system

Abstract

The system according to this embodiment aims to efficiently propose products that meet the user's needs. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, an analysis unit, and a dialogue unit. The collection unit collects information about products. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes products that meet the user's needs based on the information analyzed by the analysis unit. The analysis unit analyzes the reasons for the products proposed by the proposal unit and how to solve the problem. The dialogue unit allows the user to add requests in a chat dialogue format.

Description

Technical Field

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[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time for a user to find a product that suits them, and the purchased product may not meet expectations.

[0005] The system according to the embodiment aims to efficiently propose a product that meets the needs of the user.

Means for Solving the Problems

[0006] Note: There seems to be a formatting issue in the original text where some tags are not properly separated. I've tried my best to maintain the integrity of the tags during translation. Also, the "!16", "!20", "!19", "3!1" in the tags are likely errors in the original text and are translated as-is for the purpose of maintaining the original format.The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, an analysis unit, and a dialogue unit. The collection unit collects information about products. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes products that meet the user's needs based on the information analyzed by the analysis unit. The analysis unit analyzes the reasons for the products proposed by the proposal unit and how to solve problems. The dialogue unit allows the user to add requests in a chat-like dialogue format. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently propose products that meet the user's needs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The product suggestion system according to an embodiment of the present invention is a system for solving the problem that it takes time for users to find the optimal product when purchasing a new product. The product suggestion system solves the problem that even when searching for recommended products online, there is a lot of information, it takes time to understand the pros and cons of each, and the purchased product may not be suitable for the user. This idea solves this problem by utilizing AI. First, the AI ​​learns from online reviews and product descriptions. This allows the AI ​​to understand the characteristics and evaluations of each product. Next, when a user experiences a problem or inconvenience, they explain the details to the AI. For example, they input a specific request such as, "I want a cleaning robot that doesn't easily get pet hair tangled in it." Based on the user's profile, the AI ​​suggests products that meet the user's needs. The profile includes detailed information such as what the user already owns and their family structure. Furthermore, the AI ​​analyzes the reasons for suggesting products and how they can solve the problem or inconvenience. The user can add further requests in a chat-like dialogue format and ultimately decide on a product as if they were talking to a regular shopping guide. This makes it easier to find products that meet the individual's actual needs. It also makes it easy to guide users to the company's services, such as e-commerce sites. For example, when a user searches for products on an e-commerce site, and AI suggests the most suitable products, the user is more likely to use the company's services. This system allows users to find products that suit them, improving their shopping experience. Companies can also promote the use of their services and improve customer satisfaction. In this way, product suggestion systems can efficiently suggest products that meet user needs and improve the shopping experience.

[0029] The product suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, an analysis unit, and a dialogue unit. The collection unit collects information about products. For example, the collection unit collects online reviews and product descriptions. The collection unit can collect user reviews, expert reviews, blog posts, etc. The collection unit can also collect official manufacturer descriptions, sales site descriptions, user descriptions, etc. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to understand the characteristics and evaluations of each product. The analysis unit can identify the functions, design, performance, etc. of the products. The analysis unit can also perform evaluations based on criteria such as star ratings, score ratings, and comment ratings. The suggestion unit suggests products that meet the user's needs based on the information analyzed by the analysis unit. For example, the suggestion unit suggests products that meet the user's needs based on the user's profile. The suggestion unit can make suggestions based on information such as the user's age, gender, interests, and purchase history. The analysis unit analyzes the reasons for the products suggested by the suggestion unit and how to solve problems. The analysis unit analyzes, for example, the reasons for suggesting a product and how to solve the problem. The analysis unit can perform analysis based on product characteristics, user needs, past purchase history, etc. The dialogue unit allows users to add requests in a chat-like format. The dialogue unit can add requests in the form of text chat, voice chat, video chat, etc. As a result, the product suggestion system according to this embodiment can efficiently suggest products that meet the user's needs and improve the shopping experience.

[0030] The data collection unit collects information about products. For example, it collects online reviews and product descriptions. Specifically, the data collection unit uses web crawlers and APIs to automatically collect product information from various websites. User reviews are obtained from online shopping sites and review sites, while expert reviews are collected from articles and blogs written by experts. Blog posts are collected from personal blogs and professional blogs, allowing for information from diverse perspectives. Furthermore, the data collection unit acquires data from manufacturers' official websites and online stores to collect official manufacturer descriptions and sales site descriptions. This includes product specifications, functions, prices, and promotional information. User descriptions are collected from social media and forums, providing information that reflects actual usage experiences and evaluations. The data collection unit centrally manages this information and stores it in a database. The collected data is stored in various formats, such as text data, image data, and video data, in preparation for analysis by the subsequent analysis unit. The data collection unit can flexibly adjust the data collection frequency and the selection of target sites, ensuring that the latest information is always available. This allows the data collection unit to efficiently gather a wide range of information about products, thereby strengthening the information infrastructure of the entire system.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the collected information to understand the characteristics and evaluations of each product. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract features such as product function, design, and performance. For example, it extracts keywords from reviews and descriptions to classify product characteristics. For image data, it uses image recognition technology to analyze the appearance and design of the product. For video data, it uses video analysis technology to analyze how to use the product and its performance. The analysis unit can also perform evaluations based on criteria such as star ratings, score ratings, and comment ratings. This includes a process of scoring the content of reviews and calculating an overall rating. Furthermore, the analysis unit can statistically analyze the collected data to grasp the popularity and trends of products. For example, it can analyze the number of reviews and changes in ratings of a particular product over time to predict market trends for the product. The analysis unit can use AI to process data in real time and provide analysis results based on the latest information. This allows the analysis unit to quickly and accurately analyze the collected information and understand the characteristics and evaluations of products in detail.

