requests, identifies relevant products and services, and recommends them.

[0745] User request input

[0746] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[0747] Sending data

[0748] The client device sends the data entered by the user to the server using an HTTP request or similar.

[0749] Receiving and analyzing data

[0750] The server receives the user's request data sent from the client device. After receiving it, the server analyzes the received data using a generative AI model. Through this analysis, the request is recognized as a "new mobile plan."

[0751] Matching products and services

[0752] The server then identifies relevant products and services based on the analysis, searching its database to find multiple plans and services that best fit the user's needs.

[0753] Providing recommendation results

[0754] The server then formats the identified products and services into an appropriate format and sends it to the client device. This format is designed to be easy for the user to understand. For example, it is displayed as a "great value data plan" or "family discount plan."

[0755] Displaying recommendation results

[0756] Finally, the client device displays the recommendation results received from the server to the user, who can then select the products and services that best suit their needs.

[0757] Specific examples

[0758] As a concrete example, consider the case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. The entered data is sent to the server and analyzed by the generative AI model. Based on the analysis results, "value data plans" and "family discount plans" are identified and sent from the server to the client device. The client device displays these results to the user, allowing the user to select an appropriate plan.

[0759] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[0760] The processing flow will be explained below.

[0761] Step 1:

[0762] User request input

[0763] The user enters their request using a text box or voice input field on the client device. For example, the user enters "I'm looking for a new mobile plan."

[0764] Step 2:

[0765] Sending data

[0766] The client device sends the data entered by the user to the server using a communication method such as an HTTP request, thereby passing the user's request to the server.

[0767] Step 3:

[0768] Receiving data

[0769] The server receives the user's requested data sent from the client terminal, and passes the received data to an internal processing engine.

[0770] Step 4:

[0771] Data analysis

[0772] The server passes the received request data to the generative AI model for analysis. The generative AI model classifies the user's request into appropriate categories and extracts specific needs. For example, from the request for "a new mobile plan," the "mobile plan" category is recognized.

[0773] Step 5:

[0774] Identifying related products and services

[0775] The server then searches the database for relevant products and services based on the analyzed data. This search identifies multiple options based on the user's needs, such as "great data plans" or "family discount plans."

[0776] Step 6:

[0777] Recommendation result format

[0778] The server formats the results in a user-friendly format to provide the identified products and services to the user, for example, a list containing multiple plan names and brief descriptions.

[0779] Step 7:

[0780] Sending recommendations

[0781] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[0782] Step 8:

[0783] Displaying the results

[0784] The client device displays the recommendation results received from the server to the user, including details such as "value data plans" and "family discount plans."

[0785] This series of steps allows users to easily find the products and services that best suit their needs.

[0786] Example 1

[0787] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0788] In today's market, users often have difficulty finding the products and services that best suit their needs. In particular, users often become confused due to the lack of an efficient way to find the best product among the many options. Therefore, there is a need for a system that allows users to easily and quickly find products and services that meet their needs.

[0789] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0790] In this invention, the server includes input means for receiving requests from users, transmission means for transmitting the requests to the server, analysis means for analyzing the requests, matching means for identifying related products and services based on the analysis results, provision means for providing the identified products and services to the user, and display means for displaying the provided products and services on the user terminal, thereby enabling users to quickly and easily find products and services that best suit their requests.

[0791] "Input means" refers to an interface or device for receiving requests from a user.

[0792] "Transmission means" refers to the protocol or function for transmitting the received request to the server.

[0793] "Analysis means" refers to the technology or model used to analyze the request data received on the server side and understand the content of the request.

[0794] "Matching means" refers to algorithms and database search functions that identify related products and services based on the analysis results.

[0795] "Means of provision" refers to the method or mechanism for providing information about specified products or services to users.

[0796] "Display means" refers to the technology or interface used to display information about the products or services provided on the user's device.

[0797] "Generative AI models" refer to artificial intelligence techniques used to analyze user requests, often utilizing natural language processing models.

[0798] The present invention relates to a system that allows users to easily find products and services that best suit their needs. This system is mainly composed of a client terminal, a server, and a user.

[0799] Hardware and software used

[0800] Client devices are personal computers (PCs) or smartphones. The client devices function as an input means for inputting user requests. The server is the central hardware for receiving, analyzing, processing, and providing data, and runs various software components. A generative AI model (e.g., GPT-3) is used for analysis. A database (e.g., an SQL database or NoSQL database) is used to store information about products and services.

[0801] System Operation

[0802] First, the user enters their request through a text box or voice input field on the client device. For example, the user may enter "I'm looking for a new mobile plan."

[0803] The client device then sends this request data to the server using an HTTP POST request or similar, and the data is transferred to the server in JSON format or similar.

[0804] The server analyzes the received data using an analytical means. This analysis is performed using a generative AI model. The generative AI model uses natural language processing technology to understand the received data and recognize the request, for example, that the user is looking for a "new mobile plan."

[0805] Once the analysis is complete, the server uses matching methods to search the database to identify relevant products and services, such as "great data plans" or "family discount plans."

[0806] The server then formats the identified products and services into JSON format or similar and provides them to the client terminal, which receives this data, converts it into a visually easy-to-understand display format, and presents it to the user.

[0807] Specific examples

[0808] For example, consider a user looking for a new mobile plan using this system. When the user types "I'm looking for a new mobile plan" into the client device, the data is sent to the server in JSON format. The server analyzes this data using a generative AI model and recognizes the request for a "new mobile plan." It then searches for relevant plans, identifying options such as "value data plans" and "family discount plans," and sends them in JSON format to the client device. Finally, the client device displays this information to the user, who can choose the option that best suits them.

[0809] Prompt Sentence Examples

[0810] For example, the following prompts are given to a generative AI model:

[0811] "I'm looking for a new mobile plan. Which plan is best for me?"

[0812] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[0813] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0814] Step 1:

[0815] The user uses the client terminal to input his / her request. The input is made through a text box or a voice input field. For example, the user may input "I'm looking for a new mobile plan." The input data is in text format.

[0816] Step 2:

[0817] The client terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is in text format, but is converted into JSON format when sent to the server.

[0818] Step 3:

[0819] The server receives JSON data sent from the client device. The received data first goes through a data validation process to ensure it is in the correct format. Here, the input is JSON data, and the output is the validated data.

[0820] Step 4:

[0821] The server uses a generative AI model to analyze the received data. The analysis automatically understands the user's request and recognizes, for example, that they are looking for a "new mobile plan." The input is the verified data, and the output is the analysis result of the request.

[0822] Step 5:

[0823] The server searches the database based on the analysis results to identify related products and services. In this case, products and services such as "value data plans" and "family discount plans" are extracted from the database. The input is the analysis results of the request, and the output is a list of identified products and services.

[0824] Step 6:

[0825] The server formats the information on the identified products and services in JSON format and provides it to the client terminal. The input is a list of products and services, and the output is JSON format data.

[0826] Step 7:

[0827] The client device parses the JSON data received from the server and displays it visually to the user. For example, it may be presented as part of a web page or application, containing detailed product or service information and links. The input is the JSON data sent from the server, and the output is a list of products or services that the user can view on the screen.

[0828] In this way, each processing step realizes a series of steps from receiving a user request to identifying related products and services and providing them to the user.

[0829] (Application example 1)

[0830] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0831] In today's information-saturated society, it is difficult for users to easily find the products and services that best meet their needs. As a result, users have to spend a lot of time and effort trying to find the right product or service from the vast number of options. Furthermore, existing systems require a complicated process from when users enter their needs to when the optimal product or service is recommended, which can result in a poor user experience.

[0832] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0833] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying the identified products and services on the smartphone, thereby enabling the user to easily and quickly find products and services that best match their requests.

[0834] "Input means" refers to a means provided for receiving requests from a user, and includes text input and voice input.

