system

Abstract

The system according to this embodiment aims to provide product information based on user needs and to present solutions to potential problems and desires. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a collection unit, and a solution presentation unit. The reception unit receives user requests. The generation unit analyzes the requests received by the reception unit and generates product information based on the user's needs. The provision unit provides the product information generated by the generation unit to the user. The collection unit collects the user's past conversation history and behavioral data. The solution presentation unit verbalizes the user's potential problems and desires based on the information provided by the provision unit and presents solutions to them.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

[0002] [[ID=I2]]Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for consumers to find the product information they really want among excessive information.

[0005] The system according to the embodiment aims to provide product information based on user needs and present solutions to potential problems and desires.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a collection unit, and a solution presentation unit. The reception unit receives user requests. The generation unit analyzes the requests received by the reception unit and generates product information based on the user's needs. The provision unit provides the product information generated by the generation unit to the user. The collection unit collects the user's past conversation history and behavioral data. The solution presentation unit verbalizes the user's potential problems and desires based on the information provided by the provision unit and presents solutions to them. [Effects of the Invention]

[0007] The system according to this embodiment can provide product information based on user needs and present solutions to potential problems and desires. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 information provision system according to an embodiment of the present invention is a system for solving the problem that consumers are finding it difficult to find products due to excessive advertising in the age of information overload. This information provision system uses a social networking service (SNS) that is used daily and creates a conversational agent using a generative AI. This agent communicates with the user based on various product data and provides the information the user truly wants. First, the user starts a conversation with the agent on the SNS. For example, the user enters a request such as "I want a new smartphone." This request is analyzed by the generative AI, and product information based on the user's needs and preferences is provided. The generative AI recommends the most suitable product based on the user's past conversation history and behavioral data. Next, the agent provides the user with detailed information about the recommended product. For example, it presents the product's specifications, price, reviews, etc. Furthermore, if the user asks additional questions, the generative AI also provides appropriate answers to those questions. This allows the user to find the product that best suits their needs. In addition, the agent verbalizes the user's potential problems and desires and presents solutions to them. For example, if the user has a concern such as "I'm having trouble getting enough exercise lately," the generative AI recommends appropriate fitness products and services to address that concern. This system allows users to find the best product for them without being misled by excessive advertising. Furthermore, conversations with agents make the user's purchasing behavior more efficient and satisfying. The information provision system generates and provides product information based on user requests, enabling users to find the optimal product.

[0029] The information provision system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a collection unit, and a solution presentation unit. The reception unit receives user requests. User requests include, but are not limited to, text format, audio format, and image format. The reception unit analyzes text format requests using natural language processing technology, for example. The reception unit can also convert audio format requests into text using speech recognition technology and analyze them. Furthermore, the reception unit can analyze image format requests using image recognition technology. The generation unit analyzes the requests received by the reception unit using a generation AI and generates product information based on the user's needs. The generation AI recommends the most suitable product based on the user's past conversation history and behavioral data, for example. The generation unit generates relevant product information in response to user requests, for example. The generation unit can also select the most suitable product from among multiple products based on the user's needs and generate information about it. The provision unit provides the product information generated by the generation unit to the user. The provision unit presents the generated product information to the user in text format, for example. Furthermore, the provisioning unit can provide the generated product information to the user in audio format. In addition, the provisioning unit can provide the generated product information to the user in image format. The collection unit collects the user's past conversation history and behavioral data. For example, the collection unit collects the user's past chat logs. The collection unit can also collect the user's past audio recordings. Furthermore, the collection unit can also collect the user's website browsing history and purchase history. The solution presentation unit verbalizes the user's potential problems and desires based on the information provided by the provisioning unit and presents solutions to them. For example, the solution presentation unit presents relevant solutions in response to the user's request. The solution presentation unit can also select the optimal solution from among multiple solutions based on the user's needs and present that information. As a result, the information provision system according to the embodiment generates and provides product information based on the user's request, enabling the user to find the optimal product.

[0030] The reception desk receives user requests. User requests include, but are not limited to, text, audio, and image formats. For example, the reception desk analyzes text requests using natural language processing technology. Natural language processing technology is used to grammatically analyze the text entered by the user and understand its meaning. This allows for an accurate understanding of the user's intent and requests. The reception desk can also convert audio requests into text using speech recognition technology and analyze them. Speech recognition technology converts user speech into text data, making it possible to analyze audio input in the same way as text input. Furthermore, the reception desk can analyze image requests using image recognition technology. Image recognition technology extracts and analyzes specific information from image data, allowing for the acquisition of necessary information from images sent by the user. For example, if a user sends a photo of a product, the reception desk can extract the product name and features from the image to understand the request. This allows the reception desk to accept and appropriately analyze requests in various formats.

[0031] The generation unit uses a generation AI to analyze requests received by the reception unit and generate product information based on user needs. For example, the generation AI recommends the most suitable product based on the user's past conversation history and behavioral data. The generation AI utilizes natural language processing and machine learning technologies to analyze user requests in detail, and generates optimal product information while considering the user's preferences and past behavioral patterns. For example, it identifies products that the user might be interested in based on information about products the user has previously searched for, purchased, and viewed web pages. The generation AI also generates relevant product information in response to user requests. For example, if a user requests "I'm looking for a new smartphone," the generation AI gathers information on the latest smartphones and recommends the most suitable product to the user. Furthermore, the generation unit can select the most suitable product from multiple products based on the user's needs and generate information on that product. This allows the generation unit to provide highly accurate product information that meets user requests.

