system

Abstract

We provide the system. [Solution] The information processing device includes means for searching a database based on conditions obtained from the user and selecting suitable product candidates, Through dialogue with users, we provide a means to narrow down the options from product candidates, A means of guiding the purchase process for the finally selected product, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When a user purchases a specific product, for example, a mattress, there is a problem that it takes time and effort to select the optimal one from various options. In particular, the process of selecting a product while considering various conditions such as brand, firmness, and price range is complicated. Therefore, there is a demand for providing a system that quickly and efficiently proposes an optimal product to the user and supports the process until purchase.

Means for Solving the Problems

[0005] This invention provides a system that uses an information processing device to search a database based on conditions obtained from the user and automatically selects suitable product candidates. Furthermore, by providing means to narrow down the options through interaction with the user and ultimately guide the user through the reservation and purchase of the selected product, the system reduces time and effort and supports optimal product selection.

[0006] An "information processing device" is an electronic device that receives information input from a user and performs database searches and processing based on that information.

[0007] "User" refers to an unspecified number of individuals who use a particular product or service and input their own criteria.

[0008] "Conditions" refer to information that users input into the information processing device as preferences or constraints regarding product selection, and these include budget, durability, brand, etc.

[0009] A "database" is a collection of information that stores information about various products, and is the target of retrieval by an information processing device.

[0010] "Product candidates" is a list of possible products selected from the database based on the entered criteria.

[0011] "Dialogue" refers to the exchange of information between a user and an information processing device, and is a process of narrowing down options based on the user's preferences.

[0012] "Selection" is the act of deciding on a target from among several options based on specific criteria.

[0013] "Reservation and purchase" refers to the procedure for securing and actually obtaining selected products. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] To implement this invention, a system is constructed that communicates between an information processing device and a user terminal, supporting the entire process from product selection to purchase. This allows users to quickly and efficiently select the optimal product through the communication terminal.

[0036] First, the user accesses the information processing device using a communication terminal. The user inputs product-related conditions, including budget and desired features (e.g., hardness, size, brand, etc.). The conditions entered by the user are transmitted to the server in real time.

[0037] The server searches a large database based on the received conditions and extracts candidate products that match those conditions. This search process evaluates multivariate attributes based on the conditions and lists possible options.

[0038] The server then sends a list of candidates to the user, who reviews it via their communication device. The candidate list includes basic characteristics of each product (price, firmness, brand name, etc.). The user can request more details about candidates they are interested in, and the server provides the details upon request.

[0039] Through dialogue, users can further narrow down their product selection, and the server responds to support the selection process. Once the user has finalized their selection, the server guides them through the specific procedures for reservation and purchase.

[0040] This type of system allows users to efficiently purchase the products they want while reducing the effort required for product selection.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user accesses the LINE official account using a communication terminal to connect to the information processing device. After accessing the account, they send a message indicating that they would like to choose a mattress.

[0044] Step 2:

[0045] The server receives a message from the user and activates a chatbot as an automated response system. It then asks the user questions to inquire about their purchase conditions, such as budget and desired firmness.

[0046] Step 3:

[0047] The user enters their conditions in response to a question from the server. For example, they might provide details such as, "My budget is 50,000 yen, and I prefer a solid build."

[0048] Step 4:

[0049] The server searches the database based on the conditions provided by the user. During this process, it considers factors such as budget, firmness, and brand to select product candidates that meet the criteria.

[0050] Step 5:

[0051] The server presents the user with multiple mattress options as search results. The information presented includes basic characteristics of each product, such as price, firmness, and brand name.

[0052] Step 6:

[0053] Users can view the list of candidates provided by the server on their communication terminal and inquire about products for which they require more detailed information.

[0054] Step 7:

[0055] The server responds to user requests for detailed information and provides more detailed information (materials, reviews, etc.) about the selected product.

[0056] Step 8:

[0057] Users can narrow down their product choices based on the detailed information provided. They can then continue interacting with the server to select their final purchase options.

[0058] Step 9:

[0059] The server confirms the user's final purchase intention and guides them through the reservation and purchase process for the selected items. It provides links to the purchase page and instructions for the necessary steps.

[0060] By going through this series of processes, users can efficiently select the product that best suits them and have a smooth purchasing experience from start to finish.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In modern online shopping, users often face the problem of spending a considerable amount of time and effort finding the perfect product from a vast selection. Furthermore, unintended products may be prioritized due to a lack of proper prioritization based on user input. Therefore, there is a need for support for an appropriate and efficient product selection and purchase process.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for searching a memory area based on conditions obtained from the user and selecting suitable object candidates; means for narrowing down the options from the object candidates through dialogue with the user; means for guiding the user through the purchase procedure of the finally selected object; and means for improving the priority of search results using a generative AI model. This enables the user to quickly and accurately find and purchase products that meet their needs.

[0066] An "information processing device" is a computer system that performs data input, processing, storage, and output.

[0067] "User" refers to an individual or organization that uses an information system to input information and receives the output information.

[0068] "Storage area" refers to an area using hardware or software to store data, and includes databases, etc.

[0069] A "potential target item" is a list of products or services that may potentially meet the conditions specified by the user.

[0070] "Communication device" refers to hardware or software that connects to an information processing device via a network and transmits and receives data.

[0071] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze data and derive the optimal solution for a specific task.

[0072] Prioritization is the process of ranking items based on their importance according to specific conditions or criteria.

[0073] "Means" refers to the methods, processes, or devices used to achieve a particular objective.

[0074] This invention is a system that uses a server as an information processing device and a terminal as a communication device, and supports the process of effectively selecting and purchasing the products and services that users desire.

[0075] Users access the server via the internet using devices such as smartphones, tablets, or personal computers. Users input product selection criteria (e.g., price range, product characteristics, brand name, etc.) through the device's interface. The entered criteria are transmitted from the device to the server in real time. HTTP or HTTPS protocols are used for this communication.

[0076] The server analyzes the received conditions and searches for relevant product information using a pre-built database system (e.g., MySQL®, PostgreSQL). During the search process, a generative AI model is used to evaluate the suitability of products based on the conditions and rank the results. This method makes it possible to generate a list of product candidates with appropriate priorities.

[0077] For example, if a user enters "firm single-size mattress available for under 20,000 yen" as their search criteria, the server extracts matching product candidates from its database and sends a list to the user's terminal. This list includes the price, rating, and brand name of each product.

[0078] Users can review the submitted product suggestions and view detailed information by selecting items they are interested in. Once a purchase is finalized, the server provides the user with information on payment methods, purchase procedures, and shipping options. This system allows users to quickly select the most suitable product and proceed smoothly through the purchase process.

[0079] The advantage of a search process using generative AI models is that it can dynamically adjust product priorities based on user input, thereby improving the accuracy of search results. Through this system, users can have a stress-free shopping experience.

[0080] As an example of a prompt, it can address specific needs such as, "Please recommend some firm, single-size mattresses within a budget of 20,000 yen."

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user accesses the system using a terminal. They input product selection criteria into the interface displayed on the terminal screen. These criteria may include, for example, "price range," "characteristics," and "brand." The entered data is sent from the terminal to the server. Based on the input data, the criteria are converted into a format suitable for database searching.

[0084] Step 2:

[0085] The server analyzes the conditional data received from the terminal. The analyzed conditions are constructed as a database query, and a search is performed against the product database. A generative AI model is used to evaluate and prioritize the characteristics of the product data according to the conditions. As a search result, a list of product candidates that match the conditions is generated.

[0086] Step 3:

[0087] The server formats the generated list of product candidates and sends it to the user's terminal. The formatted data is sent in JSON or XML format. The terminal parses the received data and displays it as a list on the user interface. The user can then review the product candidates through this list.

[0088] Step 4:

[0089] The user selects an item of interest from the displayed list of suggested products. The device sends the selection information to the server and requests detailed information about the selected item. The server retrieves the detailed data related to that item from its database and sends it back to the device. Based on the provided product details, the user can then compare and consider other products.

[0090] Step 5:

[0091] The server receives the user's decision and supports the purchase process for the ultimately selected product. The purchase process includes selecting a payment method and entering shipping information. Based on the purchase information, the server processes the order and sends a confirmation notification to the user. This allows the user to complete the purchase.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In traditional online purchasing systems, the process of users selecting the best product from a large number of candidates is cumbersome and time-consuming. Furthermore, the limited input methods make it a cumbersome experience for users. Additionally, if users have difficulty clearly defining their desired criteria, finding a suitable product becomes challenging.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for searching a database based on conditions obtained from the user, means for obtaining conditions using voice input and converting those conditions into text data, and means for narrowing down product candidates based on the obtained conditions using a generative AI model. This enables the user to select products simply and efficiently through voice input.

[0097] An "information processing device" is a device that has the function of searching a database based on conditions obtained from the user and selecting product candidates.

[0098] "Voice input" is a method of acquiring what a user says as digital data, and this data is used as a means of inputting conditions.

[0099] "Text data" refers to data obtained by converting information acquired through voice input into written text.

[0100] A "generative AI model" is an artificial intelligence model used to narrow down product candidates from a vast amount of data based on acquired conditions.

[0101] "Product candidates" refer to a group of products that may meet the user's criteria, from which the user can make a selection.

[0102] The invention will now be described in terms of embodiments for carrying out the invention. This invention is a system that connects an information processing device and a user terminal equipped with voice recognition functionality via a communication network to support the product selection process. The user specifies conditions by voice input, and the voice recognition software converts this into text data. This converted data is transmitted to the server via communication.

[0103] The server searches the database based on the input conditions and uses a generative AI model to narrow down product candidates. The generative AI model is an algorithm for selecting products that match the conditions and plays a role in efficiently narrowing down candidates from a large amount of data. This process uses speech recognition libraries such as Google® Speech-to-Text API, and recommendation engines such as Amazon Personalize are used to identify product candidates.

[0104] Furthermore, users can specify additional conditions by voice and engage in voice dialogue about product details. Through this dialogue, users can more easily narrow down their product selection and receive guidance on payment and delivery procedures for the final selected product. As a concrete example, a user might voice-input a prompt such as, "Please recommend a firm mattress under 20,000 yen," and the system would then recommend a group of products that match that condition.

[0105] In this way, users can efficiently select and purchase products through devices they use on a daily basis, using simpler and more convenient methods.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal receives voice input from the user and converts that input into text data using speech recognition software. The user specifies concrete conditions in this input. Since this converted text data is used in subsequent processing, conversion accuracy is crucial.

[0109] Step 2:

[0110] The terminal sends the converted text data to the server via the communication network. The transmitted data includes the user's specified criteria. The data received by the server is used for the subsequent database search process.

[0111] Step 3:

[0112] The server analyzes the user's criteria from the received text data and searches the database for relevant products. During this process, it evaluates each product in the database based on the criteria and extracts candidate products that meet those criteria. A fast and efficient search algorithm is used for data retrieval.

[0113] Step 4:

[0114] The server uses a generative AI model to narrow down the candidate products extracted. The AI ​​model includes a trained algorithm to determine the product that best suits the user's criteria, and selects the optimal product from the candidate products obtained through the database search.