[0032] The Proposal Department suggests products that meet user needs based on information analyzed by the Analysis Department. For example, the Proposal Department suggests products that meet user needs based on the user's profile. Specifically, it uses an AI algorithm to select the most suitable products based on information such as the user's age, gender, interests, and purchase history. For example, it suggests the latest gadgets and fashion items to younger users, and health-related products and easy-to-use home appliances to older users. The Proposal Department can analyze the user's past purchase history and suggest repeat purchases and related products. Furthermore, the Proposal Department generates customized product lists based on the user's interests. For example, it suggests camping and mountaineering equipment to users who enjoy the outdoors, and kitchenware and recipe books to users who enjoy cooking. The Proposal Department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the Proposal Department to efficiently suggest products that meet user needs and improve the shopping experience.

[0033] The analysis department analyzes the reasons behind the products proposed by the proposal department and how they solve problems. Specifically, the analysis department evaluates the validity of the proposal based on the characteristics of the proposed product, the user's needs, and past purchase history. For example, if a user is looking for a specific function, the analysis department analyzes why a product with that function was proposed. It also evaluates the relationship between products the user has purchased in the past and the proposed product to confirm the appropriateness of the proposal. The analysis department can use AI to analyze data and automatically evaluate the reasons for proposals and how they solve problems. Furthermore, the analysis department can identify problems and areas for improvement of proposed products and provide feedback to users. For example, it can analyze reviews of proposed products to anticipate potential problems users may have and propose solutions. This allows the analysis department to evaluate the validity of proposals and make more appropriate product recommendations to users.

[0034] The dialogue unit accepts user requests via chat. Specifically, it accepts user requests in various formats such as text chat, voice chat, and video chat. The dialogue unit uses natural language processing (NLP) technology to understand user requests and generate appropriate responses. For example, if a user requests detailed information about a specific product, the dialogue unit provides a detailed explanation based on information obtained from the data collection and analysis units. Also, if a user wants to compare products, the dialogue unit can compare multiple products and present their features and evaluations. The dialogue unit can collect user feedback in real time and use it to improve the entire system. Furthermore, the dialogue unit collaborates with the suggestion and analysis units to provide optimal product suggestions and problem-solving solutions in response to user requests. In this way, the dialogue unit can provide a more personalized shopping experience through interaction with users and improve user satisfaction.

[0035] The data collection unit can collect online reviews and product descriptions. For example, the data collection unit can collect online reviews and product descriptions. The data collection unit can collect user reviews, expert reviews, blog posts, etc. The data collection unit can also collect official manufacturer descriptions, sales site descriptions, user descriptions, etc. This allows for an understanding of product features and evaluations by collecting online reviews and product descriptions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input online reviews and product descriptions into a generating AI, and the generating AI can collect the information.

[0036] The analysis unit can analyze the collected information and understand the characteristics and evaluations of each product. For example, the analysis unit can analyze the collected information and understand the characteristics and evaluations of each product. The analysis unit can identify the functions, design, and performance of the products. The analysis unit can also perform evaluations based on criteria such as star ratings, score ratings, and comment ratings. In this way, by analyzing the collected information, the characteristics and evaluations of each product can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI, and the generating AI can analyze the information.

[0037] The suggestion unit can suggest products that meet the user's needs based on the user's profile. For example, the suggestion unit can suggest products that meet the user's needs based on the user's profile. The suggestion unit can make suggestions based on information such as the user's age, gender, interests, and purchase history. This allows the user to find products that meet their needs by suggesting products based on their profile. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's profile into a generating AI, and the generating AI can then suggest products.

[0038] The analysis department can analyze the reasons for the proposed product and how it solves problems. For example, the analysis department can analyze the reasons for the proposed product and how it solves problems. The analysis department can perform analysis based on product characteristics, user needs, past purchase history, etc. By analyzing the reasons for the proposed product and how it solves problems, it can make proposals that are convincing to users. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input information about the proposed product into a generating AI and have the generating AI perform the analysis.

[0039] The dialogue unit allows users to add requests in a chat-like format. For example, users can add requests in a chat-like format. The dialogue unit can also accept requests in formats such as text chat, voice chat, and video chat. This allows users to address more specific needs by adding requests in a chat-like format. Some or all of the above-described processes in the dialogue unit may be performed using, for example, a generative AI, or not. For example, the dialogue unit can input user requests into a generative AI, which can then conduct the dialogue.

[0040] The data collection unit can prioritize collecting highly relevant information by referring to the user's past purchase history during the collection process. For example, the data collection unit can prioritize collecting reviews of products similar to those the user has previously purchased. The data collection unit can prioritize collecting reviews of products that the user has previously given high ratings to. The data collection unit can prioritize collecting reviews of related products based on the categories of products the user has previously purchased. This allows for the priority collection of highly relevant information by referring to the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history into a generating AI, which can then collect highly relevant information.

[0041] The data collection unit can filter data based on the user's current areas of interest and lifestyle during the collection process. For example, the data collection unit can prioritize collecting product reviews in categories that the user is currently interested in. The data collection unit can collect product reviews relevant to the user based on their lifestyle (e.g., owning pets, having children). The data collection unit can collect product reviews relevant to the user based on their recent search history. This allows the system to provide highly relevant information by filtering data based on the user's current areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current areas of interest and lifestyle into a generating AI, which can then perform the filtering.

[0042] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize collecting reviews of nearby stores based on the user's current location. The data collection unit can prioritize collecting reviews of popular products in the user's region. The data collection unit can collect reviews of relevant products based on the characteristics of the user's region (e.g., climate, culture). This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect reviews related to products that the user has "liked" on social media. The data collection unit can collect reviews of products recommended by influencers that the user follows. The data collection unit can collect reviews of products that the user has shared on social media. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant information.

[0044] The analysis unit can evaluate the reliability of the collected information during analysis and prioritize the analysis of highly reliable information. For example, the analysis unit can prioritize the analysis of highly rated reviews. The analysis unit can prioritize the analysis of information from reliable sources. The analysis unit can prioritize the analysis of information supported by many users. In this way, by evaluating the reliability of the collected information, highly reliable information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the collected information into a generating AI, have the generating AI evaluate the reliability, and prioritize the analysis of highly reliable information.

[0045] The analysis unit can apply different analysis algorithms to each product category during analysis. For example, the analysis unit can apply an analysis algorithm based on performance evaluation to home appliances. For fashion items, it can apply an analysis algorithm based on design and trends. For food products, it can apply an analysis algorithm based on taste and nutritional value. By applying different analysis algorithms to each product category, the analysis unit can provide optimal analysis results for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each product category into a generating AI, and the generating AI can perform the analysis.