[0032] The providing unit provides users with product information generated by the generating unit. For example, the providing unit presents the generated product information to the user in text format. The text format information is organized in a way that is easy for the user to understand, and the necessary information is clearly stated. The providing unit can also provide the generated product information to the user in audio format. Audio information is particularly useful for users with visual impairments or those whose hands are occupied. Furthermore, the providing unit can also provide the generated product information to the user in image format. Image information can visually convey the appearance and characteristics of the product, helping the user to visualize the product more concretely. For example, the providing unit can notify the user of the generated product information on their smartphone, making it easily accessible to the user. The providing unit can also customize the method of information delivery according to the user's preferences. This allows the providing unit to provide product information to the user in the most optimal format, improving user convenience.

[0033] The data collection unit collects the user's past conversation history and behavioral data. For example, the data collection unit collects the user's past chat logs. Chat logs contain the content of conversations the user has had in the past, and by analyzing this, the user's preferences and interests can be understood. The data collection unit can also collect the user's past voice recordings. Voice recordings contain the content of what the user has said in the past, and by analyzing this, the user's needs and requests can be understood. Furthermore, the data collection unit can also collect the user's website browsing history and purchase history. Website browsing history contains information on web pages the user has visited in the past, and by analyzing this, the user's interests and preferences can be understood. Purchase history contains information on products the user has purchased in the past, and by analyzing this, the user's purchasing trends can be understood. In this way, the data collection unit can collect a wide range of past behavioral data from the user and understand the user's needs and preferences in detail.

[0034] The solution-providing unit articulates the user's potential challenges and desires based on the information provided by the service provider and presents solutions to them. For example, the solution-providing unit presents relevant solutions to user requests. For instance, if a user requests "I'm looking for a new smartphone," the solution-providing unit selects the most suitable product from several smartphones based on the user's needs and presents that information. The solution-providing unit can also select the most suitable solution from several solutions based on the user's needs and present that information. For example, if a user requests "I'm looking for a travel destination," the solution-providing unit recommends the most suitable travel destination based on the user's preferences and past travel history. Furthermore, the solution-providing unit can collect user feedback and continuously improve the accuracy and effectiveness of its solutions. This allows the solution-providing unit to provide the best possible solutions to user requests and improve user satisfaction.

[0035] The data collection unit can collect the user's past conversation history and behavioral data. For example, the data collection unit can collect the user's past chat logs. The data collection unit can also collect the user's past voice recordings. Furthermore, the data collection unit can collect the user's website browsing history and purchase history. By collecting the user's past conversation history and behavioral data, it is possible to provide more accurate product information. 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 chat logs into AI, which can then analyze the chat logs and extract the necessary data.

[0036] The generation unit can generate product information based on user needs using collected data. For example, the generation unit analyzes collected data and generates product information based on user needs. The generation unit can also use a generation AI to generate product information best suited to user needs. For example, the generation unit inputs collected data into the generation AI, which then generates product information based on user needs. This allows the generation unit to provide product information that meets user needs by generating product information based on collected data. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs collected data into the generation AI, which then generates product information based on user needs.

[0037] The information provider can provide the user with the generated product information. For example, the information provider can present the generated product information to the user in text format. The information provider can also provide the user with the generated product information in audio format. Furthermore, the information provider can provide the user with the generated product information in image format. This allows the user to easily obtain product information by providing the generated product information to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the generated product information into the AI, and the AI ​​can provide the information to the user in an appropriate format.

[0038] The solution presentation unit can articulate the user's potential problems and desires and present solutions to them. For example, the solution presentation unit can present relevant solutions in response to a user's request. Furthermore, based on the user's needs, the solution presentation unit can select the optimal solution from among multiple solutions and present that information. In this way, by articulating the user's potential problems and desires and presenting solutions to them, it assists the user in solving their problems. Some or all of the above-described processes in the solution presentation unit may be performed using AI, or not. For example, the solution presentation unit inputs the user's request into the AI, and the AI ​​generates relevant solutions.

[0039] The reception desk can analyze a user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past request history into the AI, and the AI ​​will select the optimal reception method.

[0040] The reception unit can filter requests based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception unit prioritizes requests related to that situation. The reception unit can also filter and display relevant requests based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate requests based on the user's current activity status (e.g., exercising, working, etc.). This allows for the priority of receiving highly relevant requests by filtering requests based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the user's current situation data into the AI, and the AI ​​filters the relevant requests.

[0041] The reception unit can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize requests related to that region. It can also prioritize requests related to locations close to the user's current location. Furthermore, if the user is traveling, the reception unit can prioritize requests related to their travel destination. This allows for the prioritization of highly relevant requests by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit inputs the user's geographical location into the AI, which then filters the relevant requests.

[0042] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can accept relevant requests based on information the user has shared on social media. It can also analyze the user's social media activity history and suggest relevant requests. Furthermore, the reception unit can accept relevant requests based on the accounts the user follows on social media. This allows for priority acceptance of relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the user's social media activity data into the AI, which then filters out relevant requests.

[0043] The generation unit can adjust the level of detail generated based on the importance of the user's needs when generating product information. For example, if the user prioritizes a particular feature, the generation unit will generate detailed information about that feature. Furthermore, if the user prioritizes price, the generation unit can prioritize generating information about price. Additionally, if the user prioritizes reviews, the generation unit can generate detailed information about reviews. This allows the generation unit to provide users with the necessary information by adjusting the level of detail based on the importance of their needs. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user needs data into the generation AI, which then adjusts the level of detail of the product information based on the importance of the needs.

[0044] The generation unit can apply different generation algorithms depending on the product category when generating product information. For example, in the case of electronic devices, the generation unit can generate detailed information about specifications and functions. It can also generate detailed information about design and materials for fashion items. Furthermore, in the case of food products, it can generate detailed information about ingredients and nutritional value. This allows for the provision of optimal information for each category by applying different generation algorithms depending on the product category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product category data into the generation AI, which then applies a category-appropriate algorithm to generate product information.