[0115] Step 5:

[0116] The server sends the product candidates selected by the AI ​​model to the terminal. This output includes detailed information such as product name, price, and features. The user makes their final product selection based on this information.

[0117] Step 6:

[0118] Users check product details via voice or touch controls on their device and send their selection results to the server. Based on this, the server guides them through the final purchase process and provides payment and delivery options.

[0119] This series of processes allows users to efficiently and easily select products and complete the purchase process using voice input.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention is an information processing system designed to support users. By incorporating an emotion engine into the information processing device, it recognizes the user's emotions and provides more personalized services. This emotion engine identifies emotions through interaction with the user and uses that information to optimize product selection and recommendations.

[0122] First, the user accesses the information processing device using a communication terminal and inputs their criteria for product selection. At this time, the server sends data such as the text and tone of voice entered by the user to the emotion engine, which analyzes the user's emotional state. For example, if the user says "I want a firm mattress" in a cheerful voice, the emotion engine determines that the user has positive emotions.

[0123] The emotional information analyzed by the emotion engine is reflected in the server's search algorithm. Specifically, if a positive emotion is detected in the user, products that meet certain criteria are prioritized and presented among the product candidates. If the user expresses anxiety, the server supports the user by providing more reassuring information (e.g., reviews and detailed information about safety).

[0124] Users can select products on their communication terminals while reviewing the results of interactions via the emotion engine. When a user requests detailed information about a selected product, the server returns the information taking their emotional state into consideration. For example, if the emotion engine recognizes the user's curiosity, the latest information and trend data related to that product will also be presented.

[0125] In this process, when the user finally decides to make a purchase, the server guides them through the procedures for reserving and purchasing the selected items. This allows users to make appropriate product choices based on their emotions, enabling them to enjoy a high-quality purchasing experience.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user accesses the information processing device using a communication terminal and launches the product selection system via the LINE official account. The user then begins entering product requirements (e.g., budget, rigidity).

[0129] Step 2:

[0130] The server receives user input in real time and sends the text data to the emotion engine. The emotion engine analyzes the emotions from the input text and the input method (e.g., typing speed, tone of voice input).

[0131] Step 3:

[0132] The emotion engine identifies the user's emotions based on its analysis. For example, if the user shows signs of anxiety, the system prepares to incorporate that information into the algorithm.

[0133] Step 4:

[0134] The server selects product candidates by searching the database based on user conditions, while considering the emotional information provided by the emotion engine. Among the products that meet the conditions, it prioritizes listing those that are best suited to the user's emotional state.

[0135] Step 5:

[0136] The server presents the user with selected product candidates and displays basic details for each product (price, features, brand, etc.). Emotional prioritization is also considered here.

[0137] Step 6:

[0138] Users view a product list displayed on their communication device and request more detailed information about products that interest them. It's important to keep in mind that this request is also an emotionally influenced behavior.

[0139] Step 7:

[0140] The server presents relevant information in an emotionally sensitive manner based on the user's request for more details. For example, if the user is feeling anxious, it will emphasize product reviews and safety information.

[0141] Step 8:

[0142] The user makes their final selection based on the detailed information provided. After making their selection, they indicate their intention to purchase the item they have decided on.

[0143] Step 9:

[0144] The server confirms the user's purchase intent and guides them through the reservation and purchase procedures for selected products. The goal is to provide an efficient and fulfilling purchasing experience.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Conventional information processing systems have the problem of failing to provide optimal suggestions and options that users desire because they select products and services without considering the user's emotional state. Furthermore, they struggle to flexibly respond to changes in user preferences and emotions, making it difficult to improve the quality of the purchasing experience.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for an information processing device to search an information set based on conditions obtained from the user and select suitable product candidates; means for identifying the emotional state through interaction with the user and prioritizing product candidates based on that emotional information; and means for guiding the user through the procedure for acquiring the finally selected product. This makes it possible to provide optimal products and services while taking the user's emotions into consideration.

[0150] An "information processing device" is a device that searches for data based on input conditions from a user, processes and provides information necessary for a specific purpose.

[0151] An "information set" is a collection of data containing various types of information, stored in a database or similar system.

[0152] A "product candidate" is a list of products selected based on the user's criteria that may be suitable for purchase or use.

[0153] "Emotional state" refers to the state of mind and expression of emotions recognized from the user's voice, word choice, and text.

[0154] Prioritization is the process of evaluating multiple options based on specific criteria and rearranging them according to their importance and relevance.

[0155] "Acquisition procedures" refer to a series of operations or procedures required to obtain a specific product or service.

[0156] This invention relates to an information processing system that provides optimal product suggestions while taking user emotions into consideration. In this embodiment, an information processing device plays a crucial role. First, the user accesses the information processing device using a communication terminal and inputs criteria for product selection. This communication terminal uses speech recognition software (e.g., a voice-to-text API) to convert voice input into text data.

[0157] The server receives user input data sent from the terminal and sends that data to the emotion engine. The emotion engine incorporates natural language processing techniques (e.g., BERT and GPT models) and has the ability to identify emotional states from the user's text data. The emotional information analyzed by the emotion engine is reflected in the server's internal search algorithm.

[0158] This algorithm uses emotional information to prioritize product candidates and provide suggestions tailored to the user. For example, if a user enters "I'm looking for new earphones," and the emotion engine detects a positive emotion, the server can recommend the latest popular products and suggest the best product for a relaxing music experience.

[0159] An example of a prompt using a generative AI model is, "Please suggest music-related products that would be suitable if the user is relaxed," which enables emotion-based product recommendations. This invention allows users to enjoy product selection that takes into account their emotions and preferences, in addition to mere functions and price.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user enters product specifications via a communication terminal. The user can choose between text input or voice input. In the case of voice input, the communication terminal uses speech recognition technology to convert the voice into text data. This input data forms the basis for the next processing step.

[0163] Step 2:

[0164] The terminal sends user input data to the server. The server receives this data and prepares it for sentiment analysis. Here, the input data is structured and converted into an input format for the sentiment engine.

[0165] Step 3:

[0166] The server passes the incoming data to the emotion engine, which performs emotional state identification. The emotion engine uses a generative AI model to extract the user's emotions (e.g., positive, negative, neutral) from the input text data. This generated emotion data is then used for prioritizing in the next process.

[0167] Step 4:

[0168] The server utilizes sentiment information and an internal search algorithm to prioritize product candidates. Specifically, it assigns priority scores to products retrieved from the database based on sentiment data. The order in which products are suggested to the user is then determined based on these scores.

[0169] Step 5:

[0170] The server sends a prioritized list of product candidates to the communication terminal. The terminal receives this information and displays it as a list of suggestions to the user. The user can then review the product candidates and request more detailed information.

[0171] Step 6:

[0172] The user selects a product of interest and requests additional information. This request is sent from the terminal to the server. The server receives the user's request and collects detailed information about the specific product. At this time, the server also provides the latest data and relevant trend information.

[0173] Step 7:

[0174] When the user ultimately decides to purchase, the server guides them through the purchase process. It provides a purchase form and payment information input interface to ensure a smooth transaction. This process completes a purchasing experience that takes the user's emotions into consideration.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] Conventional information processing systems often make uniform suggestions to users when they select products, without considering their individual needs or emotions. This has made it difficult to provide users with a satisfying purchasing experience. Furthermore, because product candidates are selected without considering the user's emotional state, the suggested products frequently fail to meet the user's expectations.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for selecting information based on conditions obtained from the user, means for analyzing emotions recognized through interaction with the user and personalizing product candidates, and means for optimizing and proposing suitable products using the analyzed emotion information. This enables more personalized product suggestions based on the user's emotions.

[0180] An "information processing device" is a device that processes data based on user input conditions and has the function of selecting and presenting relevant information.

[0181] "Dialogue" refers to communication that takes place between a user and an information processing device, and is the process of exchanging information through voice or text.

[0182] "Analyzing emotions" refers to the process of identifying an emotional state based on information obtained from a user and classifying it as positive, negative, or neutral.

[0183] "Product candidates" refer to a selection of products and services chosen based on user needs and conditions.

[0184] "Personalizing" refers to adjusting suggestions to take into account the user's specific needs and emotional state, and providing the information and products that are best suited to each individual user.

[0185] "Optimization" refers to adjusting a system or process to achieve its objectives as effectively and efficiently as possible.

[0186] A "communication terminal" is a device used to access information processing equipment, and includes smartphones and computers.

[0187] The system for carrying out this invention consists of an information processing device comprising a server, a user terminal, and an emotion analysis engine. Users access the information processing device using a communication terminal such as a smartphone to search for and receive product suggestions. Users input product-related conditions via voice or text and inquire about products that match the system.

[0188] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the user's speech into text, and then uses a natural language processing API (e.g., Google Cloud Natural Language API) to perform sentiment analysis based on the converted text. The sentiment analysis engine identifies positive, negative, or neutral emotions from the user's input and uses that information to personalize product recommendations.

[0189] Using user sentiment information, the server searches a product database to identify the most suitable product. If the sentiment analysis determines that the user is in a positive state, it suggests highly-rated products or those ranked highly in popularity. If the user is negative, it provides information on products that emphasize safety and security. The user selects the product that best suits their needs from the suggested options, and the server supports them through the purchase process.

[0190] For example, if a user enters "I want a new camera, but I'm a beginner so I'm worried," the server will detect this worry through sentiment analysis and present information on user-friendly cameras suitable for beginners, along with detailed reviews. In this way, it is possible to improve the user's purchasing experience by using emotional information to suggest products.

[0191] An example of a prompt in a generative AI model might be: "If a user says, 'I want a safe bicycle for my child,' what kind of product would you suggest that would help them choose with emotional confidence?"

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The user inputs product specifications via voice or text using a communication terminal. The terminal captures these specifications and sends them to the server. The input is raw voice or text data.

[0195] Step 2:

[0196] The server converts the received audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text). In this step, the audio input is processed into text data. This converted text then becomes the input for sentiment analysis.

[0197] Step 3:

[0198] The server uses a natural language processing API (e.g., Google Cloud Natural Language API) to analyze the user's emotions from the converted text. In this step, the text data is classified into emotions such as positive, negative, or neutral by an emotion analysis engine. The output is data indicating the user's emotional state.

[0199] Step 4:

[0200] The server searches the product database based on the results of sentiment analysis. It uses sentiment information to select the most suitable product candidates. In this step, sentiment data and search queries are used as input, and a list of matching products is output. The priority of product selection changes according to the user's emotions.

[0201] Step 5:

[0202] The server sends the selected product candidates to the user's terminal and presents the information to the user. The user makes a selection based on the presented product information. The input for this step is a list of suitable products, and the output displays product information that is of interest to the user.

[0203] Step 6:

[0204] The user selects the items they wish to purchase and sends this information to the server. The server assists with the purchase process, processing the necessary data to generate information for payment and delivery. This completes the purchase process. The input for this step is the user's selected items, and the output is a notification that the purchase process is complete.