[0835] "Analysis means" means a means used to analyze requests received from users, and analyzes requests using a generative AI model.

[0836] The "matching means" is a means used to identify related products and services based on the analysis results obtained by the analysis means.

[0837] "Means of provision" refers to the means established to provide specified products or services to users.

[0838] The "display means" is a means for providing a visual interface for displaying identified products and services on a smartphone.

[0839] A "database" is a storage device for storing information on multiple products, and is used to search for and list products and services.

[0840] A "generative AI model" is an artificial intelligence model used to analyze a user's natural language requests, for example, using generative AI.

[0841] overview

[0842] This invention is a system that allows users to easily find products and services that best suit their needs. The system includes an input means for receiving requests from users, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying them on a smartphone.

[0843] System Configuration

[0844] Input Method

[0845] Users can provide their requests to the system through text input or voice input, which is made possible by using a smartphone application.

[0846] Analysis means

[0847] The server analyzes the requests received from users using a generative AI model, such as an advanced natural language processing model like OpenAI's GPT-3.

[0848] Matching Method

[0849] The server searches a database based on the analysis results obtained by the analysis means to identify related products and services. This database stores, for example, a number of communication plans and product information.

[0850] Means of provision and display

[0851] The identified products and services are sent from the server to the user's smartphone and displayed visually, allowing the user to select the most appropriate product or service.

[0852] Specific processing flow

[0853] User request input

[0854] The user opens the application on their smartphone and types "I'm looking for a new laptop" into the input field. Voice input is also possible.

[0855] Data transmission and analysis

[0856] The smartphone sends the input data to the server using an HTTP request, and the server uses a generative AI model to analyze the data and understand the user's request.

[0857] Product identification and recommendations

[0858] The server then searches the database based on the analysis results to identify relevant products, such as "high-performance laptops" or "laptops at great prices."

[0859] Displaying recommendation results

[0860] The server sends the compressed results back to the smartphone, which displays the results to the user using the smartphone's display means.

[0861] Hardware and software used

[0862] Hardware: Smartphone (e.g. iPhone, Android)

[0863] Software: Python, requests library, JSON, generative AI model (e.g., OpenAI GPT-3)

[0864] Examples and prompts

[0865] Specific examples

[0866] When a user types, "I'm looking for a new laptop," the system recommends the most suitable laptop, using a generative AI model to analyze the natural language request, identify relevant products from a database, and display them on the smartphone.

[0867] Prompt Sentence Examples

[0868] A user types, "I'm looking for a new laptop."

[0869] Please recommend the most suitable product from the options below:

[0870] 1. High-performance laptop

[0871] 2. Great value laptops

[0872] 3. Lightweight model

[0873] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0874] Step 1:

[0875] The user opens the application on their smartphone and enters their request into an input field, for example, "I'm looking for a new laptop," either by typing it in text or by using voice input to communicate their request to the system. The data obtained from this input field becomes the initial data.

[0876] Input: User's requested text or voice data

[0877] Output: User request data on the client terminal

[0878] Specific behavior: A smartphone application provides a text box or voice input interface to allow users to input requests in natural language.

[0879] Step 2:

[0880] The client terminal sends the data entered by the user to the server as an HTTP request, formatted in JSON.

[0881] Input: User request data on the client device

[0882] Output: JSON data sent in the HTTP request

[0883] What happens: The smartphone application uses the requests library to send an HTTP POST request to the server, which includes the user's input data.

[0884] Step 3:

[0885] The server receives the user's request data sent from the client device, then inputs the data into the generative AI model and analyzes the request.

[0886] Input: JSON data sent from the client terminal

[0887] Output: Parsed request data

[0888] How it works: The server uses a generative AI model, such as OpenAI's GPT-3, to parse the natural language request it receives. For example, the request might be "I'm looking for a new laptop."

[0889] Step 4:

[0890] Based on the analysis results, the server searches the database to identify related products and services. Multiple matching candidates are found and information about each is retrieved.

[0891] Input: Parsed request data

[0892] Output: Multiple identified product information

[0893] Specific operation: The server searches a database (e.g., MySQL or SQLite) based on the analysis results and identifies related products such as "high-performance laptops" and "laptops at great prices."

[0894] Step 5:

[0895] The server generates a list of recommendations for the identified products and services to offer to the user and sends it back to the client device in JSON format.

[0896] Input: Multiple identified product information

[0897] Output: JSON data of the recommendation list

[0898] Specific operation: The server formats the data of the identified products, generates a recommendation list, converts it into JSON format data, and sends it to the client device.

[0899] Step 6:

[0900] The client terminal visually displays the received recommendation list to the user, who can then select the product that best meets their needs based on the displayed information.

[0901] Input: JSON data of the recommendation list received from the server

[0902] Output: Product list displayed on the client device

[0903] Specific operation: The smartphone application parses the received JSON data and displays a list of recommended products in the user interface. The user can then select from the displayed list of products.

[0904] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0905] The present invention is a system for analyzing a user's needs and emotions and recommending optimal products and services. Hereinafter, an embodiment of the present invention will be specifically described.

[0906] This system mainly consists of a client device, a server, a user, and an emotion engine. The client device inputs the user's requests and sends them to the server. The server analyzes the received requests and emotions, and identifies and recommends related products and services. The emotion engine recognizes the user's emotions and uses this emotional information for analysis.

[0907] User request input

[0908] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[0909] Sending data

[0910] The client device sends the data entered by the user to the server using an HTTP request. At this stage, the emotion engine also simultaneously analyzes the user's emotions and sends that information to the server.

[0911] Receiving and analyzing data

[0912] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server uses a generative AI model and emotion engine to analyze the request and emotion. This analysis identifies the content of the request and the user's emotion. For example, the request for a "new mobile plan" and the emotion of "excitement" or "anxiety" are analyzed.

[0913] Matching products and services

[0914] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account the user's emotional data. For example, the recommended products and services may differ depending on whether the user is "excited" or "anxious."

[0915] Recommendation result format

[0916] The server formats the results to present the identified products and services to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, a "great data plan" might be especially highlighted to a user in an "excited" state.

[0917] Sending recommendations

[0918] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[0919] Displaying recommendation results

[0920] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[0921] Specific examples

[0922] Consider a case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. At the same time, the emotion engine recognizes the user's "excitement" state. The entered data and emotion data are sent to the server and analyzed by the generative AI model and emotion engine. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted in a format appropriate for the user's excitement state. Finally, the recommendation results are displayed on the client device, allowing the user to choose the plan that best suits them.

[0923] In this way, the system of the present invention makes it possible to quickly provide optimal products and services taking into consideration the user's needs and emotions.

[0924] The processing flow will be explained below.

[0925] Step 1:

[0926] User request input

[0927] The user enters their request using a text box or voice input field on the client device, for example, "I'm looking for a new mobile plan."

[0928] Step 2:

[0929] Sending data

[0930] The client device sends the request data entered by the user to the server using a communication method such as an HTTP request. The emotion engine also analyzes the user's emotions, and this emotion data is also sent to the server at the same time.

[0931] Step 3:

[0932] Receiving data

[0933] The server receives the user's request data and emotion data sent from the client's terminal, and passes the received data to an internal processing engine.

[0934] Step 4:

[0935] Data analysis

[0936] The server passes the received request data to the generative AI model for analysis. At the same time, the emotion engine analyzes the received emotion data. The generative AI model classifies the user's request into an appropriate category, and the emotion engine recognizes the user's emotional state (e.g., "excited" or "anxious").

[0937] Step 5:

[0938] Reflecting emotional data

[0939] The server integrates the analyzed request data with the emotional data and adjusts the recommended products and services based on the user's emotional state. For example, if the user is feeling anxious, it will recommend services that will make them feel more secure.