[0045] The generation unit can determine the generation priority based on the product submission date when generating product information. For example, if a new product is released, the generation unit will prioritize generating information about that product. The generation unit can also prioritize generating product information during sales periods. Furthermore, the generation unit can prioritize generating information about seasonal or limited-edition products. By determining the generation priority based on the product submission date, it is possible to provide timely information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product submission date data into the generation AI, and the generation AI determines the generation priority based on the submission date.

[0046] The generation unit can adjust the generation order based on product relevance when generating product information. For example, the generation unit can prioritize generating information related to products the user has previously purchased. It can also prioritize generating product information in categories the user is currently interested in. Furthermore, it can prioritize generating product information from brands the user follows. By adjusting the generation order based on product relevance, it can prioritize providing the user with information that is highly relevant to them. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product relevance data into the generation AI, and the generation AI adjusts the generation order based on relevance.

[0047] The information provider can adjust the level of detail provided based on the importance of the user's needs. For example, if a user prioritizes a particular feature, the provider can provide detailed information about that feature. Similarly, if a user prioritizes price, the provider can prioritize price-related information. Furthermore, if a user prioritizes reviews, the provider can provide detailed information about reviews. By adjusting the level of detail based on the importance of the user's needs, the provider can provide the information the user needs. Some or all of the above processing in the information provider may be performed using AI or not. For example, the service provider inputs user needs data into an AI, which then adjusts the level of detail based on the importance of the needs.

[0048] The information provider can provide optimal information based on the user's past behavioral data when providing information. For example, the provider can provide information related to products the user has purchased in the past. It can also provide information related to products the user has searched for in the past. Furthermore, it can provide information related to products the user has viewed in the past. In this way, by providing optimal information based on the user's past behavioral data, it is possible to provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past behavioral data into AI, and the AI ​​can select and provide the optimal information.

[0049] The information provider can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, if the user is in a specific region, the provider can prioritize providing information related to that region. The provider can also prioritize providing information related to locations close to the user's current location. Furthermore, if the user is traveling, the provider can prioritize providing information related to their travel destination. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's geographical location information into the AI, and the AI ​​can select and provide relevant information.

[0050] The information provider can analyze a user's social media activity and provide relevant information when providing information. For example, the provider can provide relevant information based on information shared by the user on social media. The provider can also analyze a user's social media activity history and suggest relevant information. Furthermore, the provider can provide relevant information based on accounts followed by the user on social media. This allows for the priority provision of relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity data into AI, which then selects and provides relevant information.

[0051] The data collection unit can analyze the user's past behavior data to select the optimal data collection method during data collection. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also select the most efficient data collection method from the user's past behavior data. Furthermore, the data collection unit can analyze the user's past behavior data to select the optimal data collection method for a specific time period. In this way, the optimal data collection method can be selected by analyzing the user's past behavior data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input the user's past behavior data into the AI, and the AI ​​may select the optimal data collection method.

[0052] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of data related to locations close to the user's current location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, and the AI ​​will select and collect the relevant data.

[0053] The solution presentation unit can select the optimal solution by analyzing the user's past challenges and desires when presenting a solution. For example, the solution presentation unit can select the optimal solution based on challenges the user has faced in the past. It can also analyze the user's past desires and select a solution based on those desires. Furthermore, the solution presentation unit can select the optimal solution by referring to the user's past behavioral data. In this way, the optimal solution can be selected by analyzing the user's past challenges and desires. Some or all of the above processing in the solution presentation unit may be performed using AI or not. For example, the solution presentation unit inputs the user's past challenge data into the AI, and the AI ​​selects the optimal solution.

[0054] The solution presentation unit can customize solutions based on the user's current situation when presenting solutions. For example, when the user inputs their current situation, the solution presentation unit presents the most suitable solution for that situation. The solution presentation unit can also suggest appropriate solutions based on the user's current activity status (e.g., exercising, working, etc.). Furthermore, the solution presentation unit can customize solutions based on the user's current emotional state. By customizing solutions based on the user's current situation, the solution presentation unit can provide the user with the most suitable solution. Some or all of the above processing in the solution presentation unit may be performed using AI or not. For example, the solution presentation unit inputs the user's current situation data into the AI, and the AI ​​selects and presents the most suitable solution.

[0055] The solution presentation unit can select the optimal solution by considering the user's geographical location information when presenting solutions. For example, if the user is in a specific region, the solution presentation unit will present solutions related to that region. It can also present solutions related to locations close to the user's current location. Furthermore, if the user is traveling, the solution presentation unit can present solutions related to their travel destination. This allows for the provision of highly relevant solutions by considering the user's geographical location information. Some or all of the above processing in the solution presentation unit may be performed using AI or not. For example, the solution presentation unit can input the user's geographical location information into the AI, which will then select and present the optimal solution.

[0056] The solution-providing unit can analyze the user's social media activity and propose solutions when presenting solutions. For example, the solution-providing unit can present relevant solutions based on information shared by the user on social media. It can also analyze the user's social media activity history and propose relevant solutions. Furthermore, the solution-providing unit can present relevant solutions based on the accounts the user follows on social media. In this way, relevant solutions can be provided by analyzing the user's social media activity. Some or all of the above processing in the solution-providing unit may be performed using AI or not. For example, the solution-providing unit inputs the user's social media activity data into AI, and the AI ​​selects and presents relevant solutions.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The information provision system can also provide product information based on the user's hobbies and interests. For example, if a user is interested in music, it can recommend the latest music equipment and related accessories. If a user is interested in cooking, it can provide the latest kitchen gadgets and recipe books. Furthermore, if a user is interested in travel, it can recommend recommended travel destinations and travel goods. By providing product information based on the user's hobbies and interests, it can increase user satisfaction.