[0205] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0206] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0207] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0211] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0212] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0213] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0214] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0215] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0216] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0217] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0218] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0219] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0220] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0221] To implement this invention, a system is constructed that communicates between an information processing device and a user terminal, supporting the entire process from product selection to purchase. This allows users to quickly and efficiently select the optimal product through the communication terminal.

[0222] First, the user accesses the information processing device using a communication terminal. The user enters product-related conditions, including budget and desired features (e.g., hardness, size, brand, etc.). The conditions entered by the user are transmitted to the server in real time.

[0223] The server searches a large database based on the received conditions and extracts candidate products that match those conditions. This search process evaluates multivariate attributes based on the conditions and lists possible options.

[0224] The server then sends a list of candidates to the user, who reviews it via their communication device. The candidate list includes basic characteristics of each product (price, firmness, brand name, etc.). The user can request more details about candidates they are interested in, and the server provides the details upon request.

[0225] Through dialogue, users can further narrow down their product selection, and the server responds to support the selection process. Once the user has finalized their selection, the server guides them through the specific procedures for reservation and purchase.

[0226] This type of system allows users to efficiently purchase the products they want while reducing the effort required for product selection.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user accesses the LINE official account using a communication terminal to connect to the information processing device. After accessing the account, they send a message indicating that they would like to choose a mattress.

[0230] Step 2:

[0231] The server receives a message from the user and activates a chatbot as an automated response system. It then asks the user questions to inquire about their purchase conditions, such as budget and desired firmness.

[0232] Step 3:

[0233] The user enters their conditions in response to a question from the server. For example, they might provide details such as, "My budget is 50,000 yen, and I prefer a solid build."

[0234] Step 4:

[0235] The server searches the database based on the conditions provided by the user. During this process, it considers factors such as budget, firmness, and brand to select product candidates that meet the criteria.

[0236] Step 5:

[0237] The server presents the user with multiple mattress options as search results. The information presented includes basic characteristics of each product, such as price, firmness, and brand name.

[0238] Step 6:

[0239] Users can view the list of candidates provided by the server on their communication terminal and inquire about products for which they require more detailed information.

[0240] Step 7:

[0241] The server responds to user requests for detailed information and provides more detailed information (materials, reviews, etc.) about the selected product.

[0242] Step 8:

[0243] Users can narrow down their product choices based on the detailed information provided. They can then continue interacting with the server to select their final purchase options.

[0244] Step 9:

[0245] The server confirms the user's final purchase intention and guides them through the reservation and purchase process for the selected items. It provides links to the purchase page and instructions for the necessary steps.

[0246] By going through this series of processes, users can efficiently select the product that best suits them and have a smooth purchasing experience from start to finish.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] In modern online shopping, users often face the problem of spending a considerable amount of time and effort finding the perfect product from a vast selection. Furthermore, unintended products may be prioritized due to a lack of proper prioritization based on user input. Therefore, there is a need for support for an appropriate and efficient product selection and purchase process.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for searching a memory area based on conditions obtained from the user and selecting suitable object candidates; means for narrowing down the options from the object candidates through dialogue with the user; means for guiding the user through the purchase procedure of the finally selected object; and means for improving the priority of search results using a generative AI model. This enables the user to quickly and accurately find and purchase products that meet their needs.

[0252] An "information processing device" is a computer system that performs data input, processing, storage, and output.

[0253] "User" refers to an individual or organization that uses an information system to input information and receives the output information.

[0254] "Storage area" refers to an area using hardware or software to store data, and includes databases, etc.

[0255] A "potential target item" is a list of products or services that may potentially meet the conditions specified by the user.

[0256] "Communication device" refers to hardware or software that connects to an information processing device via a network and transmits and receives data.

[0257] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze data and derive the optimal solution for a specific task.

[0258] Prioritization is the process of ranking items based on their importance according to specific conditions or criteria.

[0259] "Means" refers to the methods, processes, or devices used to achieve a particular objective.

[0260] This invention is a system that uses a server as an information processing device and a terminal as a communication device, and supports the process of effectively selecting and purchasing the products and services that users desire.

[0261] Users access the server via the internet using devices such as smartphones, tablets, or personal computers. Users input product selection criteria (e.g., price range, product characteristics, brand name, etc.) through the device's interface. The entered criteria are transmitted from the device to the server in real time. HTTP or HTTPS protocols are used for this communication.

[0262] The server analyzes the received conditions and searches for relevant product information using a pre-built database system (e.g., MySQL, PostgreSQL). During the search process, a generative AI model is used to evaluate the suitability of products based on the conditions and rank the results. This method makes it possible to generate a list of product candidates with appropriate priorities.

[0263] For example, if a user enters "firm single-size mattress available for under 20,000 yen" as their search criteria, the server extracts matching product candidates from its database and sends a list to the user's terminal. This list includes the price, rating, and brand name of each product.

[0264] Users can review the submitted product suggestions and view detailed information by selecting items they are interested in. Once a purchase is finalized, the server provides the user with information on payment methods, purchase procedures, and shipping options. This system allows users to quickly select the most suitable product and proceed smoothly through the purchase process.

[0265] The advantage of a search process using generative AI models is that it can dynamically adjust product priorities based on user input, thereby improving the accuracy of search results. Through this system, users can have a stress-free shopping experience.

[0266] As an example of a prompt, it can address specific needs such as, "Please recommend some firm, single-size mattresses within a budget of 20,000 yen."

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The user accesses the system using a terminal. They input product selection criteria into the interface displayed on the terminal screen. These criteria may include, for example, "price range," "characteristics," and "brand." The entered data is sent from the terminal to the server. Based on the input data, the criteria are converted into a format suitable for database searching.

[0270] Step 2:

[0271] The server analyzes the conditional data received from the terminal. The analyzed conditions are constructed as a database query, and a search is performed against the product database. A generative AI model is used to evaluate and prioritize the characteristics of the product data according to the conditions. As a search result, a list of product candidates that match the conditions is generated.

[0272] Step 3:

[0273] The server formats the generated list of product candidates and sends it to the user's terminal. The formatted data is sent in JSON or XML format. The terminal parses the received data and displays it as a list on the user interface. The user can then review the product candidates through this list.

[0274] Step 4:

[0275] The user selects an item of interest from the displayed list of suggested products. The device sends the selection information to the server and requests detailed information about the selected item. The server retrieves the detailed data related to that item from its database and sends it back to the device. Based on the provided product details, the user can then compare and consider other products.

[0276] Step 5:

[0277] The server receives the decision from the user and supports the purchase process of the finally selected product. The purchase process includes the selection of payment methods, the input of delivery information, etc. The server performs order processing based on the purchase information and sends a confirmation notice to the user. As a result, the user can complete the purchase process.

[0278] (Application Example 1)

[0279] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0280] In a conventional online purchase system, the process for a user to select the optimal product from a large number of candidate products is complicated and time-consuming. Also, due to the limited input methods, there is a problem that it is laborious for the user. Furthermore, there is an issue that it is difficult to find an appropriate product when the user has difficulty clearly setting their desired conditions themselves.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes means for searching a database based on conditions acquired from the user, means for acquiring conditions using voice input and converting the conditions into text data, and means for narrowing down product candidates based on the acquired conditions using a generated AI model. As a result, it becomes possible for the user to simply and efficiently select products through voice input.

[0283] An "information processing device" is a device having a function of searching a database based on conditions acquired from a user and selecting product candidates.

[0284] "Voice input" is a method of acquiring the content spoken by the user as digital data, and this data is used as a means for inputting conditions.

[0285] "Text data" refers to data obtained by converting information acquired through voice input into character information.

[0286] "Generative AI model" refers to an artificial intelligence model used to narrow down product candidates from a vast amount of data based on the acquired conditions.

[0287] "Product candidates" refer to a group of products that may meet the user's conditions, and among them are the targets selected by the user.

[0288] A mode for implementing the invention will be described. This invention is a system that connects an information processing device and a user terminal equipped with a voice recognition function via a communication network to assist in the product selection process. The user specifies conditions through voice input, and voice recognition software converts this into text data. This converted data is transmitted to the server through communication.

[0289] The server searches the database based on the input conditions and uses a generative AI model to narrow down product candidates. The generative AI model is an algorithm for selecting products that match the conditions and plays a role in efficiently narrowing down candidates from a large amount of data. In this process, for example, a voice recognition library such as Google Speech-to-Text API is used, and a recommendation engine such as Amazon Personalize is used to identify product candidates.

[0290] Furthermore, the user can specify additional conditions by voice or conduct a voice dialogue about product details. Through this dialogue, the user can narrow down products more easily and finally receive guidance on payment and delivery procedures for the selected product. As a specific example, it is the process of inputting a prompt sentence such as "Tell me a recommended firm mattress for under 20,000 yen" by voice and being recommended a group of products that meet the conditions.

[0291] In this way, users can efficiently select and purchase products through devices they use on a daily basis, using simpler and more convenient methods.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The terminal receives voice input from the user and converts that input into text data using speech recognition software. The user specifies concrete conditions in this input. Since this converted text data is used in subsequent processing, conversion accuracy is crucial.

[0295] Step 2:

[0296] The terminal sends the converted text data to the server via the communication network. The transmitted data includes the user's specified criteria. The data received by the server is used for the subsequent database search process.

[0297] Step 3:

[0298] The server analyzes the user's criteria from the received text data and searches the database for relevant products. During this process, it evaluates each product in the database based on the criteria and extracts candidate products that meet those criteria. A fast and efficient search algorithm is used for data retrieval.

[0299] Step 4:

[0300] The server uses a generative AI model to narrow down the candidate products extracted. The AI ​​model includes a trained algorithm to determine the product that best suits the user's criteria, and selects the optimal product from the candidate products obtained through the database search.

[0301] Step 5:

[0302] The server sends the product candidates selected by the AI model to the terminal. This output includes detailed information such as the product name, price, and features. The user makes the final selection of the product based on this information.

[0303] Step 6:

[0304] The user checks the details of the product through voice or touch operations via the terminal and sends the selection result to the server. Based on this, the server guides the final purchase procedure of the product and provides options for payment and delivery.

[0305] Through this series of processes, the user can utilize voice input to efficiently and easily select products and complete the purchase procedure.

[0306] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0307] The present invention is an information processing system for assisting users. By incorporating an emotion engine into the information processing device, it recognizes the user's emotions and provides more personalized services. This emotion engine identifies emotions through interactions with the user and optimizes product selection and proposals using that information.

[0308] First, the user accesses the information processing device using a communication terminal and inputs conditions for product selection. At this time, the server sends data such as the text input by the user and the tone of voice to the emotion engine and analyzes the emotional state. For example, when the user says "I want a firm mattress" in a happy voice, the emotion engine determines that the user has positive emotions.

[0309] The emotional information analyzed by the emotion engine is reflected in the server's search algorithm. Specifically, if a positive emotion is detected in the user, products that meet certain criteria are prioritized and presented among the product candidates. If the user expresses anxiety, the server supports the user by providing more reassuring information (e.g., reviews and detailed information about safety).