[0940] Step 6:

[0941] Identifying related products and services

[0942] The server then searches the database for relevant products and services based on the combined data, identifying the most suitable options based on the analysis results.

[0943] Step 7:

[0944] Recommendation result format

[0945] The server formats the results in an easy-to-view format to provide the identified products and services to the user, including product details that are sensitive to the user's emotional state.

[0946] Step 8:

[0947] Sending recommendations

[0948] The server sends the formatted recommendation results to the client's device, which prepares them for display to the user.

[0949] Step 9:

[0950] Displaying the results

[0951] The client device receives the recommendation results from the server and displays them to the user, including details that correspond to the user's emotional state, such as "value data plans" and "family discount plans."

[0952] This series of steps makes it easy for users to find the products and services that best suit their needs and emotions.

[0953] Example 2

[0954] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0955] Conventional recommendation systems simply analyze users' requests and provide relevant products and services. However, because these systems do not take into account the user's emotional state, they often fail to recommend products and services that are truly optimal for the user. Furthermore, if the input request is interpreted subtly differently depending on the user's emotions, analysis results that do not take this into account will not meet the user's expectations. For this reason, there is a need for a system that can analyze requests and recommend products and services based on the user's emotional state.

[0956] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for receiving a request from a user, an analysis means for analyzing the request, the analysis means including an emotion analysis means for analyzing the user's emotion, a matching means for identifying related products and services based on the analysis results, and a provision means for providing the identified products and services to the user. This makes it possible to analyze the request taking into consideration the user's emotional state and to recommend optimal products and services based on the results.

[0957] "Input means" refers to a method or device for receiving requests from a user, including text input and voice input.

[0958] The "analysis means" is a method or device for analyzing requests received from users.

[0959] "Emotion analysis means" refers to a method or device for analyzing the emotional state of a user when analyzing the user's needs.

[0960] The "matching means" is a method or device for identifying related products or services based on the results obtained by the analysis means.

[0961] "Provision means" refers to a method or device for providing a specified product or service to a user.

[0962] "Generative AI model" refers to an artificial intelligence model used to analyze user requests.

[0963] A "prompt" is a sentence to be input into a generative AI model, and is a document that contains a specific request or emotion.

[0964] This invention is a system that analyzes user needs and emotions and recommends optimal products and services. This system is mainly composed of a client terminal, a server, users, and an emotion analysis engine.

[0965] User request input

[0966] A user uses a client device to input their request into the system. This input is done through an interface such as a text box or voice input field. For example, a user might input, "I'm looking for a new mobile plan."

[0967] Sending data

[0968] The client device sends the data entered by the user to the server using an HTTP request or similar. At the same time, the emotion analysis engine also analyzes the user's emotional state and sends that information to the server. At this stage, the user's request and emotional information are combined into a single data packet.

[0969] Receiving and analyzing data

[0970] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server analyzes the data using the generative AI model and emotion analysis engine. Specifically, it generates a prompt sentence to input the data into the generative AI model. For example, if the request is "Looking for a new mobile plan" and the emotion is "Excited," the server inputs the following prompt sentence into the generative AI model:

[0971] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0972] A generative AI model analyzes this prompt and generates a list of related products and services.

[0973] Matching products and services

[0974] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account emotional data. For example, different products and services may be recommended if the user is "excited" or "anxious."

[0975] Recommendation result format

[0976] The server then formats the identified products and services to present to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, "great data plans" might be especially emphasized for an excited user.

[0977] Sending recommendations

[0978] The server sends the formatted recommendation results to the client device as an HTTP response, allowing the client device to display the recommended products and services to the user.

[0979] Displaying recommendation results

[0980] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[0981] Specific examples

[0982] Consider a case where a user looking for a new mobile plan uses this system. The user types "I'm looking for a new mobile plan" into the client device, and the emotion analysis engine recognizes the "excited" state. This data is sent to the server, which prompts the generative AI model with the following sentence:

[0983] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[0984] The input is analyzed. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted to suit the user's excitement level. Finally, the recommendation results are displayed on the client's device, allowing the user to choose the plan that best suits them.

[0985] In this way, the system of the present invention can quickly provide optimal products and services taking into account the user's needs and emotional state.

[0986] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0987] Step 1:

[0988] The user enters a request

[0989] The user uses the client device to enter their request into a text box or voice input field, for example, by typing "I'm looking for a new mobile plan." Once this input is complete, the device temporarily stores the data.

[0990] Step 2:

[0991] The device sends the data to the server

[0992] The client device sends input data to the server in the form of an HTTP request. At the same time, the emotion analysis engine analyzes the user's emotional state and sends that information in the same request to the server. For example, the user's request and emotional information such as "excitement" are packaged in a data packet.

[0993] Step 3:

[0994] The server receives the data

[0995] The server receives the HTTP request sent from the client device. The received data includes the user's desired text and emotional state information. The server converts this data into the format required for analysis.

[0996] Step 4:

[0997] The server generates a prompt for parsing.

[0998] The server converts the received data into prompts to be fed into the generative AI model. For example, based on the request "I'm looking for a new mobile plan" and the emotional state "excited," it generates the following prompt:

[0999] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[1000] Step 5:

[1001] The server analyzes the data using a generative AI model

[1002] The server inputs the generated prompt into a generative AI model, which analyzes it based on the customer's request and sentiment. The generative AI model processes the prompt and outputs a list of related products and services.

[1003] Step 6:

[1004] The server uses sentiment analysis techniques to adjust the results.

[1005] The server uses sentiment analysis to tailor the results from the generative AI model to the user's emotional state, highlighting, for example, special offers and discounts to excited users.

[1006] Step 7:

[1007] The server cleans and formats the search results

[1008] The server then formats the tailored product or service information into an easy-to-read format, adding design and wording that matches the user's excitement level.

[1009] Step 8:

[1010] The server sends the results to the device

[1011] The formatted recommendation results are sent to the client device as an HTTP response, including details of, for example, "value data plans."

[1012] Step 9:

[1013] Your device will display recommended results

[1014] The client device displays the recommendation results received from the server to the user, providing information to help the user make the best choice, such as details such as "value data plans" and "family discount plans."

[1015] In this way, it becomes possible to recommend optimal products and services taking into account the user's needs and emotions.

[1016] (Application example 2)

[1017] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1018] There is a demand for systems that can analyze users' needs and emotions and, based on that, quickly and accurately recommend the most appropriate products and services. Furthermore, it is important that the recommendation results are presented to users in an intuitively easy-to-understand format. However, conventional systems cannot fully meet these requirements, and improving the user experience is a challenge.

[1019] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1020] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests and the user's emotions, a matching means for identifying relevant products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a means for the provision means to display recommendation results using a head-mounted display, smart glasses, or a smartphone. This not only enables optimal products and services to be recommended based on the user's requests and emotions, but also makes it possible to present the recommendation results visually and intuitively.

[1021] "User" refers to a person who uses this system to input requests and receive recommendations for products and services.

[1022] "Requests" refer to requests or wishes regarding products or services that users desire from the system.

[1023] "Input means" refers to a means for providing an interface for users to input requests into the system, and includes text input and voice input.

[1024] "Analysis means" refers to the algorithms and engines used to analyze the requests and emotions received from users.

[1025] "Emotion" refers to the mental state or sensations experienced when a user inputs a request.

[1026] The "matching means" refers to a means for identifying relevant products and services based on the results of the desires and emotions analyzed by the analysis means.

[1027] "Means for providing" refers to the means for providing identified products or services to users, including displaying recommendation results using a head-mounted display, smart glasses, or a smartphone.

[1028] A "head-mounted display" is a display device worn by a user on the head, and is a means for displaying visual information.

[1029] "Smart glasses" refers to a device in the shape of glasses that displays information and has interactive functions.

[1030] A "smartphone" is a type of mobile phone, a digital device equipped with internet connectivity and a variety of applications.