[0059] The information provision system can also analyze users' purchase history and provide product information to encourage repeat purchases. For example, it can predict when a user will need a previously purchased product again and send a reminder at that time. Furthermore, if a user prefers a particular brand, it can prioritize providing information on new products and sales from that brand. It can also recommend similar products based on reviews of products the user has previously purchased. This improves user convenience by encouraging repeat purchases based on the user's purchase history.

[0060] The information provision system can also utilize the user's geographical location to provide region-specific benefits and sales information. For example, if a user is in a specific region, it can provide information on sales and events being held in that region. If a user is traveling, it can also provide information on benefits and discounts available at their travel destination. Furthermore, by providing information on benefits at stores near the user's current location, it can increase the user's motivation to visit those stores. In this way, by utilizing the user's geographical location information, it is possible to provide region-specific benefits and sales information and increase the user's purchasing intent.

[0061] The information provision system can also analyze users' social media activity and provide trend-based product information. For example, it can recommend products featured by influencers that users follow. It can also provide products that are trending in communities that users participate in. Furthermore, it can attract users' interest by providing product information related to posts that users have shared. In this way, by analyzing users' social media activity, it can provide trend-based product information and attract users' interest.

[0062] The information provision system can also predict user purchasing behavior and provide product information based on those predictions. For example, if a user tends to purchase certain products during a particular season, the system can provide product information tailored to that season. Similarly, if a user purchases products related to a specific event (e.g., a birthday or anniversary), the system can provide product information tailored to that event. Furthermore, it can predict when users will need consumables for products they have previously purchased and send reminders at that time. By predicting user purchasing behavior, the system can provide timely product information and improve user convenience.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives user requests. User requests can be in text format, audio format, image format, etc. The reception desk can analyze text-format requests using natural language processing technology, convert audio-format requests to text using speech recognition technology and then analyze them. It can also analyze image-format requests using image recognition technology. Step 2: The generation unit analyzes the request received by the reception unit and generates product information based on the user's needs. Using the generation AI, it recommends the most suitable product based on the user's past conversation history and behavioral data. The generation unit can also generate relevant product information in response to the user's request and select the most suitable product from among multiple products. Step 3: The providing unit provides the user with the product information generated by the generating unit. The providing unit can present the generated product information to the user in text, audio, or image format. Step 4: The collection unit collects the user's past conversation history and behavioral data. The collection unit can collect the user's past chat logs, voice recordings, website browsing history, and purchase history. Step 5: The solution presentation unit articulates the user's potential challenges and needs based on the information provided by the service provider unit, and presents solutions to address them. The solution presentation unit can present relevant solutions to the user's requests and select the optimal solution from among multiple options.

[0065] (Example of form 2) The information provision system according to an embodiment of the present invention is a system for solving the problem that consumers are finding it difficult to find products due to excessive advertising in the age of information overload. This information provision system uses a social networking service (SNS) that is used daily and creates a conversational agent using a generative AI. This agent communicates with the user based on various product data and provides the information the user truly wants. First, the user starts a conversation with the agent on the SNS. For example, the user enters a request such as "I want a new smartphone." This request is analyzed by the generative AI, and product information based on the user's needs and preferences is provided. The generative AI recommends the most suitable product based on the user's past conversation history and behavioral data. Next, the agent provides the user with detailed information about the recommended product. For example, it presents the product's specifications, price, reviews, etc. Furthermore, if the user asks additional questions, the generative AI also provides appropriate answers to those questions. This allows the user to find the product that best suits their needs. In addition, the agent verbalizes the user's potential problems and desires and presents solutions to them. For example, if the user has a concern such as "I'm having trouble getting enough exercise lately," the generative AI recommends appropriate fitness products and services to address that concern. This system allows users to find the best product for them without being misled by excessive advertising. Furthermore, conversations with agents make the user's purchasing behavior more efficient and satisfying. The information provision system generates and provides product information based on user requests, enabling users to find the optimal product.

[0066] The information provision system according to this embodiment comprises a reception unit, a generation unit, a provision unit, a collection unit, and a solution presentation unit. The reception unit receives user requests. User requests include, but are not limited to, text format, audio format, and image format. The reception unit analyzes text format requests using natural language processing technology, for example. The reception unit can also convert audio format requests into text using speech recognition technology and analyze them. Furthermore, the reception unit can analyze image format requests using image recognition technology. The generation unit analyzes the requests received by the reception unit using a generation AI and generates product information based on the user's needs. The generation AI recommends the most suitable product based on the user's past conversation history and behavioral data, for example. The generation unit generates relevant product information in response to user requests, for example. The generation unit can also select the most suitable product from among multiple products based on the user's needs and generate information about it. The provision unit provides the product information generated by the generation unit to the user. The provision unit presents the generated product information to the user in text format, for example. Furthermore, the provisioning unit can provide the generated product information to the user in audio format. In addition, the provisioning unit can provide the generated product information to the user in image format. The collection unit collects the user's past conversation history and behavioral data. For example, the collection unit collects the user's past chat logs. The collection unit can also collect the user's past audio recordings. Furthermore, the collection unit can also collect the user's website browsing history and purchase history. The solution presentation unit verbalizes the user's potential problems and desires based on the information provided by the provisioning unit and presents solutions to them. For example, the solution presentation unit presents relevant solutions in response to the user's request. The solution presentation unit can also select the optimal solution from among multiple solutions based on the user's needs and present that information. As a result, the information provision system according to the embodiment generates and provides product information based on the user's request, enabling the user to find the optimal product.