[0046] The analysis unit can determine the priority of analysis based on the submission date of the collected information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent reviews. The analysis unit can also prioritize the latest information while referring to older reviews. The analysis unit can evaluate the reliability of the information based on the submission date and determine the priority of analysis. This allows for the prioritization of the latest information by determining the priority of analysis based on the submission date of the collected information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission dates of the collected information into a generating AI, which can then evaluate the submission dates and determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the collected information during analysis. For example, the analysis unit can prioritize the analysis of information most relevant to the user's needs. The analysis unit can postpone the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows the analysis unit to prioritize the analysis of information most relevant to the user's needs by adjusting the order of analysis based on the relevance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the collected information into a generating AI, have the generating AI evaluate the relevance, and adjust the order of analysis.

[0048] The suggestion function can adjust the level of detail in a suggestion based on the importance of the product. For example, the suggestion function can provide detailed information for expensive products, while providing concise information for everyday products. The suggestion function can also adjust the level of detail in a suggestion based on the user's level of interest. This allows the suggestion function to provide the user with the most relevant information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of the product into a generating AI, which can then adjust the level of detail in the suggestion.

[0049] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm based on performance evaluation to home appliances. For fashion items, it can apply a suggestion algorithm based on design and trends. For food products, it can apply a suggestion algorithm based on taste and nutritional value. By applying different suggestion algorithms depending on the product category, the unit can provide the most suitable suggestions for each category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input different suggestion algorithms for each product category into a generating AI, and the generating AI can then make suggestions.

[0050] The proposal department can determine the priority of proposals based on the product submission date when submitting a proposal. For example, the proposal department may prioritize the newest products. The proposal department can also prioritize the newest products while referring to older products. The proposal department can evaluate the reliability of products based on the submission date and determine the priority of proposals. This allows for the provision of the latest information preferentially by prioritizing proposals based on the product submission date. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the product submission dates into a generating AI, which can then evaluate the submission dates and determine the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit can prioritize suggesting products that are most relevant to the user's needs. The suggestion unit can also postpone suggesting products that are less relevant. The suggestion unit can dynamically adjust the order of suggestions based on the relevance of the products. This allows the suggestion unit to prioritize suggesting products that are most relevant to the user's needs by adjusting the order of suggestions based on the relevance of the products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of products into a generating AI, which can then evaluate the relevance and adjust the order of suggestions.

[0052] The analysis unit can improve the accuracy of its analysis by referring to past evaluations of the proposed product during the analysis process. For example, the analysis unit can analyze by referring to past evaluations of highly-rated products. The analysis unit can analyze by referring to past evaluations of low-rated products. The analysis unit can analyze by referring to past evaluations of products supported by many users. In this way, the accuracy of the analysis can be improved by referring to past evaluations of the proposed product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past evaluations of the proposed product into a generating AI, and the generating AI can perform the analysis by referring to past evaluations.

[0053] The analysis unit can perform analysis based on the usage status of the proposed product. For example, the analysis unit can analyze based on the actual usage status of the product used by a user. The analysis unit can also analyze based on the usage status of the product used by other users. The analysis unit can select the optimal analysis method based on the usage status of the proposed product. This allows the analysis unit to provide the user with the most optimal analysis results by performing the analysis based on the usage status of the proposed product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the usage status of the proposed product into a generating AI, and the generating AI can analyze the usage status.

[0054] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the proposed product during the analysis process. For example, the analysis unit can perform a detailed analysis based on the relevant literature on the proposed product. The analysis unit can provide reliable information by referring to relevant literature on the proposed product. The analysis unit can provide information that meets the user's needs based on the relevant literature on the proposed product. This allows the accuracy of the analysis to be improved by referring to relevant literature on the proposed product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature on the proposed product into a generating AI, and the generating AI can refer to the relevant literature and perform the analysis.

[0055] The analysis unit can perform analysis while considering the geographical distribution of the proposed products. For example, the analysis unit can provide evaluations for each region based on the geographical distribution of the proposed products. The analysis unit can provide information that is tailored to the needs of each region, taking into account the geographical distribution of the proposed products. The analysis unit can select the optimal analysis method based on the geographical distribution of the proposed products. This allows for the provision of information tailored to the needs of each region by considering the geographical distribution of the proposed products. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of the proposed products into a generating AI, and the generating AI can perform analysis while considering the geographical distribution.

[0056] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. The dialogue unit can provide a dialogue method that suits the user's preferences by referring to the user's past dialogue history. The dialogue unit can select a dialogue method that suits the user's needs based on the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI, and the generating AI can select the optimal dialogue method.

[0057] The dialogue unit can customize the dialogue content based on the user's current areas of interest during a conversation. For example, the dialogue unit can customize the dialogue content based on the user's current areas of interest. The dialogue unit can provide relevant information considering the user's current areas of interest. The dialogue unit can select the optimal dialogue method based on the user's current areas of interest. This allows the dialogue unit to provide the most suitable conversation for the user by customizing the dialogue content based on the user's current areas of interest. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's current areas of interest into a generating AI, and the generating AI can customize the dialogue content.

[0058] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's device information. For example, if the user is using a smartphone, the dialogue unit can provide a dialogue method adapted to the screen size. If the user is using a tablet, the dialogue unit can provide a dialogue method optimized for a larger screen. If the user is using a smartwatch, the dialogue unit can provide a concise and highly visible dialogue method. In this way, the optimal dialogue method can be provided by taking into account the user's device information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's device information into a generating AI, and the generating AI can select the optimal dialogue method.