[1031] "Recommendation Results" refers to information about products and services identified based on an analysis of needs and sentiment.

[1032] "Generative AI model" refers to an artificial intelligence model that analyzes user needs and emotions to identify the most suitable products and services.

[1033] The present invention is a system that analyzes user requests and emotions and recommends optimal products and services based on the results. This system is mainly composed of an input means, an analysis means, a matching means, and a provision means.

[1034] User request input

[1035] The user uses an input device to input their request into the system by text or voice. This input device works in conjunction with a smartphone or a robot in a virtual store. For example, imagine a user says to the robot, "I'm looking for a new smartphone."

[1036] Sending data

[1037] The client device (here, a robot in a virtual store) sends the data entered by the user to the server. This is done using HTTP requests, WebSocket communication, etc. At the same time, the emotion engine analyzes the user's emotions and sends this information to the server.

[1038] Receiving and analyzing data

[1039] The server receives user input data and emotion data sent by the client. After receiving the data, the server uses the generative AI model to analyze the request and emotion. For example, the request for a "new smartphone" and emotions such as "excitement" and "anticipation" are analyzed.

[1040] Matching products and services

[1041] The server then searches the database for relevant products and services based on the analysis results. The user's emotional data is also taken into account in this search. For example, different products and services may be recommended if the user is feeling "excited" or "anxious."

[1042] Recommendation result format

[1043] The server then formats the results in an easy-to-view format to offer the identified products and services to the user. The formatted results include product details adapted to the user's emotional state. For example, a "smartphone with the latest camera features" might be recommended to a user in an "expecting" state.

[1044] Sending recommendations

[1045] The server sends the formatted recommendation results to the client's terminal, which then displays the recommended products and services to the user.

[1046] Displaying recommendation results

[1047] The client device then displays the received recommendations to the user, who can view them visually using a head-mounted display, smart glasses, or smartphone, along with additional product reviews and purchase links.

[1048] Specific examples

[1049] For example, if a user looking for a new smartphone uses the system:

[1050] Example prompt: "I'm looking for a new smartphone," say the robot.

[1051] Example response: Robot: "Here's the perfect smartphone for you! It's the latest model with a great camera!"

[1052] In this way, the system of the present invention can quickly and accurately provide optimal products and services taking into account the user's needs and emotions, which is expected to improve the user experience.

[1053] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1054] Step 1:

[1055] The user inputs their request to the robot or smartphone in the virtual store. The input method is text input or voice input. At this stage, the user inputs a specific request such as "I'm looking for a new smartphone." The input data is saved in text format. The input amount is expressed as follows:

[1056] Input: User's request text "Looking for a new smartphone"

[1057] Output: Text data "Looking for a new smartphone"

[1058] Step 2:

[1059] The input means sends the user's request text to the analysis means. At the same time, the emotion engine analyzes the user's emotions from their voice and facial expression data and sends the results to the server. The emotion data is expressed in the form of, for example, "excitement" or "expectation." The input data is sent together with the emotion data.

[1060] Input: User's voice data, facial expression data

[1061] Output: Emotion data "Excitement"

[1062] Step 3:

[1063] The server analyzes the user's request text and emotional data received from the client device. It uses a generative AI model to analyze the request content and emotions and extracts the features necessary for specific product recommendations. The request text and emotional data are then input into the analysis model to obtain the analysis results.

[1064] Input: Text data "Looking for a new smartphone", Emotion data "Excitement"

[1065] Output: Analysis results (request "smartphone", emotion "excitement")

[1066] Step 4:

[1067] Based on the analysis results, the server searches the database for related products and services. Here, it generates a search query to identify the most suitable products, taking into account the user's needs and emotions, and searches the database. It creates a list of results and selects them as recommendation candidates.

[1068] Input: Analysis results (request "smartphone", emotion "excitement")

[1069] Output: Product list (e.g., "Latest smartphone model A")

[1070] Step 5:

[1071] The server formats the identified product and service information in a way that is appropriate for the user, shapes the display of the recommendation results to correspond to the user's emotions, and prepares the output as a visually appealing interface.

[1072] Input: Product list (e.g., "Latest Smartphone Model A")

[1073] Output: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[1074] Step 6:

[1075] The server sends the formatted recommendation results to the client device, which can then display details of the recommended products or services to the user. The sending method is HTTP responses or WebSocket messages.

[1076] Input: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[1077] Output: Recommendation result data to client terminal

[1078] Step 7:

[1079] The client device then displays the received recommendations to the user, who can visually confirm the recommended products and services using a head-mounted display, smart glasses, or smartphone, along with additional information such as product reviews and purchase links.

[1080] Input: Recommendation result data

[1081] Output: Visual recommendation results displayed to the user

[1082] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1084] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1085] [Fourth embodiment]

[1086] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1087] 7, a 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.

[1088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1089] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1090] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1093] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1094] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1095] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1097] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1098] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1099] The present invention is a system that enables a user to easily find products and services that best meet his or her needs. The following describes in detail the embodiments of the present invention.

[1100] This system mainly consists of a client terminal, a server, and a user. The client terminal inputs the user's requests and sends them to the server. The server analyzes the received requests, identifies relevant products and services, and recommends them.

[1101] User request input

[1102] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[1103] Sending data

[1104] The client device sends the data entered by the user to the server using an HTTP request or similar.

[1105] Receiving and analyzing data

[1106] The server receives the user's request data sent from the client device. After receiving it, the server analyzes the received data using a generative AI model. Through this analysis, the request is recognized as a "new mobile plan."

[1107] Matching products and services

[1108] The server then identifies relevant products and services based on the analysis, searching its database to find multiple plans and services that best fit the user's needs.

[1109] Providing recommendation results

[1110] The server then formats the identified products and services into an appropriate format and sends it to the client device. This format is designed to be easy for the user to understand. For example, it is displayed as a "great value data plan" or "family discount plan."

[1111] Displaying recommendation results

[1112] Finally, the client device displays the recommendation results received from the server to the user, who can then select the products and services that best suit their needs.

[1113] Specific examples

[1114] As a concrete example, consider the case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. The entered data is sent to the server and analyzed by the generative AI model. Based on the analysis results, "value data plans" and "family discount plans" are identified and sent from the server to the client device. The client device displays these results to the user, allowing the user to select an appropriate plan.

[1115] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[1116] The processing flow will be explained below.

[1117] Step 1:

[1118] User request input

[1119] The user enters their request using a text box or voice input field on the client device. For example, the user enters "I'm looking for a new mobile plan."

[1120] Step 2:

[1121] Sending data

[1122] The client device sends the data entered by the user to the server using a communication method such as an HTTP request, thereby passing the user's request to the server.

[1123] Step 3:

[1124] Receiving data

[1125] The server receives the user's requested data sent from the client terminal, and passes the received data to an internal processing engine.

[1126] Step 4:

[1127] Data analysis

[1128] The server passes the received request data to the generative AI model for analysis. The generative AI model classifies the user's request into appropriate categories and extracts specific needs. For example, from the request for "a new mobile plan," the "mobile plan" category is recognized.

[1129] Step 5:

[1130] Identifying related products and services

[1131] The server then searches the database for relevant products and services based on the analyzed data. This search identifies multiple options based on the user's needs, such as "great data plans" or "family discount plans."

[1132] Step 6:

[1133] Recommendation result format

[1134] The server formats the results in a user-friendly format to provide the identified products and services to the user, for example, a list containing multiple plan names and brief descriptions.

[1135] Step 7:

[1136] Sending recommendations

[1137] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[1138] Step 8:

[1139] Displaying the results

[1140] The client device displays the recommendation results received from the server to the user, including details such as "value data plans" and "family discount plans."

[1141] This series of steps allows users to easily find the products and services that best suit their needs.