[0067] The reception desk receives user requests. User requests include, but are not limited to, text, audio, and image formats. For example, the reception desk analyzes text requests using natural language processing technology. Natural language processing technology is used to grammatically analyze the text entered by the user and understand its meaning. This allows for an accurate understanding of the user's intent and requests. The reception desk can also convert audio requests into text using speech recognition technology and analyze them. Speech recognition technology converts user speech into text data, making it possible to analyze audio input in the same way as text input. Furthermore, the reception desk can analyze image requests using image recognition technology. Image recognition technology extracts and analyzes specific information from image data, allowing for the acquisition of necessary information from images sent by the user. For example, if a user sends a photo of a product, the reception desk can extract the product name and features from the image to understand the request. This allows the reception desk to accept and appropriately analyze requests in various formats.

[0068] The generation unit uses a generation AI to analyze requests received by the reception unit and generate product information based on user needs. For example, the generation AI recommends the most suitable product based on the user's past conversation history and behavioral data. The generation AI utilizes natural language processing and machine learning technologies to analyze user requests in detail, and generates optimal product information while considering the user's preferences and past behavioral patterns. For example, it identifies products that the user might be interested in based on information about products the user has previously searched for, purchased, and viewed web pages. The generation AI also generates relevant product information in response to user requests. For example, if a user requests "I'm looking for a new smartphone," the generation AI gathers information on the latest smartphones and recommends the most suitable product to the user. Furthermore, the generation unit can select the most suitable product from multiple products based on the user's needs and generate information on that product. This allows the generation unit to provide highly accurate product information that meets user requests.

[0069] The providing unit provides users with product information generated by the generating unit. For example, the providing unit presents the generated product information to the user in text format. The text format information is organized in a way that is easy for the user to understand, and the necessary information is clearly stated. The providing unit can also provide the generated product information to the user in audio format. Audio information is particularly useful for users with visual impairments or those whose hands are occupied. Furthermore, the providing unit can also provide the generated product information to the user in image format. Image information can visually convey the appearance and characteristics of the product, helping the user to visualize the product more concretely. For example, the providing unit can notify the user of the generated product information on their smartphone, making it easily accessible to the user. The providing unit can also customize the method of information delivery according to the user's preferences. This allows the providing unit to provide product information to the user in the most optimal format, improving user convenience.

[0070] The data collection unit collects the user's past conversation history and behavioral data. For example, the data collection unit collects the user's past chat logs. Chat logs contain the content of conversations the user has had in the past, and by analyzing this, the user's preferences and interests can be understood. The data collection unit can also collect the user's past voice recordings. Voice recordings contain the content of what the user has said in the past, and by analyzing this, the user's needs and requests can be understood. Furthermore, the data collection unit can also collect the user's website browsing history and purchase history. Website browsing history contains information on web pages the user has visited in the past, and by analyzing this, the user's interests and preferences can be understood. Purchase history contains information on products the user has purchased in the past, and by analyzing this, the user's purchasing trends can be understood. In this way, the data collection unit can collect a wide range of past behavioral data from the user and understand the user's needs and preferences in detail.

[0071] The solution-providing unit articulates the user's potential challenges and desires based on the information provided by the service provider and presents solutions to them. For example, the solution-providing unit presents relevant solutions to user requests. For instance, if a user requests "I'm looking for a new smartphone," the solution-providing unit selects the most suitable product from several smartphones based on the user's needs and presents that information. The solution-providing unit can also select the most suitable solution from several solutions based on the user's needs and present that information. For example, if a user requests "I'm looking for a travel destination," the solution-providing unit recommends the most suitable travel destination based on the user's preferences and past travel history. Furthermore, the solution-providing unit can collect user feedback and continuously improve the accuracy and effectiveness of its solutions. This allows the solution-providing unit to provide the best possible solutions to user requests and improve user satisfaction.

[0072] The data collection unit can collect the user's past conversation history and behavioral data. For example, the data collection unit can collect the user's past chat logs. The data collection unit can also collect the user's past voice recordings. Furthermore, the data collection unit can collect the user's website browsing history and purchase history. By collecting the user's past conversation history and behavioral data, it is possible to provide more accurate product information. 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 chat logs into AI, which can then analyze the chat logs and extract the necessary data.

[0073] The generation unit can generate product information based on user needs using collected data. For example, the generation unit analyzes collected data and generates product information based on user needs. The generation unit can also use a generation AI to generate product information best suited to user needs. For example, the generation unit inputs collected data into the generation AI, which then generates product information based on user needs. This allows the generation unit to provide product information that meets user needs by generating product information based on collected data. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs collected data into the generation AI, which then generates product information based on user needs.

[0074] The information provider can provide the user with the generated product information. For example, the information provider can present the generated product information to the user in text format. The information provider can also provide the user with the generated product information in audio format. Furthermore, the information provider can provide the user with the generated product information in image format. This allows the user to easily obtain product information by providing the generated product information to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the generated product information into the AI, and the AI ​​can provide the information to the user in an appropriate format.

[0075] The solution presentation unit can articulate the user's potential problems and desires and present solutions to them. For example, the solution presentation unit can present relevant solutions in response to a user's request. Furthermore, based on the user's needs, the solution presentation unit can select the optimal solution from among multiple solutions and present that information. In this way, by articulating the user's potential problems and desires and presenting solutions to them, it assists the user in solving their problems. Some or all of the above-described processes in the solution presentation unit may be performed using AI, or not. For example, the solution presentation unit inputs the user's request into the AI, and the AI ​​generates relevant solutions.