[0059] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can suggest dialogue content related to products the user has "liked" on social media. The dialogue unit can suggest dialogue content related to products recommended by influencers the user follows. The dialogue unit can suggest dialogue content related to products the user has shared on social media. In this way, relevant dialogue content can be provided by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media activity into a generating AI, and the generating AI can suggest dialogue content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can prioritize collecting highly relevant information by referring to the user's past purchase history. For example, the data collection unit can prioritize collecting reviews of products similar to those the user has previously purchased. The data collection unit can prioritize collecting reviews of products that the user has previously given high ratings to. The data collection unit can prioritize collecting reviews of related products based on the categories of products the user has previously purchased. In this way, highly relevant information can be prioritized by referring to the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history into a generating AI, and the generating AI can collect highly relevant information.

[0062] The suggestion function can adjust the level of detail in a suggestion based on the importance of the product. For example, the suggestion function can provide detailed information for expensive products, while providing concise information for everyday products. The suggestion function can also adjust the level of detail in a suggestion based on the user's level of interest. This allows the suggestion function to provide the user with the most relevant information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of the product into a generating AI, which can then adjust the level of detail in the suggestion.

[0063] The analysis unit can evaluate the reliability of the collected information during analysis and prioritize the analysis of highly reliable information. For example, the analysis unit can prioritize the analysis of highly rated reviews. The analysis unit can prioritize the analysis of information from reliable sources. The analysis unit can prioritize the analysis of information supported by many users. In this way, by evaluating the reliability of the collected information, highly reliable information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the collected information into a generating AI, have the generating AI evaluate the reliability, and prioritize the analysis of highly reliable information.

[0064] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. The dialogue unit can provide a dialogue method that suits the user's preferences by referring to the user's past dialogue history. The dialogue unit can select a dialogue method that suits the user's needs based on the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI, and the generating AI can select the optimal dialogue method.

[0065] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm based on performance evaluation to home appliances. For fashion items, it can apply a suggestion algorithm based on design and trends. For food products, it can apply a suggestion algorithm based on taste and nutritional value. By applying different suggestion algorithms depending on the product category, the unit can provide the most suitable suggestions for each category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input different suggestion algorithms for each product category into a generating AI, and the generating AI can then make suggestions.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection team gathers information about the product. For example, they collect online reviews, product descriptions, user reviews, expert reviews, blog posts, official manufacturer descriptions, sales site descriptions, and user reviews. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected information to understand the characteristics and evaluations of each product and identify the product's functions, design, performance, etc. Evaluations can also be performed using criteria such as star ratings, point ratings, and comment ratings. Step 3: The proposal department proposes products that meet the user's needs based on the information analyzed by the analysis department. For example, based on the user's profile, the proposal is made based on information such as the user's age, gender, interests, and purchase history. Step 4: The analysis department analyzes the reasons and problem-solving methods for the products proposed by the proposal department. For example, the analysis is based on the characteristics of the proposed product, user needs, and past purchase history. Step 5: The dialogue section allows users to add requests in a chat format. For example, requests can be added via text chat, voice chat, video chat, etc.

[0068] (Example of form 2) The product suggestion system according to an embodiment of the present invention is a system for solving the problem that it takes time for users to find the optimal product when purchasing a new product. The product suggestion system solves the problem that even when searching for recommended products online, there is a lot of information, it takes time to understand the pros and cons of each, and the purchased product may not be suitable for the user. This idea solves this problem by utilizing AI. First, the AI ​​learns from online reviews and product descriptions. This allows the AI ​​to understand the characteristics and evaluations of each product. Next, when a user experiences a problem or inconvenience, they explain the details to the AI. For example, they input a specific request such as, "I want a cleaning robot that doesn't easily get pet hair tangled in it." Based on the user's profile, the AI ​​suggests products that meet the user's needs. The profile includes detailed information such as what the user already owns and their family structure. Furthermore, the AI ​​analyzes the reasons for suggesting products and how they can solve the problem or inconvenience. The user can add further requests in a chat-like dialogue format and ultimately decide on a product as if they were talking to a regular shopping guide. This makes it easier to find products that meet the individual's actual needs. It also makes it easy to guide users to the company's services, such as e-commerce sites. For example, when a user searches for products on an e-commerce site, and AI suggests the most suitable products, the user is more likely to use the company's services. This system allows users to find products that suit them, improving their shopping experience. Companies can also promote the use of their services and improve customer satisfaction. In this way, product suggestion systems can efficiently suggest products that meet user needs and improve the shopping experience.

[0069] The product suggestion system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, an analysis unit, and a dialogue unit. The collection unit collects information about products. For example, the collection unit collects online reviews and product descriptions. The collection unit can collect user reviews, expert reviews, blog posts, etc. The collection unit can also collect official manufacturer descriptions, sales site descriptions, user descriptions, etc. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to understand the characteristics and evaluations of each product. The analysis unit can identify the functions, design, performance, etc. of the products. The analysis unit can also perform evaluations based on criteria such as star ratings, score ratings, and comment ratings. The suggestion unit suggests products that meet the user's needs based on the information analyzed by the analysis unit. For example, the suggestion unit suggests products that meet the user's needs based on the user's profile. The suggestion unit can make suggestions based on information such as the user's age, gender, interests, and purchase history. The analysis unit analyzes the reasons for the products suggested by the suggestion unit and how to solve problems. The analysis unit analyzes, for example, the reasons for suggesting a product and how to solve the problem. The analysis unit can perform analysis based on product characteristics, user needs, past purchase history, etc. The dialogue unit allows users to add requests in a chat-like format. The dialogue unit can add requests in the form of text chat, voice chat, video chat, etc. As a result, the product suggestion system according to this embodiment can efficiently suggest products that meet the user's needs and improve the shopping experience.

[0070] The data collection unit collects information about products. For example, it collects online reviews and product descriptions. Specifically, the data collection unit uses web crawlers and APIs to automatically collect product information from various websites. User reviews are obtained from online shopping sites and review sites, while expert reviews are collected from articles and blogs written by experts. Blog posts are collected from personal blogs and professional blogs, allowing for information from diverse perspectives. Furthermore, the data collection unit acquires data from manufacturers' official websites and online stores to collect official manufacturer descriptions and sales site descriptions. This includes product specifications, functions, prices, and promotional information. User descriptions are collected from social media and forums, providing information that reflects actual usage experiences and evaluations. The data collection unit centrally manages this information and stores it in a database. The collected data is stored in various formats, such as text data, image data, and video data, in preparation for analysis by the subsequent analysis unit. The data collection unit can flexibly adjust the data collection frequency and the selection of target sites, ensuring that the latest information is always available. This allows the data collection unit to efficiently gather a wide range of information about products, thereby strengthening the information infrastructure of the entire system.