[1142] Example 1

[1143] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1144] In today's market, users often have difficulty finding the products and services that best suit their needs. In particular, users often become confused due to the lack of an efficient way to find the best product among the many options. Therefore, there is a need for a system that allows users to easily and quickly find products and services that meet their needs.

[1145] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1146] In this invention, the server includes input means for receiving requests from users, transmission means for transmitting the requests to the server, analysis means for analyzing the requests, matching means for identifying related products and services based on the analysis results, provision means for providing the identified products and services to the user, and display means for displaying the provided products and services on the user terminal, thereby enabling users to quickly and easily find products and services that best suit their requests.

[1147] "Input means" refers to an interface or device for receiving requests from a user.

[1148] "Transmission means" refers to the protocol or function for transmitting the received request to the server.

[1149] "Analysis means" refers to the technology or model used to analyze the request data received on the server side and understand the content of the request.

[1150] "Matching means" refers to algorithms and database search functions that identify related products and services based on the analysis results.

[1151] "Means of provision" refers to the method or mechanism for providing information about specified products or services to users.

[1152] "Display means" refers to the technology or interface used to display information about the products or services provided on the user's device.

[1153] "Generative AI models" refer to artificial intelligence techniques used to analyze user requests, often utilizing natural language processing models.

[1154] The present invention relates to a system that allows users to easily find products and services that best suit their needs. This system is mainly composed of a client terminal, a server, and a user.

[1155] Hardware and software used

[1156] Client devices are personal computers (PCs) or smartphones. The client devices function as an input means for inputting user requests. The server is the central hardware for receiving, analyzing, processing, and providing data, and runs various software components. A generative AI model (e.g., GPT-3) is used for analysis. A database (e.g., an SQL database or NoSQL database) is used to store information about products and services.

[1157] System Operation

[1158] First, the user enters their request through a text box or voice input field on the client device. For example, the user may enter "I'm looking for a new mobile plan."

[1159] The client device then sends this request data to the server using an HTTP POST request or similar, and the data is transferred to the server in JSON format or similar.

[1160] The server analyzes the received data using an analytical means. This analysis is performed using a generative AI model. The generative AI model uses natural language processing technology to understand the received data and recognize the request, for example, that the user is looking for a "new mobile plan."

[1161] Once the analysis is complete, the server uses matching methods to search a database to identify relevant products and services, such as "great data plans" or "family discount plans."

[1162] The server then formats the identified products and services into JSON format or similar and provides them to the client terminal, which receives this data, converts it into a visually easy-to-understand display format, and presents it to the user.

[1163] Specific examples

[1164] For example, consider a user looking for a new mobile plan using this system. When the user types "I'm looking for a new mobile plan" into the client device, the data is sent to the server in JSON format. The server analyzes this data using a generative AI model and recognizes the request for a "new mobile plan." It then searches for relevant plans, identifying options such as "value data plans" and "family discount plans," and sends them in JSON format to the client device. Finally, the client device displays this information to the user, who can choose the option that best suits them.

[1165] Prompt Sentence Examples

[1166] For example, the following prompts are given to a generative AI model:

[1167] "I'm looking for a new mobile plan. Which plan is best for me?"

[1168] In this way, the system of the present invention allows users to quickly and easily find the products and services that best suit their needs.

[1169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1170] Step 1:

[1171] The user uses the client terminal to input his / her request. The input is made through a text box or a voice input field. For example, the user may input "I'm looking for a new mobile plan." The input data is in text format.

[1172] Step 2:

[1173] The client terminal converts the data entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is in text format, but is converted into JSON format when sent to the server.

[1174] Step 3:

[1175] The server receives JSON data sent from the client device. The received data first goes through a data validation process to ensure it is in the correct format. Here, the input is JSON data, and the output is the validated data.

[1176] Step 4:

[1177] The server uses a generative AI model to analyze the received data. The analysis automatically understands the user's request and recognizes, for example, that they are looking for a "new mobile plan." The input is the verified data, and the output is the analysis result of the request.

[1178] Step 5:

[1179] The server searches the database based on the analysis results to identify related products and services. In this case, products and services such as "value data plans" and "family discount plans" are extracted from the database. The input is the analysis results of the request, and the output is a list of identified products and services.

[1180] Step 6:

[1181] The server formats the information on the identified products and services in JSON format and provides it to the client terminal. The input is a list of products and services, and the output is JSON format data.

[1182] Step 7:

[1183] The client device parses the JSON data received from the server and displays it visually to the user. For example, it may be presented as part of a web page or application, containing detailed product or service information and links. The input is the JSON data sent from the server, and the output is a list of products or services that the user can view on the screen.

[1184] In this way, each processing step realizes a series of steps from receiving a user request to identifying related products and services and providing them to the user.

[1185] (Application example 1)

[1186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1187] In today's information-saturated society, it is difficult for users to easily find the products and services that best meet their needs. As a result, users have to spend a lot of time and effort trying to find the right product or service from the vast number of options. Furthermore, existing systems require a complicated process from when users enter their needs to when the optimal product or service is recommended, which can result in a poor user experience.

[1188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1189] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying the identified products and services on the smartphone, thereby enabling the user to easily and quickly find products and services that best match their requests.

[1190] "Input means" refers to a means provided for receiving requests from a user, and includes text input and voice input.

[1191] "Analysis means" means a means used to analyze requests received from users, and analyzes requests using a generative AI model.

[1192] The "matching means" is a means used to identify related products and services based on the analysis results obtained by the analysis means.

[1193] "Means of provision" refers to the means established to provide specified products or services to users.

[1194] The "display means" is a means for providing a visual interface for displaying identified products and services on a smartphone.

[1195] A "database" is a storage device for storing information on multiple products, and is used to search for and list products and services.

[1196] A "generative AI model" is an artificial intelligence model used to analyze a user's natural language requests, for example, using generative AI.

[1197] overview

[1198] This invention is a system that allows users to easily find products and services that best suit their needs. The system includes an input means for receiving requests from users, an analysis means for analyzing the requests, a matching means for identifying related products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a display means for displaying them on a smartphone.

[1199] System Configuration

[1200] Input Method

[1201] Users can provide their requests to the system through text input or voice input, which is made possible by using a smartphone application.

[1202] Analysis means

[1203] The server analyzes the requests received from users using a generative AI model, such as an advanced natural language processing model like OpenAI's GPT-3.

[1204] Matching Method

[1205] The server searches a database based on the analysis results obtained by the analysis means to identify related products and services. This database stores, for example, a number of communication plans and product information.

[1206] Means of provision and display

[1207] The identified products and services are sent from the server to the user's smartphone and displayed visually, allowing the user to select the most appropriate product or service.

[1208] Specific processing flow

[1209] User request input

[1210] The user opens the application on their smartphone and types "I'm looking for a new laptop" into the input field. Voice input is also possible.

[1211] Data transmission and analysis

[1212] The smartphone sends the input data to the server using an HTTP request, and the server uses a generative AI model to analyze the data and understand the user's request.

[1213] Product identification and recommendations

[1214] The server then searches the database based on the analysis results to identify relevant products, such as "high-performance laptops" or "laptops at great prices."

[1215] Displaying recommendation results

[1216] The server sends the compressed results back to the smartphone, which displays the results to the user using the smartphone's display means.

[1217] Hardware and software used

[1218] Hardware: Smartphone (e.g. iPhone, Android)

[1219] Software: Python, requests library, JSON, generative AI model (e.g., OpenAI GPT-3)

[1220] Examples and prompts

[1221] Specific examples

[1222] When a user types, "I'm looking for a new laptop," the system recommends the most suitable laptop, using a generative AI model to analyze the natural language request, identify relevant products from a database, and display them on the smartphone.

[1223] Prompt Sentence Examples

[1224] A user types, "I'm looking for a new laptop."