[0310] Users can select products on their communication terminals while reviewing the results of interactions via the emotion engine. When a user requests detailed information about a selected product, the server returns the information taking their emotional state into consideration. For example, if the emotion engine recognizes the user's curiosity, the latest information and trend data related to that product will also be presented.

[0311] In this process, when the user finally decides to make a purchase, the server guides them through the procedures for reserving and purchasing the selected items. This allows users to make appropriate product choices based on their emotions, enabling them to enjoy a high-quality purchasing experience.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user accesses the information processing device using a communication terminal and launches the product selection system via the LINE official account. The user then begins entering product requirements (e.g., budget, rigidity).

[0315] Step 2:

[0316] The server receives user input in real time and sends the text data to the emotion engine. The emotion engine analyzes the emotions from the input text and the input method (e.g., typing speed, tone of voice input).

[0317] Step 3:

[0318] The emotion engine identifies the user's emotions based on its analysis. For example, if the user shows signs of anxiety, the system prepares to incorporate that information into the algorithm.

[0319] Step 4:

[0320] The server selects product candidates by searching the database based on user conditions, while considering the emotional information provided by the emotion engine. Among the products that meet the conditions, it prioritizes listing those that are best suited to the user's emotional state.

[0321] Step 5:

[0322] The server presents the user with selected product candidates and displays basic details for each product (price, features, brand, etc.). Emotional prioritization is also considered here.

[0323] Step 6:

[0324] Users view a product list displayed on their communication device and request more detailed information about items that interest them. It's important to keep in mind that this request is also an emotionally influenced behavior.

[0325] Step 7:

[0326] The server presents relevant information in an emotionally sensitive manner based on the user's request for more details. For example, if the user is feeling anxious, it will emphasize product reviews and safety information.

[0327] Step 8:

[0328] The user makes their final selection based on the detailed information provided. After making their selection, they indicate their intention to purchase the item they have decided on.

[0329] Step 9:

[0330] The server confirms the user's purchase intent and guides them through the reservation and purchase procedures for the selected products. The goal is to provide an efficient and fulfilling purchasing experience.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] Conventional information processing systems have the problem of failing to provide optimal suggestions and options that users desire because they select products and services without considering the user's emotional state. Furthermore, they struggle to flexibly respond to changes in user preferences and emotions, making it difficult to improve the quality of the purchasing experience.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for an information processing device to search an information set based on conditions obtained from the user and select suitable product candidates; means for identifying the emotional state through interaction with the user and prioritizing product candidates based on that emotional information; and means for guiding the user through the procedure for acquiring the finally selected product. This makes it possible to provide optimal products and services while taking the user's emotions into consideration.

[0336] An "information processing device" is a device that searches for data based on input conditions from a user, processes and provides information necessary for a specific purpose.

[0337] An "information set" is a collection of data containing various types of information, stored in a database or similar system.

[0338] A "product candidate" is a list of products selected based on the user's criteria that may be suitable for purchase or use.

[0339] "Emotional state" refers to the state of mind and expression of emotions recognized from the user's voice, word choice, and text.

[0340] Prioritization is the process of evaluating multiple options based on specific criteria and rearranging them according to their importance and relevance.

[0341] "Acquisition procedures" refer to a series of operations or procedures required to obtain a specific product or service.

[0342] This invention relates to an information processing system that provides optimal product suggestions while taking user emotions into consideration. In this embodiment, an information processing device plays a crucial role. First, the user accesses the information processing device using a communication terminal and inputs criteria for product selection. This communication terminal uses speech recognition software (e.g., a voice-to-text API) to convert voice input into text data.

[0343] The server receives user input data sent from the terminal and sends that data to the emotion engine. The emotion engine incorporates natural language processing techniques (e.g., BERT and GPT models) and has the ability to identify emotional states from the user's text data. The emotional information analyzed by the emotion engine is reflected in the server's internal search algorithm.

[0344] This algorithm uses emotional information to prioritize product candidates and provide suggestions tailored to the user. For example, if a user enters "I'm looking for new earphones," and the emotion engine detects a positive emotion, the server can recommend the latest popular products and suggest the best product for a relaxing music experience.

[0345] An example of a prompt using a generative AI model is, "Please suggest music-related products that would be suitable if the user is relaxed," which enables emotion-based product recommendations. This invention allows users to enjoy product selection that takes into account their emotions and preferences, in addition to mere functions and price.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The user enters product specifications via a communication terminal. The user can choose between text input or voice input. In the case of voice input, the communication terminal uses speech recognition technology to convert the voice into text data. This input data forms the basis for the next processing step.

[0349] Step 2:

[0350] The terminal sends user input data to the server. The server receives this data and prepares it for sentiment analysis. Here, the input data is structured and converted into an input format for the sentiment engine.

[0351] Step 3:

[0352] The server passes the incoming data to the emotion engine, which performs emotional state identification. The emotion engine uses a generative AI model to extract the user's emotions (e.g., positive, negative, neutral) from the input text data. This generated emotion data is then used for prioritizing in the next process.

[0353] Step 4:

[0354] The server utilizes sentiment information and an internal search algorithm to prioritize product candidates. Specifically, it assigns priority scores to products retrieved from the database based on sentiment data. The order in which products are suggested to the user is then determined based on these scores.

[0355] Step 5:

[0356] The server sends a prioritized list of product candidates to the communication terminal. The terminal receives this information and displays it as a list of suggestions to the user. The user can then review the product candidates and request more detailed information.

[0357] Step 6:

[0358] The user selects a product of interest and requests additional information. This request is sent from the terminal to the server. The server receives the user's request and collects detailed information about the specific product. At this time, the server also provides the latest data and relevant trend information.

[0359] Step 7:

[0360] When the user ultimately decides to purchase, the server guides them through the purchase process. It provides a purchase form and payment information input interface to ensure a smooth transaction. This process completes a purchasing experience that takes the user's emotions into consideration.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] Conventional information processing systems often make uniform suggestions to users when they select products, without considering their individual needs or emotions. This has made it difficult to provide users with a satisfying purchasing experience. Furthermore, because product candidates are selected without considering the user's emotional state, the suggested products frequently fail to meet the user's expectations.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for selecting information based on conditions obtained from the user, means for analyzing emotions recognized through interaction with the user and personalizing product candidates, and means for optimizing and proposing suitable products using the analyzed emotion information. This enables more personalized product suggestions based on the user's emotions.

[0366] An "information processing device" is a device that processes data based on user input conditions and has the function of selecting and presenting relevant information.

[0367] "Dialogue" refers to communication that takes place between a user and an information processing device, and is the process of exchanging information through voice or text.

[0368] "Analyzing emotions" refers to the process of identifying an emotional state based on information obtained from a user and classifying it as positive, negative, or neutral.

[0369] "Product candidates" refer to a selection of products and services chosen based on user needs and conditions.

[0370] "Personalizing" refers to adjusting suggestions to take into account the user's specific needs and emotional state, and providing the information and products that are best suited to each individual user.

[0371] "Optimization" refers to adjusting a system or process to achieve its objectives as effectively and efficiently as possible.

[0372] A "communication terminal" is a device used to access information processing equipment, and includes smartphones and computers.

[0373] The system for carrying out this invention consists of an information processing device comprising a server, a user terminal, and an emotion analysis engine. Users access the information processing device using a communication terminal such as a smartphone to search for and receive product suggestions. Users input product-related conditions via voice or text and inquire about products that match the system.

[0374] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the user's speech into text, and then uses a natural language processing API (e.g., Google Cloud Natural Language API) to perform sentiment analysis based on the converted text. The sentiment analysis engine identifies positive, negative, or neutral emotions from the user's input and uses that information to personalize product recommendations.

[0375] Using user sentiment information, the server searches a product database to identify the most suitable product. If the sentiment analysis determines that the user is in a positive state, it suggests highly-rated products or those ranked highly in popularity. If the user is negative, it provides information on products that emphasize safety and security. The user selects the product that best suits their needs from the suggested options, and the server supports them through the purchase process.

[0376] For example, if a user enters "I want a new camera, but I'm a beginner so I'm worried," the server will detect this worry through sentiment analysis and present information on user-friendly cameras suitable for beginners, along with detailed reviews. In this way, it is possible to improve the user's purchasing experience by using emotional information to suggest products.

[0377] An example of a prompt in a generative AI model might be: "If a user says, 'I want a safe bicycle for my child,' what kind of product would you suggest that would help them choose with emotional confidence?"

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The user enters product specifications via voice or text using a communication terminal. The terminal captures these specifications and sends them to the server. The input is raw voice or text data.

[0381] Step 2:

[0382] The server converts the received audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text). In this step, the audio input is processed into text data. This converted text then becomes the input for the next sentiment analysis.

[0383] Step 3:

[0384] The server uses a natural language processing API (e.g., Google Cloud Natural Language API) to analyze the user's emotions from the converted text. In this step, the text data is classified into emotions such as positive, negative, or neutral by an emotion analysis engine. The output is data indicating the user's emotional state.

[0385] Step 4:

[0386] The server searches the product database based on the results of sentiment analysis. It uses sentiment information to select the most suitable product candidates. In this step, sentiment data and search queries are used as input, and a list of matching products is output. The priority of product selection changes according to the user's emotions.

[0387] Step 5:

[0388] The server sends the selected product candidates to the user's terminal and presents the information to the user. The user makes a selection based on the presented product information. The input for this step is a list of suitable products, and the output displays product information that is of interest to the user.

[0389] Step 6:

[0390] The user selects the items they wish to purchase and sends this information to the server. The server assists with the purchase process, processing the necessary data to generate information for payment and delivery. This completes the purchase process. The input for this step is the user's selected items, and the output is a notification that the purchase process is complete.

[0391] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0392] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0397] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0399] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0401] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0402] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0403] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0404] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0406] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0407] To implement this invention, a system is constructed that communicates between an information processing device and a user terminal, supporting the entire process from product selection to purchase. This allows users to quickly and efficiently select the optimal product through the communication terminal.

[0408] First, the user accesses the information processing device using a communication terminal. The user enters product-related conditions, including budget and desired features (e.g., hardness, size, brand, etc.). The conditions entered by the user are transmitted to the server in real time.

[0409] The server searches a large database based on the received conditions and extracts candidate products that match those conditions. This search process evaluates multivariate attributes based on the conditions and lists possible options.

[0410] The server then sends a list of candidates to the user, who reviews it via their communication device. The candidate list includes basic characteristics of each product (price, firmness, brand name, etc.). The user can request more details about candidates they are interested in, and the server provides the details upon request.

[0411] Through dialogue, users can further narrow down their product selection, and the server responds to support the selection process. Once the user has finalized their selection, the server guides them through the specific procedures for reservation and purchase.

[0412] This type of system allows users to efficiently purchase the products they want while reducing the effort required for product selection.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The user accesses the LINE official account using a communication terminal to connect to the information processing device. After accessing the account, they send a message indicating that they would like to choose a mattress.