[0076] The reception desk can estimate the user's emotions and adjust how requests are processed based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to process the request quickly. This allows for appropriate responses tailored to the user's situation by adjusting how requests are processed based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs user emotion data into an AI, which analyzes the emotions and adjusts how requests are processed.

[0077] The reception desk can analyze a user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past request history into the AI, and the AI ​​will select the optimal reception method.

[0078] The reception unit can filter requests based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception unit prioritizes requests related to that situation. The reception unit can also filter and display relevant requests based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate requests based on the user's current activity status (e.g., exercising, working, etc.). This allows for the priority of receiving highly relevant requests by filtering requests based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the user's current situation data into the AI, and the AI ​​filters the relevant requests.

[0079] The reception desk can estimate the user's emotions and determine the priority of requests based on the estimated emotions. For example, if a user has an urgent request, the reception desk will prioritize that request. If the user is relaxed, the reception desk can also prioritize requests for stress reduction. Furthermore, if the user is stressed, the reception desk can prioritize requests to alleviate that stress. This allows for the priority processing of urgent requests by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs user emotion data into an AI, which analyzes the emotions and determines the priority of requests.

[0080] The reception unit can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize requests related to that region. It can also prioritize requests related to locations close to the user's current location. Furthermore, if the user is traveling, the reception unit can prioritize requests related to their travel destination. This allows for the prioritization of highly relevant requests by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit inputs the user's geographical location into the AI, which then filters the relevant requests.

[0081] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can accept relevant requests based on information the user has shared on social media. It can also analyze the user's social media activity history and suggest relevant requests. Furthermore, the reception unit can accept relevant requests based on the accounts the user follows on social media. This allows for priority acceptance of relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the user's social media activity data into the AI, which then filters out relevant requests.

[0082] The generation unit can estimate the user's emotions and adjust the way product information is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate information including detailed product descriptions. If the user is in a hurry, the generation unit can also generate concise and to-the-point product information. Furthermore, if the user is excited, the generation unit can generate visually appealing product information. This allows for the provision of easily understandable information by adjusting the presentation of product information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user emotion data into the generation AI, which then adjusts the presentation of product information based on the emotions.

[0083] The generation unit can adjust the level of detail generated based on the importance of the user's needs when generating product information. For example, if the user prioritizes a particular feature, the generation unit will generate detailed information about that feature. Furthermore, if the user prioritizes price, the generation unit can prioritize generating information about price. Additionally, if the user prioritizes reviews, the generation unit can generate detailed information about reviews. This allows the generation unit to provide users with the necessary information by adjusting the level of detail based on the importance of their needs. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs user needs data into the generation AI, which then adjusts the level of detail of the product information based on the importance of the needs.

[0084] The generation unit can apply different generation algorithms depending on the product category when generating product information. For example, in the case of electronic devices, the generation unit can generate detailed information about specifications and functions. It can also generate detailed information about design and materials for fashion items. Furthermore, in the case of food products, it can generate detailed information about ingredients and nutritional value. This allows for the provision of optimal information for each category by applying different generation algorithms depending on the product category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product category data into the generation AI, which then applies a category-appropriate algorithm to generate product information.

[0085] The generation unit can estimate the user's emotions and adjust the length of the product information it generates based on those emotions. For example, if the user is in a hurry, the generation unit can generate short, concise product information. If the user is relaxed, the generation unit can also generate longer product information with detailed descriptions. Furthermore, if the user is excited, the generation unit can generate product information with visually stimulating effects. By adjusting the length of the product information based on the user's emotions, the system can provide the user with an appropriate amount of information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, which then adjusts the length of the product information based on the emotion.

[0086] The generation unit can determine the generation priority based on the product submission date when generating product information. For example, if a new product is released, the generation unit will prioritize generating information about that product. The generation unit can also prioritize generating product information during sales periods. Furthermore, the generation unit can prioritize generating information about seasonal or limited-edition products. By determining the generation priority based on the product submission date, it is possible to provide timely information. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product submission date data into the generation AI, and the generation AI determines the generation priority based on the submission date.

[0087] The generation unit can adjust the generation order based on product relevance when generating product information. For example, the generation unit can prioritize generating information related to products the user has previously purchased. It can also prioritize generating product information in categories the user is currently interested in. Furthermore, it can prioritize generating product information from brands the user follows. By adjusting the generation order based on product relevance, it can prioritize providing the user with information that is highly relevant to them. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs product relevance data into the generation AI, and the generation AI adjusts the generation order based on relevance.

[0088] The service provider can estimate the user's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the user is relaxed, the service provider can provide information including detailed product descriptions. If the user is in a hurry, the service provider can also provide concise and to-the-point information. Furthermore, if the user is excited, the service provider can provide visually appealing information. By adjusting the presentation of information based on the user's emotions, the service provider can provide information that is easy for the user to understand. 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 service provider may be performed using AI or not. For example, the service provider inputs user emotion data into the AI, which analyzes the emotions and adjusts the presentation of the information.

[0089] The information provider can adjust the level of detail provided based on the importance of the user's needs. For example, if a user prioritizes a particular feature, the provider can provide detailed information about that feature. Similarly, if a user prioritizes price, the provider can prioritize price-related information. Furthermore, if a user prioritizes reviews, the provider can provide detailed information about reviews. By adjusting the level of detail based on the importance of the user's needs, the provider can provide the information the user needs. Some or all of the above processing in the information provider may be performed using AI or not. For example, the service provider inputs user needs data into an AI, which then adjusts the level of detail based on the importance of the needs.

[0090] The information provider can provide optimal information based on the user's past behavioral data when providing information. For example, the provider can provide information related to products the user has purchased in the past. It can also provide information related to products the user has searched for in the past. Furthermore, it can provide information related to products the user has viewed in the past. In this way, by providing optimal information based on the user's past behavioral data, it is possible to provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past behavioral data into AI, and the AI ​​can select and provide the optimal information.