[0071] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit analyzes the collected information to understand the characteristics and evaluations of each product. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract features such as product function, design, and performance. For example, it extracts keywords from reviews and descriptions to classify product characteristics. For image data, it uses image recognition technology to analyze the appearance and design of the product. For video data, it uses video analysis technology to analyze how to use the product and its performance. The analysis unit can also perform evaluations based on criteria such as star ratings, score ratings, and comment ratings. This includes a process of scoring the content of reviews and calculating an overall rating. Furthermore, the analysis unit can statistically analyze the collected data to grasp the popularity and trends of products. For example, it can analyze the number of reviews and changes in ratings of a particular product over time to predict market trends for the product. The analysis unit can use AI to process data in real time and provide analysis results based on the latest information. This allows the analysis unit to quickly and accurately analyze the collected information and understand the characteristics and evaluations of products in detail.

[0072] The Proposal Department suggests products that meet user needs based on information analyzed by the Analysis Department. For example, the Proposal Department suggests products that meet user needs based on the user's profile. Specifically, it uses an AI algorithm to select the most suitable products based on information such as the user's age, gender, interests, and purchase history. For example, it suggests the latest gadgets and fashion items to younger users, and health-related products and easy-to-use home appliances to older users. The Proposal Department can analyze the user's past purchase history and suggest repeat purchases and related products. Furthermore, the Proposal Department generates customized product lists based on the user's interests. For example, it suggests camping and mountaineering equipment to users who enjoy the outdoors, and kitchenware and recipe books to users who enjoy cooking. The Proposal Department can collect user feedback and continuously improve the accuracy of its suggestions. This allows the Proposal Department to efficiently suggest products that meet user needs and improve the shopping experience.

[0073] The analysis department analyzes the reasons behind the products proposed by the proposal department and how they solve problems. Specifically, the analysis department evaluates the validity of the proposal based on the characteristics of the proposed product, the user's needs, and past purchase history. For example, if a user is looking for a specific function, the analysis department analyzes why a product with that function was proposed. It also evaluates the relationship between products the user has purchased in the past and the proposed product to confirm the appropriateness of the proposal. The analysis department can use AI to analyze data and automatically evaluate the reasons for proposals and how they solve problems. Furthermore, the analysis department can identify problems and areas for improvement of proposed products and provide feedback to users. For example, it can analyze reviews of proposed products to anticipate potential problems users may have and propose solutions. This allows the analysis department to evaluate the validity of proposals and make more appropriate product recommendations to users.

[0074] The dialogue unit accepts user requests via chat. Specifically, it accepts user requests in various formats such as text chat, voice chat, and video chat. The dialogue unit uses natural language processing (NLP) technology to understand user requests and generate appropriate responses. For example, if a user requests detailed information about a specific product, the dialogue unit provides a detailed explanation based on information obtained from the data collection and analysis units. Also, if a user wants to compare products, the dialogue unit can compare multiple products and present their features and evaluations. The dialogue unit can collect user feedback in real time and use it to improve the entire system. Furthermore, the dialogue unit collaborates with the suggestion and analysis units to provide optimal product suggestions and problem-solving solutions in response to user requests. In this way, the dialogue unit can provide a more personalized shopping experience through interaction with users and improve user satisfaction.

[0075] The data collection unit can collect online reviews and product descriptions. For example, the data collection unit can collect online reviews and product descriptions. The data collection unit can collect user reviews, expert reviews, blog posts, etc. The data collection unit can also collect official manufacturer descriptions, sales site descriptions, user descriptions, etc. This allows for an understanding of product features and evaluations by collecting online reviews and product descriptions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input online reviews and product descriptions into a generating AI, and the generating AI can collect the information.

[0076] The analysis unit can analyze the collected information and understand the characteristics and evaluations of each product. For example, the analysis unit can analyze the collected information and understand the characteristics and evaluations of each product. The analysis unit can identify the functions, design, and performance of the products. The analysis unit can also perform evaluations based on criteria such as star ratings, score ratings, and comment ratings. In this way, by analyzing the collected information, the characteristics and evaluations of each product can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI, and the generating AI can analyze the information.

[0077] The suggestion unit can suggest products that meet the user's needs based on the user's profile. For example, the suggestion unit can suggest products that meet the user's needs based on the user's profile. The suggestion unit can make suggestions based on information such as the user's age, gender, interests, and purchase history. This allows the user to find products that meet their needs by suggesting products based on their profile. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's profile into a generating AI, and the generating AI can then suggest products.

[0078] The analysis department can analyze the reasons for the proposed product and how it solves problems. For example, the analysis department can analyze the reasons for the proposed product and how it solves problems. The analysis department can perform analysis based on product characteristics, user needs, past purchase history, etc. By analyzing the reasons for the proposed product and how it solves problems, it can make proposals that are convincing to users. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input information about the proposed product into a generating AI and have the generating AI perform the analysis.

[0079] The dialogue unit allows users to add requests in a chat-like format. For example, users can add requests in a chat-like format. The dialogue unit can also accept requests in formats such as text chat, voice chat, and video chat. This allows users to address more specific needs by adding requests in a chat-like format. Some or all of the above-described processes in the dialogue unit may be performed using, for example, a generative AI, or not. For example, the dialogue unit can input user requests into a generative AI, which can then conduct the dialogue.

[0080] The data collection unit can estimate the user's emotions and determine the priority of reviews and product descriptions to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting positive reviews to alleviate the user's mood. If the user is relaxed, the data collection unit can prioritize collecting detailed product descriptions to help the user understand them more deeply. If the user is in a hurry, the data collection unit can prioritize collecting concise reviews to provide information quickly. This allows the system to provide the user with the most relevant information by prioritizing the information to collect based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of the information to collect.