[0416] Step 2:

[0417] The server receives a message from the user and activates a chatbot as an automated response system. It then asks the user questions to inquire about their purchase conditions, such as budget and desired firmness.

[0418] Step 3:

[0419] The user enters their conditions in response to a question from the server. For example, they might provide details such as, "My budget is 50,000 yen, and I prefer a solid build."

[0420] Step 4:

[0421] The server searches the database based on the conditions provided by the user. During this process, it considers factors such as budget, firmness, and brand to select product candidates that meet the criteria.

[0422] Step 5:

[0423] The server presents the user with multiple mattress options as search results. The information presented includes basic characteristics of each product, such as price, firmness, and brand name.

[0424] Step 6:

[0425] Users can view the list of candidates provided by the server on their communication terminal and inquire about products for which they require more detailed information.

[0426] Step 7:

[0427] The server responds to user requests for detailed information and provides more detailed information (materials, reviews, etc.) about the selected product.

[0428] Step 8:

[0429] Users can narrow down their product choices based on the detailed information provided. They can then continue interacting with the server to select their final purchase options.

[0430] Step 9:

[0431] The server confirms the user's final purchase intention and guides them through the reservation and purchase process for the selected items. It provides links to the purchase page and instructions for the necessary steps.

[0432] By going through this series of processes, users can efficiently select the product that best suits them and have a smooth purchasing experience from start to finish.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0435] In modern online shopping, users often face the problem of spending a considerable amount of time and effort finding the perfect product from a vast selection. Furthermore, unintended products may be prioritized due to a lack of proper prioritization based on user input. Therefore, there is a need for support for an appropriate and efficient product selection and purchase process.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for searching a memory area based on conditions obtained from the user and selecting suitable object candidates; means for narrowing down the options from the object candidates through dialogue with the user; means for guiding the user through the purchase procedure of the finally selected object; and means for improving the priority of search results using a generative AI model. This enables the user to quickly and accurately find and purchase products that meet their needs.

[0438] An "information processing device" is a computer system that performs data input, processing, storage, and output.

[0439] "User" refers to an individual or organization that uses an information system to input information and receives the output information.

[0440] "Storage area" refers to an area using hardware or software to store data, and includes databases, etc.

[0441] A "potential target item" is a list of products or services that may potentially meet the conditions specified by the user.

[0442] "Communication device" refers to hardware or software that connects to an information processing device via a network and transmits and receives data.

[0443] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze data and derive the optimal solution for a specific task.

[0444] Prioritization is the process of ranking items based on their importance according to specific conditions or criteria.

[0445] "Means" refers to the methods, processes, or devices used to achieve a particular objective.

[0446] This invention is a system that uses a server as an information processing device and a terminal as a communication device, and supports the process of effectively selecting and purchasing the products and services that users desire.

[0447] Users access the server via the internet using devices such as smartphones, tablets, or personal computers. Users input product selection criteria (e.g., price range, product characteristics, brand name, etc.) through the device's interface. The entered criteria are transmitted from the device to the server in real time. HTTP or HTTPS protocols are used for this communication.

[0448] The server analyzes the received conditions and searches for relevant product information using a pre-built database system (e.g., MySQL, PostgreSQL). During the search process, a generative AI model is used to evaluate the suitability of products based on the conditions and rank the results. This method makes it possible to generate a list of product candidates with appropriate priorities.

[0449] For example, if a user enters "firm single-size mattress available for under 20,000 yen" as their search criteria, the server extracts matching product candidates from its database and sends a list to the user's terminal. This list includes the price, rating, and brand name of each product.

[0450] Users can review the submitted product suggestions and view detailed information by selecting items they are interested in. Once a purchase is finalized, the server provides the user with information on payment methods, purchase procedures, and shipping options. This system allows users to quickly select the most suitable product and proceed smoothly through the purchase process.

[0451] The advantage of a search process using generative AI models is that it can dynamically adjust product priorities based on user input, thereby improving the accuracy of search results. Through this system, users can have a stress-free shopping experience.

[0452] As an example of a prompt, it can address specific needs such as, "Please recommend some firm, single-size mattresses within a budget of 20,000 yen."

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The user accesses the system using a terminal. They input product selection criteria into the interface displayed on the terminal screen. These criteria may include, for example, "price range," "characteristics," and "brand." The entered data is sent from the terminal to the server. Based on the input data, the criteria are converted into a format suitable for database searching.

[0456] Step 2:

[0457] The server analyzes the conditional data received from the terminal. The analyzed conditions are constructed as a database query, and a search is performed against the product database. A generative AI model is used to evaluate and prioritize the characteristics of the product data according to the conditions. As a search result, a list of product candidates that match the conditions is generated.

[0458] Step 3:

[0459] The server formats the generated list of product candidates and sends it to the user's terminal. The formatted data is sent in JSON or XML format. The terminal parses the received data and displays it as a list on the user interface. The user can then review the product candidates through this list.

[0460] Step 4:

[0461] The user selects an item of interest from the displayed list of suggested products. The device sends the selection information to the server and requests detailed information about the selected item. The server retrieves the detailed data related to that item from its database and sends it back to the device. Based on the provided product details, the user can then compare and consider other products.

[0462] Step 5:

[0463] The server receives the user's decision and supports the purchase process for the ultimately selected product. The purchase process includes selecting a payment method and entering shipping information. Based on the purchase information, the server processes the order and sends a confirmation notification to the user. This allows the user to complete the purchase.

[0464] (Application Example 1)

[0465] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] In traditional online purchasing systems, the process of users selecting the best product from a large number of candidates is cumbersome and time-consuming. Furthermore, the limited input methods make it a cumbersome experience for users. Additionally, if users have difficulty clearly defining their desired criteria, finding a suitable product becomes challenging.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0468] In this invention, the server includes means for searching a database based on conditions obtained from the user, means for obtaining conditions using voice input and converting those conditions into text data, and means for narrowing down product candidates based on the obtained conditions using a generative AI model. This enables the user to select products simply and efficiently through voice input.

[0469] An "information processing device" is a device that has the function of searching a database based on conditions obtained from the user and selecting product candidates.

[0470] "Voice input" is a method of acquiring what a user says as digital data, and this data is used as a means of inputting conditions.

[0471] "Text data" refers to data obtained by converting information acquired through voice input into written text.

[0472] A "generative AI model" is an artificial intelligence model used to narrow down product candidates from a vast amount of data based on acquired conditions.

[0473] "Product candidates" refer to a group of products that may meet the user's criteria, from which the user can make a selection.

[0474] The invention will now be described in terms of embodiments for carrying out the invention. This invention is a system that connects an information processing device and a user terminal equipped with voice recognition functionality via a communication network to support the product selection process. The user specifies conditions by voice input, and the voice recognition software converts this into text data. This converted data is transmitted to the server via communication.

[0475] The server searches the database based on the input conditions and uses a generative AI model to narrow down product candidates. The generative AI model is an algorithm for selecting products that match the conditions and plays a role in efficiently narrowing down candidates from a large amount of data. This process uses speech recognition libraries such as the Google Speech-to-Text API, and recommendation engines such as Amazon Personalize are used to identify product candidates.

[0476] Furthermore, users can specify additional conditions by voice and engage in voice dialogue about product details. Through this dialogue, users can more easily narrow down their product selection and receive guidance on payment and delivery procedures for the final selected product. As a concrete example, a user might voice-input a prompt such as, "Please recommend a firm mattress under 20,000 yen," and the system would then recommend a group of products that match that condition.

[0477] In this way, users can efficiently select and purchase products through devices they use on a daily basis, using simpler and more convenient methods.

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The terminal receives voice input from the user and converts the input into text data using speech recognition software. The user specifies concrete conditions in this input. Since this converted text data is used in subsequent processing, conversion accuracy is crucial.

[0481] Step 2:

[0482] The terminal sends the converted text data to the server via the communication network. The transmitted data includes the user's specified criteria. The data received by the server is used for the subsequent database search process.

[0483] Step 3:

[0484] The server analyzes the user's criteria from the received text data and searches the database for relevant products. During this process, it evaluates each product in the database based on the criteria and extracts candidate products that meet those criteria. A fast and efficient search algorithm is used for data retrieval.

[0485] Step 4:

[0486] The server uses a generative AI model to narrow down the candidate products extracted. The AI ​​model includes a trained algorithm to determine the product that best suits the user's criteria, and selects the optimal product from the candidate products obtained through the database search.

[0487] Step 5:

[0488] The server sends the product candidates selected by the AI ​​model to the terminal. This output includes detailed information such as product name, price, and features. The user makes their final product selection based on this information.

[0489] Step 6:

[0490] Users check product details via voice or touch controls on their device and send their selection results to the server. Based on this, the server guides them through the final purchase process and provides payment and delivery options.

[0491] This series of processes allows users to efficiently and easily select products and complete the purchase process using voice input.

[0492] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0493] This invention is an information processing system designed to support users. By incorporating an emotion engine into the information processing device, it recognizes the user's emotions and provides more personalized services. This emotion engine identifies emotions through interaction with the user and uses that information to optimize product selection and recommendations.

[0494] First, the user accesses the information processing device using a communication terminal and inputs their criteria for product selection. At this time, the server sends data such as the text and tone of voice entered by the user to the emotion engine, which analyzes the user's emotional state. For example, if the user says "I want a firm mattress" in a cheerful voice, the emotion engine determines that the user has positive emotions.

[0495] The emotional information analyzed by the emotion engine is reflected in the server's search algorithm. Specifically, if a positive emotion is detected in the user, products that meet certain criteria are prioritized and presented among the product candidates. If the user expresses anxiety, the server supports the user by providing more reassuring information (e.g., reviews and detailed information about safety).

[0496] Users can select products on their communication terminals while reviewing the results of interactions via the emotion engine. When a user requests detailed information about a selected product, the server returns the information taking their emotional state into consideration. For example, if the emotion engine recognizes the user's curiosity, the latest information and trend data related to that product will also be presented.

[0497] In this process, when the user finally decides to make a purchase, the server guides them through the procedures for reserving and purchasing the selected items. This allows users to make appropriate product choices based on their emotions, enabling them to enjoy a high-quality purchasing experience.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user accesses the information processing device using a communication terminal and launches the product selection system via the LINE official account. The user then begins entering product requirements (e.g., budget, rigidity).

[0501] Step 2:

[0502] The server receives user input in real time and sends the text data to the emotion engine. The emotion engine analyzes the emotions from the input text and the input method (e.g., typing speed, tone of voice input).

[0503] Step 3:

[0504] The emotion engine identifies the user's emotions based on its analysis. For example, if the user shows signs of anxiety, the system prepares to incorporate that information into the algorithm.

[0505] Step 4:

[0506] The server selects product candidates by searching the database based on user conditions, while considering the emotional information provided by the emotion engine. Among the products that meet the conditions, it prioritizes listing those that are best suited to the user's emotional state.

[0507] Step 5:

[0508] The server presents the user with selected product candidates and displays basic details for each product (price, features, brand, etc.). Emotional prioritization is also considered here.