[1225] Please recommend the most suitable product from the options below:

[1226] 1. High-performance laptop

[1227] 2. Great value laptops

[1228] 3. Lightweight model

[1229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1230] Step 1:

[1231] The user opens the application on their smartphone and enters their request into an input field, for example, "I'm looking for a new laptop," either by typing it in text or by using voice input to communicate their request to the system. The data obtained from this input field becomes the initial data.

[1232] Input: User's requested text or voice data

[1233] Output: User request data on the client terminal

[1234] Specific behavior: A smartphone application provides a text box or voice input interface to allow users to input requests in natural language.

[1235] Step 2:

[1236] The client terminal sends the data entered by the user to the server as an HTTP request, formatted in JSON.

[1237] Input: User request data on the client device

[1238] Output: JSON data sent in the HTTP request

[1239] What happens: The smartphone application uses the requests library to send an HTTP POST request to the server, which includes the user's input data.

[1240] Step 3:

[1241] The server receives the user's request data sent from the client device, then inputs the data into the generative AI model and analyzes the request.

[1242] Input: JSON data sent from the client terminal

[1243] Output: Parsed request data

[1244] How it works: The server uses a generative AI model, such as OpenAI's GPT-3, to parse the natural language request it receives. For example, the request might be "I'm looking for a new laptop."

[1245] Step 4:

[1246] Based on the analysis results, the server searches the database to identify related products and services. Multiple matching candidates are found and information about each is retrieved.

[1247] Input: Parsed request data

[1248] Output: Multiple identified product information

[1249] Specific operation: The server searches a database (e.g., MySQL or SQLite) based on the analysis results and identifies related products such as "high-performance laptops" and "laptops at great prices."

[1250] Step 5:

[1251] The server generates a list of recommendations for the identified products and services to offer to the user and sends it back to the client device in JSON format.

[1252] Input: Multiple identified product information

[1253] Output: JSON data of the recommendation list

[1254] Specific operation: The server formats the data of the identified products, generates a recommendation list, converts it into JSON format data, and sends it to the client device.

[1255] Step 6:

[1256] The client terminal visually displays the received recommendation list to the user, who can then select the product that best meets their needs based on the displayed information.

[1257] Input: JSON data of the recommendation list received from the server

[1258] Output: Product list displayed on the client device

[1259] Specific operation: The smartphone application parses the received JSON data and displays a list of recommended products in the user interface. The user can then select from the displayed list of products.

[1260] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1261] The present invention is a system for analyzing a user's needs and emotions and recommending optimal products and services. Hereinafter, an embodiment of the present invention will be specifically described.

[1262] This system mainly consists of a client device, a server, a user, and an emotion engine. The client device inputs the user's requests and sends them to the server. The server analyzes the received requests and emotions, and identifies and recommends related products and services. The emotion engine recognizes the user's emotions and uses this emotional information for analysis.

[1263] User request input

[1264] A user can input their request into the system using a client device. This input is done through an interface such as a text box or voice input field. For example, a user might input "I'm looking for a new mobile plan."

[1265] Sending data

[1266] The client device sends the data entered by the user to the server using an HTTP request. At this stage, the emotion engine also simultaneously analyzes the user's emotions and sends that information to the server.

[1267] Receiving and analyzing data

[1268] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server uses a generative AI model and emotion engine to analyze the request and emotion. This analysis identifies the content of the request and the user's emotion. For example, the request for a "new mobile plan" and the emotion of "excitement" or "anxiety" are analyzed.

[1269] Matching products and services

[1270] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account the user's emotional data. For example, the recommended products and services may differ depending on whether the user is "excited" or "anxious."

[1271] Recommendation result format

[1272] The server formats the results to present the identified products and services to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, a "great data plan" might be especially highlighted to a user in an "excited" state.

[1273] Sending recommendations

[1274] The server transmits the formatted recommendation results to the client terminal, which can then display the recommended products and services to the user.

[1275] Displaying recommendation results

[1276] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[1277] Specific examples

[1278] Consider a case where a user looking for a new mobile plan uses this system. The user enters "I'm looking for a new mobile plan" into the client device. At the same time, the emotion engine recognizes the user's "excitement" state. The entered data and emotion data are sent to the server and analyzed by the generative AI model and emotion engine. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted in a format appropriate for the user's excitement state. Finally, the recommendation results are displayed on the client device, allowing the user to choose the plan that best suits them.

[1279] In this way, the system of the present invention makes it possible to quickly provide optimal products and services taking into consideration the user's needs and emotions.

[1280] The processing flow will be explained below.

[1281] Step 1:

[1282] User request input

[1283] The user enters their request using a text box or voice input field on the client device, for example, "I'm looking for a new mobile plan."

[1284] Step 2:

[1285] Sending data

[1286] The client device sends the request data entered by the user to the server using a communication method such as an HTTP request. The emotion engine also analyzes the user's emotions, and this emotion data is also sent to the server at the same time.

[1287] Step 3:

[1288] Receiving data

[1289] The server receives the user's request data and emotion data sent from the client's terminal, and passes the received data to an internal processing engine.

[1290] Step 4:

[1291] Data analysis

[1292] The server passes the received request data to the generative AI model for analysis. At the same time, the emotion engine analyzes the received emotion data. The generative AI model classifies the user's request into an appropriate category, and the emotion engine recognizes the user's emotional state (e.g., "excited" or "anxious").

[1293] Step 5:

[1294] Reflecting emotional data

[1295] The server integrates the analyzed request data with the emotional data and adjusts the recommended products and services based on the user's emotional state. For example, if the user is feeling anxious, it will recommend services that will make them feel more secure.

[1296] Step 6:

[1297] Identifying related products and services

[1298] The server then searches the database for relevant products and services based on the combined data, identifying the most suitable options based on the analysis results.

[1299] Step 7:

[1300] Recommendation result format

[1301] The server formats the results in an easy-to-view format to provide the identified products and services to the user, including product details that are sensitive to the user's emotional state.

[1302] Step 8:

[1303] Sending recommendations

[1304] The server sends the formatted recommendation results to the client's device, which prepares them for display to the user.

[1305] Step 9:

[1306] Displaying the results

[1307] The client device receives the recommendation results from the server and displays them to the user, including details that correspond to the user's emotional state, such as "value data plans" and "family discount plans."

[1308] This series of steps makes it easy for users to find the products and services that best suit their needs and emotions.

[1309] Example 2

[1310] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1311] Conventional recommendation systems simply analyze users' requests and provide relevant products and services. However, because these systems do not take into account the user's emotional state, they often fail to recommend products and services that are truly optimal for the user. Furthermore, if the input request is interpreted subtly differently depending on the user's emotions, analysis results that do not take this into account will not meet the user's expectations. For this reason, there is a need for a system that can analyze requests and recommend products and services based on the user's emotional state.

[1312] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for receiving a request from a user, an analysis means for analyzing the request, the analysis means including an emotion analysis means for analyzing the user's emotion, a matching means for identifying related products and services based on the analysis results, and a provision means for providing the identified products and services to the user. This makes it possible to analyze the request taking into consideration the user's emotional state and to recommend optimal products and services based on the results.

[1313] "Input means" refers to a method or device for receiving requests from a user, including text input and voice input.

[1314] The "analysis means" is a method or device for analyzing requests received from users.

[1315] "Emotion analysis means" refers to a method or device for analyzing the emotional state of a user when analyzing the user's needs.

[1316] The "matching means" is a method or device for identifying related products or services based on the results obtained by the analysis means.

[1317] "Provision means" refers to a method or device for providing a specified product or service to a user.

[1318] "Generative AI model" refers to an artificial intelligence model used to analyze user requests.

[1319] A "prompt" is a sentence to be input into a generative AI model, and is a document that contains a specific request or emotion.