[0081] The data collection unit can prioritize collecting highly relevant information by referring to the user's past purchase history during the collection process. For example, the data collection unit can prioritize collecting reviews of products similar to those the user has previously purchased. The data collection unit can prioritize collecting reviews of products that the user has previously given high ratings to. The data collection unit can prioritize collecting reviews of related products based on the categories of products the user has previously purchased. This allows for the priority collection of highly relevant information by referring to the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history into a generating AI, which can then collect highly relevant information.

[0082] The data collection unit can filter data based on the user's current areas of interest and lifestyle during the collection process. For example, the data collection unit can prioritize collecting product reviews in categories that the user is currently interested in. The data collection unit can collect product reviews relevant to the user based on their lifestyle (e.g., owning pets, having children). The data collection unit can collect product reviews relevant to the user based on their recent search history. This allows the system to provide highly relevant information by filtering data based on the user's current areas of interest and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current areas of interest and lifestyle into a generating AI, which can then perform the filtering.

[0083] The data collection unit can estimate the user's emotions and adjust the amount of information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the amount of information and collect concise reviews. If the user is relaxed, the data collection unit can collect more detailed reviews. If the user is in a hurry, the data collection unit can collect short, to-the-point reviews. This allows the system to provide the user with the optimal amount of information by adjusting the amount of information collected based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the amount of information collected.

[0084] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize collecting reviews of nearby stores based on the user's current location. The data collection unit can prioritize collecting reviews of popular products in the user's region. The data collection unit can collect reviews of relevant products based on the characteristics of the user's region (e.g., climate, culture). This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then collect highly relevant information.

[0085] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect reviews related to products that the user has "liked" on social media. The data collection unit can collect reviews of products recommended by influencers that the user follows. The data collection unit can collect reviews of products that the user has shared on social media. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can improve the accuracy of the analysis and provide reliable information. If the user is relaxed, the analysis unit can perform a detailed analysis and provide deeper insights. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise information. By adjusting the accuracy of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and adjust the accuracy of the analysis.

[0087] The analysis unit can evaluate the reliability of the collected information during analysis and prioritize the analysis of highly reliable information. For example, the analysis unit can prioritize the analysis of highly rated reviews. The analysis unit can prioritize the analysis of information from reliable sources. The analysis unit can prioritize the analysis of information supported by many users. In this way, by evaluating the reliability of the collected information, highly reliable information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the collected information into a generating AI, have the generating AI evaluate the reliability, and prioritize the analysis of highly reliable information.

[0088] The analysis unit can apply different analysis algorithms to each product category during analysis. For example, the analysis unit can apply an analysis algorithm based on performance evaluation to home appliances. For fashion items, it can apply an analysis algorithm based on design and trends. For food products, it can apply an analysis algorithm based on taste and nutritional value. By applying different analysis algorithms to each product category, the analysis unit can provide optimal analysis results for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms for each product category into a generating AI, and the generating AI can perform the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and adjust the display method of the analysis results.

[0090] The analysis unit can determine the priority of analysis based on the submission date of the collected information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent reviews. The analysis unit can also prioritize the latest information while referring to older reviews. The analysis unit can evaluate the reliability of the information based on the submission date and determine the priority of analysis. This allows for the prioritization of the latest information by determining the priority of analysis based on the submission date of the collected information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission dates of the collected information into a generating AI, which can then evaluate the submission dates and determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the collected information during analysis. For example, the analysis unit can prioritize the analysis of information most relevant to the user's needs. The analysis unit can postpone the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows the analysis unit to prioritize the analysis of information most relevant to the user's needs by adjusting the order of analysis based on the relevance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the collected information into a generating AI, have the generating AI evaluate the relevance, and adjust the order of analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the way suggestions are presented.

[0093] The suggestion function can adjust the level of detail in a suggestion based on the importance of the product. For example, the suggestion function can provide detailed information for expensive products, while providing concise information for everyday products. The suggestion function can also adjust the level of detail in a suggestion based on the user's level of interest. This allows the suggestion function to provide the user with the most relevant information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of the product into a generating AI, which can then adjust the level of detail in the suggestion.

[0094] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm based on performance evaluation to home appliances. For fashion items, it can apply a suggestion algorithm based on design and trends. For food products, it can apply a suggestion algorithm based on taste and nutritional value. By applying different suggestion algorithms depending on the product category, the unit can provide the most suitable suggestions for each category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input different suggestion algorithms for each product category into a generating AI, and the generating AI can then make suggestions.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. By adjusting the length of the suggestion based on the user's emotions, the suggestion unit can provide the most suitable suggestion for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the suggestion.

[0096] The proposal department can determine the priority of proposals based on the product submission date when submitting a proposal. For example, the proposal department may prioritize the newest products. The proposal department can also prioritize the newest products while referring to older products. The proposal department can evaluate the reliability of products based on the submission date and determine the priority of proposals. This allows for the provision of the latest information preferentially by prioritizing proposals based on the product submission date. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the product submission dates into a generating AI, which can then evaluate the submission dates and determine the priority of proposals.

[0097] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit can prioritize suggesting products that are most relevant to the user's needs. The suggestion unit can also postpone suggesting products that are less relevant. The suggestion unit can dynamically adjust the order of suggestions based on the relevance of the products. This allows the suggestion unit to prioritize suggesting products that are most relevant to the user's needs by adjusting the order of suggestions based on the relevance of the products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of products into a generating AI, which can then evaluate the relevance and adjust the order of suggestions.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-understand analysis method. If the user is relaxed, the analysis unit can provide an analysis method that includes detailed information. If the user is in a hurry, the analysis unit can provide a concise analysis method. By adjusting the analysis method based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and adjust the analysis method.

[0099] The analysis unit can improve the accuracy of its analysis by referring to past evaluations of the proposed product during the analysis process. For example, the analysis unit can analyze by referring to past evaluations of highly-rated products. The analysis unit can analyze by referring to past evaluations of low-rated products. The analysis unit can analyze by referring to past evaluations of products supported by many users. In this way, the accuracy of the analysis can be improved by referring to past evaluations of the proposed product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past evaluations of the proposed product into a generating AI, and the generating AI can perform the analysis by referring to past evaluations.