[0509] Step 6:

[0510] Users view a product list displayed on their communication device and request more detailed information about items that interest them. It's important to keep in mind that this request is also an emotionally influenced behavior.

[0511] Step 7:

[0512] The server presents relevant information in an emotionally sensitive manner based on the user's request for more details. For example, if the user is feeling anxious, it will emphasize product reviews and safety information.

[0513] Step 8:

[0514] The user makes their final selection based on the detailed information provided. After making their selection, they indicate their intention to purchase the item they have decided on.

[0515] Step 9:

[0516] The server confirms the user's purchase intent and guides them through the reservation and purchase procedures for the selected products. The goal is to provide an efficient and fulfilling purchasing experience.

[0517] (Example 2)

[0518] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] Conventional information processing systems have the problem of failing to provide optimal suggestions and options that users desire because they select products and services without considering the user's emotional state. Furthermore, they struggle to flexibly respond to changes in user preferences and emotions, making it difficult to improve the quality of the purchasing experience.

[0520] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0521] In this invention, the server includes means for an information processing device to search an information set based on conditions obtained from the user and select suitable product candidates; means for identifying the emotional state through interaction with the user and prioritizing product candidates based on that emotional information; and means for guiding the user through the procedure for acquiring the finally selected product. This makes it possible to provide optimal products and services while taking the user's emotions into consideration.

[0522] An "information processing device" is a device that searches for data based on input conditions from a user, processes and provides information necessary for a specific purpose.

[0523] An "information set" is a collection of data containing various types of information, stored in a database or similar system.

[0524] A "product candidate" is a list of products selected based on the user's criteria that may be suitable for purchase or use.

[0525] "Emotional state" refers to the state of mind and expression of emotions recognized from the user's voice, word choice, and text.

[0526] Prioritization is the process of evaluating multiple options based on specific criteria and rearranging them according to their importance and relevance.

[0527] "Acquisition procedures" refer to a series of operations or procedures required to obtain a specific product or service.

[0528] This invention relates to an information processing system that provides optimal product suggestions while taking user emotions into consideration. In this embodiment, an information processing device plays a crucial role. First, the user accesses the information processing device using a communication terminal and inputs criteria for product selection. This communication terminal uses speech recognition software (e.g., a voice-to-text API) to convert voice input into text data.

[0529] The server receives user input data sent from the terminal and sends that data to the emotion engine. The emotion engine incorporates natural language processing techniques (e.g., BERT and GPT models) and has the ability to identify emotional states from the user's text data. The emotional information analyzed by the emotion engine is reflected in the server's internal search algorithm.

[0530] This algorithm uses emotional information to prioritize product candidates and provide suggestions tailored to the user. For example, if a user enters "I'm looking for new earphones," and the emotion engine detects a positive emotion, the server can recommend the latest popular products and suggest the best product for a relaxing music experience.

[0531] An example of a prompt using a generative AI model is, "Please suggest music-related products that would be suitable if the user is relaxed," which enables emotion-based product recommendations. This invention allows users to enjoy product selection that takes into account their emotions and preferences, in addition to mere functions and price.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The user enters product specifications via a communication terminal. The user can choose between text input or voice input. In the case of voice input, the communication terminal uses speech recognition technology to convert the voice into text data. This input data forms the basis for the next processing step.

[0535] Step 2:

[0536] The terminal sends user input data to the server. The server receives this data and prepares it for sentiment analysis. Here, the input data is structured and converted into an input format for the sentiment engine.

[0537] Step 3:

[0538] The server passes the incoming data to the emotion engine, which performs emotional state identification. The emotion engine uses a generative AI model to extract the user's emotions (e.g., positive, negative, neutral) from the input text data. This generated emotion data is then used for prioritizing in the next process.

[0539] Step 4:

[0540] The server utilizes sentiment information and an internal search algorithm to prioritize product candidates. Specifically, it assigns priority scores to products retrieved from the database based on sentiment data. The order in which products are suggested to the user is then determined based on these scores.

[0541] Step 5:

[0542] The server sends a prioritized list of product candidates to the communication terminal. The terminal receives this information and displays it as a list of suggestions to the user. The user can then review the product candidates and request more detailed information.

[0543] Step 6:

[0544] The user selects a product of interest and requests additional information. This request is sent from the terminal to the server. The server receives the user's request and collects detailed information about the specific product. At this time, the server also provides the latest data and relevant trend information.

[0545] Step 7:

[0546] When the user ultimately decides to purchase, the server guides them through the purchase process. It provides a purchase form and payment information input interface to ensure a smooth transaction. This process completes a purchasing experience that takes the user's emotions into consideration.

[0547] (Application Example 2)

[0548] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] Conventional information processing systems often make uniform suggestions to users when they select products, without considering their individual needs or emotions. This has made it difficult to provide users with a satisfying purchasing experience. Furthermore, because product candidates are selected without considering the user's emotional state, the suggested products frequently fail to meet the user's expectations.

[0550] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0551] In this invention, the server includes means for selecting information based on conditions obtained from the user, means for analyzing emotions recognized through interaction with the user and personalizing product candidates, and means for optimizing and proposing suitable products using the analyzed emotion information. This enables more personalized product suggestions based on the user's emotions.

[0552] An "information processing device" is a device that processes data based on user input conditions and has the function of selecting and presenting relevant information.

[0553] "Dialogue" refers to communication that takes place between a user and an information processing device, and is the process of exchanging information through voice or text.

[0554] "Analyzing emotions" refers to the process of identifying an emotional state based on information obtained from a user and classifying it as positive, negative, or neutral.

[0555] "Product candidates" refer to a selection of products and services chosen based on user needs and conditions.

[0556] "Personalizing" refers to adjusting suggestions to take into account the user's specific needs and emotional state, and providing the information and products that are best suited to each individual user.

[0557] "Optimization" refers to adjusting a system or process to achieve its objectives as effectively and efficiently as possible.

[0558] A "communication terminal" is a device used to access information processing equipment, and includes smartphones and computers.

[0559] The system for carrying out this invention consists of an information processing device comprising a server, a user terminal, and an emotion analysis engine. Users access the information processing device using a communication terminal such as a smartphone to search for and receive product suggestions. Users input product-related conditions via voice or text and inquire about products that match the system.

[0560] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the user's speech into text, and then uses a natural language processing API (e.g., Google Cloud Natural Language API) to perform sentiment analysis based on the converted text. The sentiment analysis engine identifies positive, negative, or neutral emotions from the user's input and uses that information to personalize product recommendations.

[0561] Using user sentiment information, the server searches a product database to identify the most suitable product. If the sentiment analysis determines that the user is in a positive state, it suggests highly-rated products or those ranked highly in popularity. If the user is negative, it provides information on products that emphasize safety and security. The user selects the product that best suits their needs from the suggested options, and the server supports them through the purchase process.

[0562] For example, if a user enters "I want a new camera, but I'm a beginner so I'm worried," the server will detect this worry through sentiment analysis and present information on user-friendly cameras suitable for beginners, along with detailed reviews. In this way, it is possible to improve the user's purchasing experience by using emotional information to suggest products.

[0563] An example of a prompt in a generative AI model might be: "If a user says, 'I want a safe bicycle for my child,' what kind of product would you suggest that would help them choose with emotional confidence?"

[0564] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0565] Step 1:

[0566] The user enters product specifications via voice or text using a communication terminal. The terminal captures these specifications and sends them to the server. The input is raw voice or text data.

[0567] Step 2:

[0568] The server converts the received audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text). In this step, the audio input is processed into text data. This converted text then becomes the input for the next sentiment analysis.

[0569] Step 3:

[0570] The server uses a natural language processing API (e.g., Google Cloud Natural Language API) to analyze the user's emotions from the converted text. In this step, the text data is classified into emotions such as positive, negative, or neutral by an emotion analysis engine. The output is data indicating the user's emotional state.

[0571] Step 4:

[0572] The server searches the product database based on the results of sentiment analysis. It uses sentiment information to select the most suitable product candidates. In this step, sentiment data and search queries are used as input, and a list of matching products is output. The priority of product selection changes according to the user's emotions.

[0573] Step 5:

[0574] The server sends the selected product candidates to the user's terminal and presents the information to the user. The user makes a selection based on the presented product information. The input for this step is a list of suitable products, and the output displays product information that is of interest to the user.

[0575] Step 6:

[0576] The user selects the items they wish to purchase and sends this information to the server. The server assists with the purchase process, processing the necessary data to generate information for payment and delivery. This completes the purchase process. The input for this step is the user's selected items, and the output is a notification that the purchase process is complete.

[0577] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0578] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0579] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0583] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0584] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0585] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0586] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0587] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0588] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0589] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0590] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0591] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0592] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0593] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0594] To implement this invention, a system is constructed that communicates between an information processing device and a user terminal, supporting the entire process from product selection to purchase. This allows users to quickly and efficiently select the optimal product through the communication terminal.

[0595] First, the user accesses the information processing device using a communication terminal. The user enters product-related conditions, including budget and desired features (e.g., hardness, size, brand, etc.). The conditions entered by the user are transmitted to the server in real time.

[0596] The server searches a large database based on the received conditions and extracts candidate products that match those conditions. This search process evaluates multivariate attributes based on the conditions and lists possible options.

[0597] The server then sends a list of candidates to the user, who reviews it via their communication device. The candidate list includes basic characteristics of each product (price, firmness, brand name, etc.). The user can request more details about candidates they are interested in, and the server provides the details upon request.

[0598] Through dialogue, users can further narrow down their product selection, and the server responds to support the selection process. Once the user has finalized their selection, the server guides them through the specific procedures for reservation and purchase.

[0599] This type of system allows users to efficiently purchase the products they want while reducing the effort required for product selection.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] The user accesses the LINE official account using a communication terminal to connect to the information processing device. After accessing the account, they send a message indicating that they would like to choose a mattress.

[0603] Step 2:

[0604] The server receives a message from the user and activates a chatbot as an automated response system. It then asks the user questions to inquire about their purchase conditions, such as budget and desired firmness.

[0605] Step 3:

[0606] The user enters their conditions in response to a question from the server. For example, they might provide details such as, "My budget is 50,000 yen, and I prefer a solid build."

[0607] Step 4:

[0608] The server searches the database based on the conditions provided by the user. During this process, it considers factors such as budget, firmness, and brand to select product candidates that meet the criteria.

[0609] Step 5:

[0610] The server presents the user with multiple mattress options as search results. The information presented includes basic characteristics of each product, such as price, firmness, and brand name.

[0611] Step 6:

[0612] Users can view the list of candidates provided by the server on their communication terminal and inquire about products for which they require more detailed information.

[0613] Step 7:

[0614] The server responds to user requests for detailed information and provides more detailed information (materials, reviews, etc.) about the selected product.

[0615] Step 8:

[0616] Users can narrow down their product choices based on the detailed information provided. They can then continue interacting with the server to select their final purchase options.

[0617] Step 9:

[0618] The server confirms the user's final purchase intention and guides them through the reservation and purchase process for the selected items. It provides links to the purchase page and instructions for the necessary steps.