[1320] This invention is a system that analyzes user needs and emotions and recommends optimal products and services. This system is mainly composed of a client terminal, a server, users, and an emotion analysis engine.

[1321] User request input

[1322] A user uses a client device to input their request into the system. This input is done through an interface such as a text box or voice input field. For example, a user might input, "I'm looking for a new mobile plan."

[1323] Sending data

[1324] The client device sends the data entered by the user to the server using an HTTP request or similar. At the same time, the emotion analysis engine also analyzes the user's emotional state and sends that information to the server. At this stage, the user's request and emotional information are combined into a single data packet.

[1325] Receiving and analyzing data

[1326] The server receives the user's request data and emotion data sent from the client device. After receiving the data, the server analyzes the data using the generative AI model and emotion analysis engine. Specifically, it generates a prompt sentence to input the data into the generative AI model. For example, if the request is "Looking for a new mobile plan" and the emotion is "Excited," the server inputs the following prompt sentence into the generative AI model:

[1327] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[1328] A generative AI model analyzes this prompt and generates a list of related products and services.

[1329] Matching products and services

[1330] The server then searches the database for relevant products and services based on the analysis results. This search also takes into account emotional data. For example, different products and services may be recommended if the user is "excited" or "anxious."

[1331] Recommendation result format

[1332] The server then formats the identified products and services to present to the user in an easy-to-view format. The formatted results include product details adapted to the user's emotional state. For example, "great data plans" might be especially emphasized for an excited user.

[1333] Sending recommendations

[1334] The server sends the formatted recommendation results to the client device as an HTTP response, allowing the client device to display the recommended products and services to the user.

[1335] Displaying recommendation results

[1336] Finally, the client device displays the recommendation results received from the server to the user. The displayed information includes details such as "value data plans" and "family discount plans." Based on this information, the user can select the most suitable products and services.

[1337] Specific examples

[1338] Consider a case where a user looking for a new mobile plan uses this system. The user types "I'm looking for a new mobile plan" into the client device, and the emotion analysis engine recognizes the "excited" state. This data is sent to the server, which prompts the generative AI model with the following sentence:

[1339] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[1340] The input is analyzed. Based on the analysis results, "value data plans" and "family discount plans" are identified and formatted to suit the user's excitement level. Finally, the recommendation results are displayed on the client's device, allowing the user to choose the plan that best suits them.

[1341] In this way, the system of the present invention can quickly provide optimal products and services taking into account the user's needs and emotional state.

[1342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1343] Step 1:

[1344] The user enters a request

[1345] The user uses the client device to enter their request into a text box or voice input field, for example, by typing "I'm looking for a new mobile plan." Once this input is complete, the device temporarily stores the data.

[1346] Step 2:

[1347] The device sends the data to the server

[1348] The client device sends input data to the server in the form of an HTTP request. At the same time, the emotion analysis engine analyzes the user's emotional state and sends that information in the same request to the server. For example, the user's request and emotional information such as "excitement" are packaged in a data packet.

[1349] Step 3:

[1350] The server receives the data

[1351] The server receives the HTTP request sent from the client device. The received data includes the user's desired text and emotional state information. The server converts this data into the format required for analysis.

[1352] Step 4:

[1353] The server generates a prompt for parsing.

[1354] The server converts the received data into prompts to be fed into the generative AI model. For example, based on the request "I'm looking for a new mobile plan" and the emotional state "excited," it generates the following prompt:

[1355] Suggest product or service recommendations to users who have a known desire to "look for a new mobile plan" and an emotion of "excitement."

[1356] Step 5:

[1357] The server analyzes the data using a generative AI model

[1358] The server inputs the generated prompt into a generative AI model, which analyzes it based on the customer's request and sentiment. The generative AI model processes the prompt and outputs a list of related products and services.

[1359] Step 6:

[1360] The server uses sentiment analysis techniques to adjust the results.

[1361] The server uses sentiment analysis to tailor the results from the generative AI model to the user's emotional state, highlighting, for example, special offers and discounts to excited users.

[1362] Step 7:

[1363] The server cleans and formats the search results

[1364] The server then formats the tailored product or service information into an easy-to-read format, adding design and wording that matches the user's excitement level.

[1365] Step 8:

[1366] The server sends the results to the device

[1367] The formatted recommendation results are sent to the client device as an HTTP response, including details of, for example, "value data plans."

[1368] Step 9:

[1369] Your device will display recommended results

[1370] The client device displays the recommendation results received from the server to the user, providing information to help the user make the best choice, such as details such as "value data plans" and "family discount plans."

[1371] In this way, it becomes possible to recommend optimal products and services taking into account the user's needs and emotions.

[1372] (Application example 2)

[1373] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1374] There is a demand for systems that can analyze users' needs and emotions and, based on that, quickly and accurately recommend the most appropriate products and services. Furthermore, it is important that the recommendation results are presented to users in an intuitively easy-to-understand format. However, conventional systems cannot fully meet these requirements, and improving the user experience is a challenge.

[1375] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1376] In this invention, the server includes an input means for receiving requests from a user, an analysis means for analyzing the requests and the user's emotions, a matching means for identifying relevant products and services based on the analysis results, a provision means for providing the identified products and services to the user, and a means for the provision means to display recommendation results using a head-mounted display, smart glasses, or a smartphone. This not only enables optimal products and services to be recommended based on the user's requests and emotions, but also makes it possible to present the recommendation results visually and intuitively.

[1377] "User" refers to a person who uses this system to input requests and receive recommendations for products and services.

[1378] "Requests" refer to requests or wishes regarding products or services that users desire from the system.

[1379] "Input means" refers to a means for providing an interface for users to input requests into the system, and includes text input and voice input.

[1380] "Analysis means" refers to the algorithms and engines used to analyze the requests and emotions received from users.

[1381] "Emotion" refers to the mental state or sensations experienced when a user inputs a request.

[1382] The "matching means" refers to a means for identifying relevant products and services based on the results of the desires and emotions analyzed by the analysis means.

[1383] "Means for providing" refers to the means for providing identified products or services to users, including displaying recommendation results using a head-mounted display, smart glasses, or a smartphone.

[1384] A "head-mounted display" is a display device worn by a user on the head, and is a means for displaying visual information.

[1385] "Smart glasses" refers to a device in the shape of glasses that displays information and has interactive functions.

[1386] A "smartphone" is a type of mobile phone, a digital device equipped with internet connectivity and a variety of applications.

[1387] "Recommendation Results" refers to information about products and services identified based on an analysis of needs and sentiment.

[1388] "Generative AI model" refers to an artificial intelligence model that analyzes user needs and emotions to identify the most suitable products and services.

[1389] The present invention is a system that analyzes user requests and emotions and recommends optimal products and services based on the results. This system is mainly composed of an input means, an analysis means, a matching means, and a provision means.

[1390] User request input

[1391] The user uses an input device to input their request into the system by text or voice. This input device works in conjunction with a smartphone or a robot in a virtual store. For example, imagine a user says to the robot, "I'm looking for a new smartphone."

[1392] Sending data

[1393] The client device (here, a robot in a virtual store) sends the data entered by the user to the server. This is done using HTTP requests, WebSocket communication, etc. At the same time, the emotion engine analyzes the user's emotions and sends this information to the server.

[1394] Receiving and analyzing data

[1395] The server receives user input data and emotion data sent by the client. After receiving the data, the server uses the generative AI model to analyze the request and emotion. For example, the request for a "new smartphone" and emotions such as "excitement" and "anticipation" are analyzed.

[1396] Matching products and services

[1397] The server then searches the database for relevant products and services based on the analysis results. The user's emotional data is also taken into account in this search. For example, different products and services may be recommended if the user is feeling "excited" or "anxious."