[0091] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user has an urgent request, the service provider will provide that information with the highest priority. The service provider can also provide information with normal priority if the user is relaxed. Furthermore, if the service provider is stressed, it can prioritize providing information to alleviate stress. This allows for the priority of providing highly urgent information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs user emotion data into an AI, which analyzes the emotions and determines the priority of the information.

[0092] The information provider can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, if the user is in a specific region, the provider can prioritize providing information related to that region. The provider can also prioritize providing information related to locations close to the user's current location. Furthermore, if the user is traveling, the provider can prioritize providing information related to their travel destination. In this way, highly relevant information can be prioritized by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's geographical location information into the AI, and the AI ​​can select and provide relevant information.

[0093] The information provider can analyze a user's social media activity and provide relevant information when providing information. For example, the provider can provide relevant information based on information shared by the user on social media. The provider can also analyze a user's social media activity history and suggest relevant information. Furthermore, the provider can provide relevant information based on accounts followed by the user on social media. This allows for the priority provision of relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity data into AI, which then selects and provides relevant information.

[0094] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect detailed behavioral data. If the user is in a hurry, the data collection unit can also collect concise behavioral data. Furthermore, if the user is excited, the data collection unit can collect visually appealing data. This allows the data necessary for the user to be collected by adjusting the type of data collected based on the user's emotions. 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 data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's emotion data into the AI, and the AI ​​analyzes the emotions and adjusts the type of data to be collected.

[0095] The data collection unit can analyze the user's past behavior data to select the optimal data collection method during data collection. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also select the most efficient data collection method from the user's past behavior data. Furthermore, the data collection unit can analyze the user's past behavior data to select the optimal data collection method for a specific time period. In this way, the optimal data collection method can be selected by analyzing the user's past behavior data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input the user's past behavior data into the AI, and the AI ​​may select the optimal data collection method.

[0096] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user has an urgent request, the data collection unit will prioritize collecting that data. The data collection unit can also collect data with normal priority if the user is relaxed. Furthermore, if the user is stressed, the data collection unit can prioritize collecting data to alleviate stress. This allows for the priority collection of highly urgent data by prioritizing data based on the user's 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 above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs user emotion data into an AI, which analyzes the emotions and determines the data priority.

[0097] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of data related to locations close to the user's current location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, and the AI ​​will select and collect the relevant data.

[0098] The solution presentation unit can estimate the user's emotions and adjust the method of presenting solutions based on the estimated emotions. For example, if the user is relaxed, the solution presentation unit may present a detailed solution. If the user is in a hurry, the solution presentation unit may also present a concise and to-the-point solution. Furthermore, if the user is excited, the solution presentation unit may also present a visually appealing solution. In this way, by adjusting the method of presenting solutions based on the user's emotions, solutions that are easy for the user to understand can be provided. 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 solution presentation unit may be performed using AI or not. For example, the solution presentation unit inputs the user's emotion data into the AI, and the AI ​​analyzes the emotions and adjusts the method of presenting solutions.

[0099] The solution presentation unit can select the optimal solution by analyzing the user's past challenges and desires when presenting a solution. For example, the solution presentation unit can select the optimal solution based on challenges the user has faced in the past. It can also analyze the user's past desires and select a solution based on those desires. Furthermore, the solution presentation unit can select the optimal solution by referring to the user's past behavioral data. In this way, the optimal solution can be selected by analyzing the user's past challenges and desires. Some or all of the above processing in the solution presentation unit may be performed using AI or not. For example, the solution presentation unit inputs the user's past challenge data into the AI, and the AI ​​selects the optimal solution.

[0100] The solution presentation unit can customize solutions based on the user's current situation when presenting solutions. For example, when the user inputs their current situation, the solution presentation unit presents the most suitable solution for that situation. The solution presentation unit can also suggest appropriate solutions based on the user's current activity status (e.g., exercising, working, etc.). Furthermore, the solution presentation unit can customize solutions based on the user's current emotional state. By customizing solutions based on the user's current situation, the solution presentation unit can provide the user with the most suitable solution. Some or all of the above processing in the solution presentation unit may be performed using AI or not. For example, the solution presentation unit inputs the user's current situation data into the AI, and the AI ​​selects and presents the most suitable solution.

[0101] The solution presentation unit can estimate the user's emotions and determine the priority of solutions based on the estimated emotions. For example, if the user has an urgent problem, the solution presentation unit will present that solution as the highest priority. If the user is relaxed, the solution presentation unit can also present solutions with normal priority. Furthermore, if the user is stressed, the solution presentation unit can prioritize solutions to alleviate stress. This allows for the provision of high-priority solutions by prioritizing solutions based on the user's 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 above processing in the solution presentation unit may be performed using AI or not. For example, the solution presentation unit inputs user emotion data into an AI, which analyzes the emotions and determines the priority of solutions.

[0102] The solution presentation unit can select the optimal solution by considering the user's geographical location information when presenting solutions. For example, if the user is in a specific region, the solution presentation unit will present solutions related to that region. It can also present solutions related to locations close to the user's current location. Furthermore, if the user is traveling, the solution presentation unit can present solutions related to their travel destination. This allows for the provision of highly relevant solutions by considering the user's geographical location information. Some or all of the above processing in the solution presentation unit may be performed using AI or not. For example, the solution presentation unit can input the user's geographical location information into the AI, which will then select and present the optimal solution.