[0619] By going through this series of processes, users can efficiently select the product that best suits them and have a smooth purchasing experience from start to finish.

[0620] (Example 1)

[0621] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] In modern online shopping, users often face the problem of spending a considerable amount of time and effort finding the perfect product from a vast selection. Furthermore, unintended products may be prioritized due to a lack of proper prioritization based on user input. Therefore, there is a need for support for an appropriate and efficient product selection and purchase process.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0624] In this invention, the server includes means for searching a memory area based on conditions obtained from the user and selecting suitable object candidates; means for narrowing down the options from the object candidates through dialogue with the user; means for guiding the user through the purchase procedure of the finally selected object; and means for improving the priority of search results using a generative AI model. This enables the user to quickly and accurately find and purchase products that meet their needs.

[0625] An "information processing device" is a computer system that performs data input, processing, storage, and output.

[0626] "User" refers to an individual or organization that uses an information system to input information and receives the output information.

[0627] "Storage area" refers to an area using hardware or software to store data, and includes databases, etc.

[0628] A "potential target item" is a list of products or services that may potentially meet the conditions specified by the user.

[0629] "Communication device" refers to hardware or software that connects to an information processing device via a network and transmits and receives data.

[0630] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to analyze data and derive the optimal solution for a specific task.

[0631] Prioritization is the process of ranking items based on their importance according to specific conditions or criteria.

[0632] "Means" refers to the methods, processes, or devices used to achieve a particular objective.

[0633] This invention is a system that uses a server as an information processing device and a terminal as a communication device, and supports the process of effectively selecting and purchasing the products and services that users desire.

[0634] Users access the server via the internet using devices such as smartphones, tablets, or personal computers. Users input product selection criteria (e.g., price range, product characteristics, brand name, etc.) through the device's interface. The entered criteria are transmitted from the device to the server in real time. HTTP or HTTPS protocols are used for this communication.

[0635] The server analyzes the received conditions and searches for relevant product information using a pre-built database system (e.g., MySQL, PostgreSQL). During the search process, a generative AI model is used to evaluate the suitability of products based on the conditions and rank the results. This method makes it possible to generate a list of product candidates with appropriate priorities.

[0636] For example, if a user enters "firm single-size mattress available for under 20,000 yen" as their search criteria, the server extracts matching product candidates from its database and sends a list to the user's terminal. This list includes the price, rating, and brand name of each product.

[0637] Users can review the submitted product suggestions and view detailed information by selecting items they are interested in. Once a purchase is finalized, the server provides the user with information on payment methods, purchase procedures, and shipping options. This system allows users to quickly select the most suitable product and proceed smoothly through the purchase process.

[0638] The advantage of a search process using generative AI models is that it can dynamically adjust product priorities based on user input, thereby improving the accuracy of search results. Through this system, users can have a stress-free shopping experience.

[0639] As an example of a prompt, it can address specific needs such as, "Please recommend some firm, single-size mattresses within a budget of 20,000 yen."

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The user accesses the system using a terminal. They input product selection criteria into the interface displayed on the terminal screen. These criteria may include, for example, "price range," "characteristics," and "brand." The entered data is sent from the terminal to the server. Based on the input data, the criteria are converted into a format suitable for database searching.

[0643] Step 2:

[0644] The server analyzes the conditional data received from the terminal. The analyzed conditions are constructed as a database query, and a search is performed against the product database. A generative AI model is used to evaluate and prioritize the characteristics of the product data according to the conditions. As a search result, a list of product candidates that match the conditions is generated.

[0645] Step 3:

[0646] The server formats the generated list of product candidates and sends it to the user's terminal. The formatted data is sent in JSON or XML format. The terminal parses the received data and displays it as a list on the user interface. The user can then review the product candidates through this list.

[0647] Step 4:

[0648] The user selects an item of interest from the displayed list of suggested products. The device sends the selection information to the server and requests detailed information about the selected item. The server retrieves the detailed data related to that item from its database and sends it back to the device. Based on the provided product details, the user can then compare and consider other products.

[0649] Step 5:

[0650] The server receives the user's decision and supports the purchase process for the ultimately selected product. The purchase process includes selecting a payment method and entering shipping information. Based on the purchase information, the server processes the order and sends a confirmation notification to the user. This allows the user to complete the purchase.

[0651] (Application Example 1)

[0652] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] In traditional online purchasing systems, the process of users selecting the best product from a large number of candidates is cumbersome and time-consuming. Furthermore, the limited input methods make it a cumbersome experience for users. Additionally, if users have difficulty clearly defining their desired criteria, finding a suitable product becomes challenging.

[0654] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0655] In this invention, the server includes means for searching a database based on conditions obtained from the user, means for obtaining conditions using voice input and converting those conditions into text data, and means for narrowing down product candidates based on the obtained conditions using a generative AI model. This enables the user to select products simply and efficiently through voice input.

[0656] An "information processing device" is a device that has the function of searching a database based on conditions obtained from the user and selecting product candidates.

[0657] "Voice input" is a method of acquiring what a user says as digital data, and this data is used as a means of inputting conditions.

[0658] "Text data" refers to data obtained by converting information acquired through voice input into written text.

[0659] A "generative AI model" is an artificial intelligence model used to narrow down product candidates from a vast amount of data based on acquired conditions.

[0660] "Product candidates" refer to a group of products that may meet the user's criteria, from which the user can make a selection.

[0661] The invention will now be described in terms of embodiments for carrying out the invention. This invention is a system that connects an information processing device and a user terminal equipped with voice recognition functionality via a communication network to support the product selection process. The user specifies conditions by voice input, and the voice recognition software converts this into text data. This converted data is transmitted to the server via communication.

[0662] The server searches the database based on the input conditions and uses a generative AI model to narrow down product candidates. The generative AI model is an algorithm for selecting products that match the conditions and plays a role in efficiently narrowing down candidates from a large amount of data. This process uses speech recognition libraries such as the Google Speech-to-Text API, and recommendation engines such as Amazon Personalize are used to identify product candidates.

[0663] Furthermore, users can specify additional conditions by voice and engage in voice dialogue about product details. Through this dialogue, users can more easily narrow down their product selection and receive guidance on payment and delivery procedures for the final selected product. As a concrete example, a user might voice-input a prompt such as, "Please recommend a firm mattress under 20,000 yen," and the system would then recommend a group of products that match that condition.

[0664] In this way, users can efficiently select and purchase products through devices they use on a daily basis, using simpler and more convenient methods.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] The terminal receives voice input from the user and converts the input into text data using speech recognition software. The user specifies concrete conditions in this input. Since this converted text data is used in subsequent processing, conversion accuracy is crucial.

[0668] Step 2:

[0669] The terminal sends the converted text data to the server via the communication network. The transmitted data includes the user's specified criteria. The data received by the server is used for the subsequent database search process.

[0670] Step 3:

[0671] The server analyzes the user's criteria from the received text data and searches the database for relevant products. During this process, it evaluates each product in the database based on the criteria and extracts candidate products that meet those criteria. A fast and efficient search algorithm is used for data retrieval.

[0672] Step 4:

[0673] The server uses a generative AI model to narrow down the candidate products extracted. The AI ​​model includes a trained algorithm to determine the product that best suits the user's criteria, and selects the optimal product from the candidate products obtained through the database search.

[0674] Step 5:

[0675] The server sends the product candidates selected by the AI ​​model to the terminal. This output includes detailed information such as product name, price, and features. The user makes their final product selection based on this information.

[0676] Step 6:

[0677] Users check product details via voice or touch controls on their device and send their selection results to the server. Based on this, the server guides them through the final purchase process and provides payment and delivery options.

[0678] This series of processes allows users to efficiently and easily select products and complete the purchase process using voice input.

[0679] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0680] This invention is an information processing system designed to support users. By incorporating an emotion engine into the information processing device, it recognizes the user's emotions and provides more personalized services. This emotion engine identifies emotions through interaction with the user and uses that information to optimize product selection and recommendations.

[0681] First, the user accesses the information processing device using a communication terminal and inputs their criteria for product selection. At this time, the server sends data such as the text and tone of voice entered by the user to the emotion engine, which analyzes the user's emotional state. For example, if the user says "I want a firm mattress" in a cheerful voice, the emotion engine determines that the user has positive emotions.

[0682] The emotional information analyzed by the emotion engine is reflected in the server's search algorithm. Specifically, if a positive emotion is detected in the user, products that meet certain criteria are prioritized and presented among the product candidates. If the user expresses anxiety, the server supports the user by providing more reassuring information (e.g., reviews and detailed information about safety).

[0683] Users can select products on their communication terminals while reviewing the results of interactions via the emotion engine. When a user requests detailed information about a selected product, the server returns the information taking their emotional state into consideration. For example, if the emotion engine recognizes the user's curiosity, the latest information and trend data related to that product will also be presented.

[0684] In this process, when the user finally decides to make a purchase, the server guides them through the procedures for reserving and purchasing the selected items. This allows users to make appropriate product choices based on their emotions, enabling them to enjoy a high-quality purchasing experience.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The user accesses the information processing device using a communication terminal and launches the product selection system via the LINE official account. The user then begins entering product requirements (e.g., budget, rigidity).

[0688] Step 2:

[0689] The server receives user input in real time and sends the text data to the emotion engine. The emotion engine analyzes the emotions from the input text and the input method (e.g., typing speed, tone of voice input).

[0690] Step 3:

[0691] The emotion engine identifies the user's emotions based on its analysis. For example, if the user shows signs of anxiety, the system prepares to incorporate that information into the algorithm.

[0692] Step 4:

[0693] The server selects product candidates by searching the database based on user conditions, while considering the emotional information provided by the emotion engine. Among the products that meet the conditions, it prioritizes listing those that are best suited to the user's emotional state.

[0694] Step 5:

[0695] The server presents the user with selected product candidates and displays basic details for each product (price, features, brand, etc.). Emotional prioritization is also considered here.

[0696] Step 6:

[0697] Users view a product list displayed on their communication device and request more detailed information about products that interest them. It's important to keep in mind that this request is also an emotionally influenced behavior.

[0698] Step 7:

[0699] The server presents relevant information in an emotionally sensitive manner based on the user's request for more details. For example, if the user is feeling anxious, it will emphasize product reviews and safety information.

[0700] Step 8:

[0701] The user makes their final selection based on the detailed information provided. After making their selection, they indicate their intention to purchase the item they have decided on.

[0702] Step 9:

[0703] The server confirms the user's purchase intent and guides them through the reservation and purchase procedures for selected products. The goal is to provide an efficient and fulfilling purchasing experience.

[0704] (Example 2)

[0705] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] Conventional information processing systems have the problem of failing to provide optimal suggestions and options that users desire because they select products and services without considering the user's emotional state. Furthermore, they struggle to flexibly respond to changes in user preferences and emotions, making it difficult to improve the quality of the purchasing experience.