[1398] Recommendation result format

[1399] The server then formats the results in an easy-to-view format to offer the identified products and services to the user. The formatted results include product details adapted to the user's emotional state. For example, a "smartphone with the latest camera features" might be recommended to a user in an "expecting" state.

[1400] Sending recommendations

[1401] The server sends the formatted recommendation results to the client's terminal, which then displays the recommended products and services to the user.

[1402] Displaying recommendation results

[1403] The client device then displays the received recommendations to the user, who can view them visually using a head-mounted display, smart glasses, or smartphone, along with additional product reviews and purchase links.

[1404] Specific examples

[1405] For example, if a user looking for a new smartphone uses the system:

[1406] Example prompt: "I'm looking for a new smartphone," say the robot.

[1407] Example response: Robot: "Here's the perfect smartphone for you! It's the latest model with a great camera!"

[1408] In this way, the system of the present invention can quickly and accurately provide optimal products and services taking into account the user's needs and emotions, which is expected to improve the user experience.

[1409] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1410] Step 1:

[1411] The user inputs their request to the robot or smartphone in the virtual store. The input method is text input or voice input. At this stage, the user inputs a specific request such as "I'm looking for a new smartphone." The input data is saved in text format. The input amount is expressed as follows:

[1412] Input: User's request text "Looking for a new smartphone"

[1413] Output: Text data "Looking for a new smartphone"

[1414] Step 2:

[1415] The input means sends the user's request text to the analysis means. At the same time, the emotion engine analyzes the user's emotions from their voice and facial expression data and sends the results to the server. The emotion data is expressed in the form of, for example, "excitement" or "expectation." The input data is sent together with the emotion data.

[1416] Input: User's voice data, facial expression data

[1417] Output: Emotion data "Excitement"

[1418] Step 3:

[1419] The server analyzes the user's request text and emotional data received from the client device. It uses a generative AI model to analyze the request content and emotions and extracts the features necessary for specific product recommendations. The request text and emotional data are then input into the analysis model to obtain the analysis results.

[1420] Input: Text data "Looking for a new smartphone", Emotion data "Excitement"

[1421] Output: Analysis results (request "smartphone", emotion "excitement")

[1422] Step 4:

[1423] Based on the analysis results, the server searches the database for related products and services. Here, it generates a search query to identify the most suitable products, taking into account the user's needs and emotions, and searches the database. It creates a list of results and selects them as recommendation candidates.

[1424] Input: Analysis results (request "smartphone", emotion "excitement")

[1425] Output: Product list (e.g., "Latest smartphone model A")

[1426] Step 5:

[1427] The server formats the identified product and service information in a way that is appropriate for the user, shapes the display of the recommendation results to correspond to the user's emotions, and prepares the output as a visually appealing interface.

[1428] Input: Product list (e.g., "Latest Smartphone Model A")

[1429] Output: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[1430] Step 6:

[1431] The server sends the formatted recommendation results to the client device, which can then display details of the recommended products or services to the user. The sending method is HTTP responses or WebSocket messages.

[1432] Input: Formatted recommendation results (e.g., "The latest smartphone with a great camera")

[1433] Output: Recommendation result data to client terminal

[1434] Step 7:

[1435] The client device then displays the received recommendations to the user, who can visually confirm the recommended products and services using a head-mounted display, smart glasses, or smartphone, along with additional information such as product reviews and purchase links.

[1436] Input: Recommendation result data

[1437] Output: Visual recommendation results displayed to the user

[1438] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1439] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1440] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1441] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1442] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1443] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1444] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1445] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1446] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1447] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1448] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1449] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1450] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1451] 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.

[1452] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1453] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1454] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1455] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1456] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1457] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1458] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1459] The following is further disclosed regarding the above embodiment.

[1460] (Claim 1)

[1461] an input means for receiving requests from a user;

[1462] analysis means for analyzing the request;

[1463] a matching means for identifying related products and services based on the analysis results;

[1464] A means for providing the identified goods and services to users;

[1465] A system including:

[1466] (Claim 2)

[1467] 2. The system of claim 1, wherein the input means includes text input and voice input.

[1468] (Claim 3)

[1469] 2. The system of claim 1, wherein the analysis means analyzes the request using a generative AI model.

[1470] "Example 1"

[1471] (Claim 1)

[1472] an input means for receiving requests from a user;

[1473] sending means for sending the request to a server;

[1474] analysis means for analyzing the request;

[1475] a matching means for identifying related products and services based on the analysis results;

[1476] A means for providing the identified goods and services to users;

[1477] a display means for displaying the provided goods and services on a user terminal;

[1478] A system including:

[1479] (Claim 2)

[1480] 2. The system of claim 1, wherein the input means includes text input and voice input.

[1481] (Claim 3)

[1482] 2. The system of claim 1, wherein the analysis means analyzes the request using a generative AI model.

[1483] "Application Example 1"

[1484] (Claim 1)

[1485] an input means for receiving requests from a user;

[1486] analysis means for analyzing the request;

[1487] a matching means for identifying related products and services based on the analysis results;

[1488] A means for providing the identified goods and services to users;

[1489] a display means for displaying the identified products and services on a smartphone;

[1490] A system including:

[1491] (Claim 2)

[1492] 2. The system of claim 1, wherein the input means includes text input and voice input.

[1493] (Claim 3)

[1494] 2. The system of claim 1, wherein the analysis means analyzes the request using a generative AI model.

[1495] (Claim 4)

[1496] 2. The system according to claim 1, wherein the display means visually displays information about the recommended products through a user interface.

[1497] (Claim 5)

[1498] 2. The system according to claim 1, wherein the matching means searches a database to list related products.

[1499] (Claim 6)

[1500] 6. The system of claim 5, wherein the database includes a plurality of communication plans.

[1501] "Example 2: Combining Emotion Engines"

[1502] (Claim 1)

[1503] an input means for receiving requests from a user;

[1504] analysis means for analyzing the request;

[1505] the analysis means includes emotion analysis means for analyzing the emotion of the user,

[1506] a matching means for identifying related products and services based on the analysis results;

[1507] A means for providing the identified goods and services to users;

[1508] A system including:

[1509] (Claim 2)

[1510] 2. The system of claim 1, wherein the input means includes text input and voice input.

[1511] (Claim 3)

[1512] 2. The system of claim 1, wherein the analysis means analyzes the request using a generative AI model.

[1513] (Claim 4)

[1514] 2. The system according to claim 1, wherein the emotion analysis means analyzes the emotional state of the user at the time of input.

[1515] (Claim 5)

[1516] The matching means searches for products and services from a database using the analysis results,

[1517] 2. The system according to claim 1, wherein the system recommends products and services based on the results of the emotion analysis means.

[1518] (Claim 6)

[1519] 2. The system according to claim 1, wherein the providing means formats the recommendation results in a format suited to the emotional state of the user and transmits the format to the client terminal.

[1520] "Application example 2 when combining emotion engines"

[1521] (Claim 1)

[1522] an input means for receiving requests from a user;

[1523] analysis means for analyzing the requests and emotions of the user;

[1524] a matching means for identifying related products and services based on the analysis results;

[1525] A means for providing the identified goods and services to users;

[1526] The providing means displays the recommendation results using a head-mounted display, smart glasses, or a smartphone;

[1527] A system including:

[1528] (Claim 2)

[1529] 2. The system of claim 1, wherein the input means includes text input and voice input.

[1530] (Claim 3)

[1531] 2. The system of claim 1, wherein the analyzing means uses a generative AI model to analyze desires and emotions. [Explanation of symbols]

[1532] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for receiving requests from a user; analysis means for analyzing the request; a matching means for identifying related products and services based on the analysis results; A means for providing the identified goods and services to users; A system including:

2. 2. The system of claim 1, wherein said input means includes text input and voice input.

3. 2. The system of claim 1, wherein the analysis means analyzes the request using a generative AI model.

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