[0100] The analysis unit can perform analysis based on the usage status of the proposed product. For example, the analysis unit can analyze based on the actual usage status of the product used by a user. The analysis unit can also analyze based on the usage status of the product used by other users. The analysis unit can select the optimal analysis method based on the usage status of the proposed product. This allows the analysis unit to provide the user with the most optimal analysis results by performing the analysis based on the usage status of the proposed product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the usage status of the proposed product into a generating AI, and the generating AI can analyze the usage status.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and adjust the display method of the analysis results.

[0102] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the proposed product during the analysis process. For example, the analysis unit can perform a detailed analysis based on the relevant literature on the proposed product. The analysis unit can provide reliable information by referring to relevant literature on the proposed product. The analysis unit can provide information that meets the user's needs based on the relevant literature on the proposed product. This allows the accuracy of the analysis to be improved by referring to relevant literature on the proposed product. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature on the proposed product into a generating AI, and the generating AI can refer to the relevant literature and perform the analysis.

[0103] The analysis unit can perform analysis while considering the geographical distribution of the proposed products. For example, the analysis unit can provide evaluations for each region based on the geographical distribution of the proposed products. The analysis unit can provide information that is tailored to the needs of each region, taking into account the geographical distribution of the proposed products. The analysis unit can select the optimal analysis method based on the geographical distribution of the proposed products. This allows for the provision of information tailored to the needs of each region by considering the geographical distribution of the proposed products. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of the proposed products into a generating AI, and the generating AI can perform analysis while considering the geographical distribution.

[0104] The dialogue unit can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated emotions. For example, if the user is nervous, the dialogue unit can proceed in a calm tone. If the user is relaxed, the dialogue unit can proceed in a friendly tone. If the user is in a hurry, the dialogue unit can proceed quickly. In this way, by adjusting the way the dialogue proceeds based on the user's emotions, the dialogue can provide the user with the most optimal experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the way the dialogue proceeds.

[0105] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. The dialogue unit can provide a dialogue method that suits the user's preferences by referring to the user's past dialogue history. The dialogue unit can select a dialogue method that suits the user's needs based on the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI, and the generating AI can select the optimal dialogue method.

[0106] The dialogue unit can customize the dialogue content based on the user's current areas of interest during a conversation. For example, the dialogue unit can customize the dialogue content based on the user's current areas of interest. The dialogue unit can provide relevant information considering the user's current areas of interest. The dialogue unit can select the optimal dialogue method based on the user's current areas of interest. This allows the dialogue unit to provide the most suitable conversation for the user by customizing the dialogue content based on the user's current areas of interest. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's current areas of interest into a generating AI, and the generating AI can customize the dialogue content.

[0107] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is nervous, the dialogue unit may set a high priority for the dialogue. If the user is relaxed, the dialogue unit may set a low priority for the dialogue. If the user is in a hurry, the dialogue unit may set a high priority for the dialogue. In this way, by determining the priority of the dialogue based on the user's emotions, the dialogue unit can provide the user with the most optimal dialogue. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the dialogue.

[0108] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's device information. For example, if the user is using a smartphone, the dialogue unit can provide a dialogue method adapted to the screen size. If the user is using a tablet, the dialogue unit can provide a dialogue method optimized for a larger screen. If the user is using a smartwatch, the dialogue unit can provide a concise and highly visible dialogue method. In this way, the optimal dialogue method can be provided by taking into account the user's device information. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's device information into a generating AI, and the generating AI can select the optimal dialogue method.

[0109] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can suggest dialogue content related to products the user has "liked" on social media. The dialogue unit can suggest dialogue content related to products recommended by influencers the user follows. The dialogue unit can suggest dialogue content related to products the user has shared on social media. In this way, relevant dialogue content can be provided by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's social media activity into a generating AI, and the generating AI can suggest dialogue content.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the way suggestions are presented.

[0112] The data collection unit can prioritize collecting highly relevant information by referring to the user's past purchase history. For example, the data collection unit can prioritize collecting reviews of products similar to those the user has previously purchased. The data collection unit can prioritize collecting reviews of products that the user has previously given high ratings to. The data collection unit can prioritize collecting reviews of related products based on the categories of products the user has previously purchased. In this way, highly relevant information can be prioritized by referring to the user's past purchase history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history into a generating AI, and the generating AI can collect highly relevant information.

[0113] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can improve the accuracy of the analysis and provide reliable information. If the user is relaxed, the analysis unit can perform a detailed analysis and provide deeper insights. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise information. By adjusting the accuracy of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, have the generative AI estimate the emotions, and adjust the accuracy of the analysis.

[0114] The suggestion function can adjust the level of detail in a suggestion based on the importance of the product. For example, the suggestion function can provide detailed information for expensive products, while providing concise information for everyday products. The suggestion function can also adjust the level of detail in a suggestion based on the user's level of interest. This allows the suggestion function to provide the user with the most relevant information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the importance of the product into a generating AI, which can then adjust the level of detail in the suggestion.

[0115] The data collection unit can estimate the user's emotions and adjust the amount of information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the amount of information and collect concise reviews. If the user is relaxed, the data collection unit can collect more detailed reviews. If the user is in a hurry, the data collection unit can collect short, to-the-point reviews. This allows the system to provide the user with the optimal amount of information by adjusting the amount of information collected based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the amount of information collected.

[0116] The analysis unit can evaluate the reliability of the collected information during analysis and prioritize the analysis of highly reliable information. For example, the analysis unit can prioritize the analysis of highly rated reviews. The analysis unit can prioritize the analysis of information from reliable sources. The analysis unit can prioritize the analysis of information supported by many users. In this way, by evaluating the reliability of the collected information, highly reliable information can be prioritized for analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the collected information into a generating AI, have the generating AI evaluate the reliability, and prioritize the analysis of highly reliable information.