[0707] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0708] In this invention, the server includes means for an information processing device to search an information set based on conditions obtained from the user and select suitable product candidates; means for identifying the emotional state through interaction with the user and prioritizing product candidates based on that emotional information; and means for guiding the user through the procedure for acquiring the finally selected product. This makes it possible to provide optimal products and services while taking the user's emotions into consideration.

[0709] An "information processing device" is a device that searches for data based on input conditions from a user, processes and provides information necessary for a specific purpose.

[0710] An "information set" is a collection of data containing various types of information, stored in a database or similar system.

[0711] A "product candidate" is a list of products selected based on the user's criteria that may be considered for purchase or use.

[0712] "Emotional state" refers to the state of mind and expression of emotions recognized from the user's voice, word choice, and text.

[0713] Prioritization is the process of evaluating multiple options based on specific criteria and rearranging them according to their importance and relevance.

[0714] "Acquisition procedures" refer to a series of operations or procedures required to obtain a specific product or service.

[0715] This invention relates to an information processing system that provides optimal product suggestions while taking user emotions into consideration. In this embodiment, an information processing device plays a crucial role. First, the user accesses the information processing device using a communication terminal and inputs criteria for product selection. This communication terminal uses speech recognition software (e.g., a voice-to-text API) to convert voice input into text data.

[0716] The server receives user input data sent from the terminal and sends that data to the emotion engine. The emotion engine incorporates natural language processing techniques (e.g., BERT and GPT models) and has the ability to identify emotional states from the user's text data. The emotional information analyzed by the emotion engine is reflected in the server's internal search algorithm.

[0717] This algorithm uses emotional information to prioritize product candidates and provide suggestions tailored to the user. For example, if a user enters "I'm looking for new earphones," and the emotion engine detects a positive emotion, the server can recommend the latest popular products and suggest the best product for a relaxing music experience.

[0718] An example of a prompt using a generative AI model is, "Please suggest music-related products that would be suitable if the user is relaxed," which enables emotion-based product recommendations. This invention allows users to enjoy product selection that takes into account their emotions and preferences, in addition to mere functions and price.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The user enters product specifications via a communication terminal. The user can choose between text input or voice input. In the case of voice input, the communication terminal uses speech recognition technology to convert the voice into text data. This input data forms the basis for the next processing step.

[0722] Step 2:

[0723] The terminal sends user input data to the server. The server receives this data and prepares it for sentiment analysis. Here, the input data is structured and converted into an input format for the sentiment engine.

[0724] Step 3:

[0725] The server passes the incoming data to the emotion engine, which performs emotional state identification. The emotion engine uses a generative AI model to extract the user's emotions (e.g., positive, negative, neutral) from the input text data. This generated emotion data is then used for prioritizing in the next process.

[0726] Step 4:

[0727] The server utilizes sentiment information and an internal search algorithm to prioritize product candidates. Specifically, it assigns priority scores to products retrieved from the database based on sentiment data. The order in which products are suggested to the user is then determined based on these scores.

[0728] Step 5:

[0729] The server sends a prioritized list of product candidates to the communication terminal. The terminal receives this information and displays it as a list of suggestions to the user. The user can then review the product candidates and request more detailed information.

[0730] Step 6:

[0731] The user selects a product of interest and requests additional information. This request is sent from the terminal to the server. The server receives the user's request and collects detailed information about the specific product. At this time, the server also provides the latest data and relevant trend information.

[0732] Step 7:

[0733] When the user ultimately decides to purchase, the server guides them through the purchase process. It provides an interface for entering purchase forms and payment information, helping to ensure a smooth transaction. This process completes a purchasing experience that takes the user's emotions into consideration.

[0734] (Application Example 2)

[0735] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] Conventional information processing systems often make uniform suggestions to users when they select products, without considering their individual needs or emotions. This has made it difficult to provide users with a satisfying purchasing experience. Furthermore, because product candidates are selected without considering the user's emotional state, the suggested products frequently fail to meet the user's expectations.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0738] In this invention, the server includes means for selecting information based on conditions obtained from the user, means for analyzing emotions recognized through interaction with the user and personalizing product candidates, and means for optimizing and proposing suitable products using the analyzed emotion information. This enables more personalized product suggestions based on the user's emotions.

[0739] An "information processing device" is a device that processes data based on user input conditions and has the function of selecting and presenting relevant information.

[0740] "Dialogue" refers to communication that takes place between a user and an information processing device, and is the process of exchanging information through voice or text.

[0741] "Analyzing emotions" refers to the process of identifying a user's emotional state based on information obtained from them, and classifying it as positive, negative, or neutral.

[0742] "Product candidates" refer to a selection of products and services chosen based on user needs and conditions.

[0743] "Personalizing" refers to adjusting suggestions to take into account the user's specific needs and emotional state, and providing the information and products that are best suited to each individual user.

[0744] "Optimization" refers to adjusting a system or process to achieve its objectives as effectively and efficiently as possible.

[0745] A "communication terminal" is a device used to access information processing equipment, and includes smartphones and computers.

[0746] The system for carrying out this invention consists of an information processing device comprising a server, a user terminal, and an emotion analysis engine. Users access the information processing device using a communication terminal such as a smartphone to search for and receive product suggestions. Users input product-related conditions via voice or text and inquire about products that match the system.

[0747] The server uses a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the user's speech into text, and then uses a natural language processing API (e.g., Google Cloud Natural Language API) to perform sentiment analysis based on the converted text. The sentiment analysis engine identifies positive, negative, or neutral emotions from the user's input and uses that information to personalize product recommendations.

[0748] Using user sentiment information, the server searches a product database to identify the most suitable product. If the sentiment analysis determines that the user is in a positive state, it suggests highly-rated products or those ranked highly in popularity. If the user is negative, it provides information on products that emphasize safety and security. The user selects the product that best suits their needs from the suggested options, and the server supports them through the purchase process.

[0749] For example, if a user enters "I want a new camera, but I'm a beginner so I'm worried," the server will detect this worry through sentiment analysis and present information on user-friendly cameras suitable for beginners, along with detailed reviews. In this way, it is possible to improve the user's purchasing experience by using emotional information to suggest products.

[0750] An example of a prompt in a generative AI model might be: "If a user says, 'I want a safe bicycle for my child,' what kind of product would you suggest that would help them choose with emotional confidence?"

[0751] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0752] Step 1:

[0753] The user enters product specifications via voice or text using a communication terminal. The terminal captures these specifications and sends them to the server. The input is raw voice or text data.

[0754] Step 2:

[0755] The server converts the received audio data into text using a speech recognition API (e.g., Google Cloud Speech-to-Text). In this step, the audio input is processed into text data. This converted text then becomes the input for the next sentiment analysis.

[0756] Step 3:

[0757] The server uses a natural language processing API (e.g., Google Cloud Natural Language API) to analyze the user's emotions from the converted text. In this step, the text data is classified into emotions such as positive, negative, or neutral by an emotion analysis engine. The output is data indicating the user's emotional state.

[0758] Step 4:

[0759] The server searches the product database based on the results of sentiment analysis. It uses sentiment information to select the most suitable product candidates. In this step, sentiment data and search queries are used as input, and a list of matching products is output. The priority of product selection changes according to the user's emotions.

[0760] Step 5:

[0761] The server sends the selected product candidates to the user's terminal and presents the information to the user. The user makes a selection based on the presented product information. The input for this step is a list of suitable products, and the output displays product information that is of interest to the user.

[0762] Step 6:

[0763] The user selects the items they wish to purchase and sends this information to the server. The server assists with the purchase process, processing the necessary data to generate information for payment and delivery. This completes the purchase process. The input for this step is the user's selected items, and the output is a notification that the purchase process is complete.

[0764] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0765] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0766] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0767] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0768] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0769] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0770] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0771] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0772] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0773] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0774] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0775] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0776] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0777] 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.

[0778] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0779] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0780] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0781] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0782] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0783] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0784] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0785] The following is further disclosed regarding the embodiments described above.

[0786] (Claim 1)

[0787] The information processing device includes means for searching a database based on conditions obtained from the user and selecting suitable product candidates,

[0788] Through dialogue with users, we provide a means to narrow down the options from product candidates,

[0789] A means of guiding the purchase process for the finally selected product,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for a user to access an information processing device using a communication terminal and input conditions.

[0793] (Claim 3)

[0794] The system according to claim 1, comprising means for selecting product candidates, which considers multiple attributes according to conditions and determines suitability based thereon.

[0795] "Example 1"

[0796] (Claim 1)

[0797] The information processing device includes means for searching the storage area based on conditions obtained from the user and selecting suitable target candidate objects,

[0798] Through dialogue with users, a means of narrowing down the options from a list of potential objects,

[0799] A means of guiding the purchase procedure for the final selected item,

[0800] A method for improving the priority of search results using a generative AI model,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, comprising means for a user to access an information processing device using a communication device and input conditions.

[0804] (Claim 3)

[0805] The system according to claim 1, comprising means for selecting a candidate object, which considers multiple characteristics according to the conditions and determines suitability based thereon.

[0806] "Application Example 1"

[0807] (Claim 1)

[0808] The information processing device includes means for searching a database based on conditions obtained from the user and selecting suitable product candidates,

[0809] A means of obtaining conditions using voice input and converting those conditions into text data,

[0810] A method for narrowing down product candidates based on acquired conditions using a generative AI model,

[0811] A means of guiding the purchase process for the finally selected product,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for a user to access an information processing device using a communication terminal that utilizes speech recognition and to input conditions.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for selecting product candidates, considering multiple attributes according to conditions, and determining suitability based on those attributes, and for acquiring attributes through voice input.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] The information processing device includes means for searching an information set based on conditions obtained from the user and selecting suitable product candidates,

[0820] A means of identifying emotional states through interaction with users and prioritizing product candidates based on that emotional information,

[0821] A means of guiding the acquisition procedure for the finally selected product,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising means for a user to access an information processing device using a communication device, input conditions, and utilize voice conversion technology in the process.

[0825] (Claim 3)

[0826] The system according to claim 1, comprising means for selecting product candidates, which considers multiple characteristics in accordance with emotional information and determines suitability based thereon.

[0827] "Application example 2 when combining with an emotional engine"

[0828] (Claim 1)

[0829] A means of selecting information based on conditions obtained from the user,

[0830] A method for analyzing emotions recognized through interaction with users and personalizing product candidates,

[0831] A means of optimizing and proposing suitable products using analyzed emotional information,

[0832] A means of guiding the purchase process for the finally selected product,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising means for accessing an information processing device using a communication terminal, inputting conditions, and obtaining user sentiment.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising means for determining suitability by considering multiple attributes in information obtained based on conditions and reflecting the results of user sentiment analysis. [Explanation of symbols]

[0838] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The information processing device includes means for searching a database based on conditions obtained from the user and selecting suitable product candidates, Through dialogue with users, we provide a means to narrow down the options from product candidates, A means of guiding the purchase process for the finally selected product, A system that includes this.

2. The system according to claim 1, further comprising means for a user to access an information processing device using a communication terminal and input conditions.

3. The system according to claim 1, comprising means for selecting product candidates, which considers multiple attributes according to conditions and determines suitability based thereon.

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