[0117] The dialogue unit can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated emotions. For example, if the user is nervous, the dialogue unit can proceed in a calm tone. If the user is relaxed, the dialogue unit can proceed in a friendly tone. If the user is in a hurry, the dialogue unit can proceed quickly. In this way, by adjusting the way the dialogue proceeds based on the user's emotions, the dialogue can provide the user with the most optimal experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, or not using AI. For example, the dialogue unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the way the dialogue proceeds.

[0118] The dialogue unit can select the optimal dialogue method by referring to the user's past dialogue history during a conversation. For example, the dialogue unit can select the optimal dialogue method based on the user's past dialogue history. The dialogue unit can provide a dialogue method that suits the user's preferences by referring to the user's past dialogue history. The dialogue unit can select a dialogue method that suits the user's needs based on the user's past dialogue history. In this way, the optimal dialogue method can be provided by referring to the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without using AI. For example, the dialogue unit can input the user's past dialogue history into a generating AI, and the generating AI can select the optimal dialogue method.

[0119] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply a suggestion algorithm based on performance evaluation to home appliances. For fashion items, it can apply a suggestion algorithm based on design and trends. For food products, it can apply a suggestion algorithm based on taste and nutritional value. By applying different suggestion algorithms depending on the product category, the unit can provide the most suitable suggestions for each category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input different suggestion algorithms for each product category into a generating AI, and the generating AI can then make suggestions.

[0120] The data collection unit can estimate the user's emotions and determine the priority of reviews and product descriptions to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting positive reviews to alleviate the user's mood. If the user is relaxed, the data collection unit can prioritize collecting detailed product descriptions to help the user understand them more deeply. If the user is in a hurry, the data collection unit can prioritize collecting concise reviews to provide information quickly. This allows the system to provide the user with the most relevant information by prioritizing the information to collect based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of the information to collect.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection team gathers information about the product. For example, they collect online reviews, product descriptions, user reviews, expert reviews, blog posts, official manufacturer descriptions, sales site descriptions, and user reviews. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected information to understand the characteristics and evaluations of each product and identify the product's functions, design, performance, etc. Evaluations can also be performed using criteria such as star ratings, point ratings, and comment ratings. Step 3: The proposal department proposes products that meet the user's needs based on the information analyzed by the analysis department. For example, based on the user's profile, the proposal is made based on information such as the user's age, gender, interests, and purchase history. Step 4: The analysis department analyzes the reasons and problem-solving methods for the products proposed by the proposal department. For example, the analysis is based on the characteristics of the proposed product, user needs, and past purchase history. Step 5: The dialogue section allows users to add requests in a chat format. For example, requests can be added via text chat, voice chat, video chat, etc.

[0123] 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.

[0124] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, analysis unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects product information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes products that meet the user's needs based on the analyzed information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the reasons for the proposed products and how to solve problems. The dialogue unit is implemented in the specific processing unit 46A of the smart device 14 and allows the user to add requests in a chat dialogue format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, analysis unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects product information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes products that meet the user's needs based on the analyzed information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the reasons for the proposed products and how to solve problems. The dialogue unit is implemented in the specific processing unit 46A of the smart glasses 214 and allows the user to add requests in a chat dialogue format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, analysis unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects product information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected information to the data processing unit 12 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes products that meet the user's needs based on the analyzed information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the reasons for the proposed products and how to solve problems. The dialogue unit is implemented in the specific processing unit 46A of the headset terminal 314 and allows the user to add requests in a chat dialogue format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0164] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] 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.

[0166] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] 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.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, analysis unit, and dialogue unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects information about products using the camera 42 and microphone 238 of the robot 414, and the control unit 46A transmits the collected information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes products that meet the user's needs based on the analyzed information. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the reasons for the proposed products and how to solve problems. The dialogue unit is implemented in the control unit 46A of the robot 414 and allows the user to add requests in a chat dialogue format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] Figure 9 shows the 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.

[0178] 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.

[0179] 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.

[0180] 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, and motorcycles, 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 based, for example, 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.

[0181] 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."

[0182] 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.

[0183] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] 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 other things 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.

[0193] 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.

[0194] (Note 1) A collection department that collects information about products, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes products that meet the user's needs. The analysis department analyzes the reasons for the products proposed by the aforementioned proposal department and methods for solving problems, It comprises a dialogue section in which the user adds requests in a chat-like format. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect online reviews and product descriptions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected information to understand the characteristics and evaluations of each product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the user's profile, we suggest products that meet their needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze the reasons behind the proposed product and how to solve the problem. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, Users add requests in a chat-like format. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates user sentiment and determines the priority of reviews and product descriptions to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information by referencing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current areas of interest and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the amount of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the reliability of the collected information is evaluated, and the most reliable information is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the collected information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During the analysis, we improve the accuracy of the analysis by referring to past evaluations of the proposed product. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, the analysis will be conducted based on the usage status of the proposed product. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, we improve the accuracy of the analysis by referring to relevant literature on the proposed products. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, the geographical distribution of the proposed products will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the conversation progresses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned dialogue unit, During a conversation, the system selects the optimal conversation method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned dialogue unit, During conversations, the dialogue content is customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned dialogue unit, During interaction, the system selects the optimal interaction method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information about products, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes products that meet the user's needs. The analysis department analyzes the reasons for the products proposed by the aforementioned proposal department and methods for solving problems, It comprises a dialogue section in which the user adds requests in a chat-like format. A system characterized by the following features.

2. The aforementioned collection unit is Collect online reviews and product descriptions. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected information to understand the characteristics and evaluations of each product. The system according to feature 1.

4. The aforementioned proposal section is, Based on the user's profile, we suggest products that meet their needs. The system according to feature 1.

5. The aforementioned analysis unit is Analyze the reasons behind the proposed product and how to solve the problem. The system according to feature 1.

6. The aforementioned dialogue unit, Users add requests in a chat-like format. The system according to feature 1.

7. The aforementioned collection unit is It estimates user sentiment and determines the priority of reviews and product descriptions to collect based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information by referencing the user's past purchase history. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current areas of interest and lifestyle. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and adjusts the amount of information collected based on those estimated emotions. The system according to feature 1.

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