[0103] The solution-providing unit can analyze the user's social media activity and propose solutions when presenting solutions. For example, the solution-providing unit can present relevant solutions based on information shared by the user on social media. It can also analyze the user's social media activity history and propose relevant solutions. Furthermore, the solution-providing unit can present relevant solutions based on the accounts the user follows on social media. In this way, relevant solutions can be provided by analyzing the user's social media activity. Some or all of the above processing in the solution-providing unit may be performed using AI or not. For example, the solution-providing unit inputs the user's social media activity data into AI, and the AI ​​selects and presents relevant solutions.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The information provision system can also collect user health data and provide product information based on their health status. For example, it can collect data from users' fitness trackers and smartwatches and analyze their health status. If a user is not getting enough exercise, it can recommend fitness products and services to encourage exercise. If a user is feeling stressed, it can also provide relaxation products and services that help reduce stress. Furthermore, it can analyze users' sleep data and recommend bedding and supplements to improve sleep quality. In this way, by providing optimal product information based on the user's health status, it can improve the user's quality of life.

[0106] The information provision system can also provide product information based on the user's hobbies and interests. For example, if a user is interested in music, it can recommend the latest music equipment and related accessories. If a user is interested in cooking, it can provide the latest kitchen gadgets and recipe books. Furthermore, if a user is interested in travel, it can recommend recommended travel destinations and travel goods. By providing product information based on the user's hobbies and interests, it can increase user satisfaction.

[0107] The information provision system can also analyze users' purchase history and provide product information to encourage repeat purchases. For example, it can predict when a user will need a previously purchased product again and send a reminder at that time. Furthermore, if a user prefers a particular brand, it can prioritize providing information on new products and sales from that brand. It can also recommend similar products based on reviews of products the user has previously purchased. This improves user convenience by encouraging repeat purchases based on the user's purchase history.

[0108] The information delivery system can also estimate the user's emotions and adjust the timing of product information delivery based on those emotions. For example, if a user is stressed, product information can be provided during a time when they can relax. If a user is excited, product information can be provided immediately to maintain that excitement. Furthermore, if a user is relaxed, detailed product information can be provided to allow the user to consider it carefully. In this way, by adjusting the timing of product information delivery based on the user's emotions, the system can improve the user's ability to receive and process the information.

[0109] The information provision system can also utilize the user's geographical location to provide region-specific benefits and sales information. For example, if a user is in a specific region, it can provide information on sales and events being held in that region. If a user is traveling, it can also provide information on benefits and discounts available at their travel destination. Furthermore, by providing information on benefits at stores near the user's current location, it can increase the user's motivation to visit those stores. In this way, by utilizing the user's geographical location information, it is possible to provide region-specific benefits and sales information and increase the user's purchasing intent.

[0110] The information delivery system can also estimate the user's emotions and adjust how product information is presented based on those emotions. For example, if the user is relaxed, it can provide information including detailed product descriptions. If the user is in a hurry, it can provide concise and to-the-point information. Furthermore, if the user is excited, it can provide visually appealing information. By adjusting how product information is presented based on the user's emotions, the system can provide information that is easy for the user to understand.

[0111] The information provision system can also analyze users' social media activity and provide trend-based product information. For example, it can recommend products featured by influencers that users follow. It can also provide products that are trending in communities that users participate in. Furthermore, it can attract users' interest by providing product information related to posts that users have shared. In this way, by analyzing users' social media activity, it can provide trend-based product information and attract users' interest.

[0112] The information delivery system can also estimate the user's emotions and prioritize product information based on those emotions. For example, if a user has an urgent request, that information will be provided with the highest priority. If the user is relaxed, information can be provided with the normal priority. Furthermore, if the user is stressed, information to alleviate stress can be prioritized. In this way, by prioritizing information based on the user's emotions, the system can prioritize the delivery of highly urgent information.

[0113] The information provision system can also predict user purchasing behavior and provide product information based on those predictions. For example, if a user tends to purchase certain products during a particular season, the system can provide product information tailored to that season. Similarly, if a user purchases products related to a specific event (e.g., a birthday or anniversary), the system can provide product information tailored to that event. Furthermore, it can predict when users will need consumables for products they have previously purchased and send reminders at that time. By predicting user purchasing behavior, the system can provide timely product information and improve user convenience.

[0114] The information delivery system can also estimate the user's emotions and adjust the length of product information based on those emotions. For example, if the user is in a hurry, it can provide short, concise product information. If the user is relaxed, it can provide longer product information with detailed descriptions. Furthermore, if the user is excited, it can provide product information with visually stimulating effects. By adjusting the length of product information based on the user's emotions, the system can provide the user with an appropriate amount of information.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives user requests. User requests can be in text format, audio format, image format, etc. The reception desk can analyze text-format requests using natural language processing technology, convert audio-format requests to text using speech recognition technology and then analyze them. It can also analyze image-format requests using image recognition technology. Step 2: The generation unit analyzes the request received by the reception unit and generates product information based on the user's needs. Using the generation AI, it recommends the most suitable product based on the user's past conversation history and behavioral data. The generation unit can also generate relevant product information in response to the user's request and select the most suitable product from among multiple products. Step 3: The providing unit provides the user with the product information generated by the generating unit. The providing unit can present the generated product information to the user in text, audio, or image format. Step 4: The collection unit collects the user's past conversation history and behavioral data. The collection unit can collect the user's past chat logs, voice recordings, website browsing history, and purchase history. Step 5: The solution presentation unit articulates the user's potential challenges and needs based on the information provided by the service provider unit, and presents solutions to address them. The solution presentation unit can present relevant solutions to the user's requests and select the optimal solution from among multiple options.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, collection unit, and solution presentation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates product information based on user needs using generation AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated product information to the user. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects the user's past conversation history and behavioral data. The solution presentation unit is implemented by the specific processing unit 290 of the data processing device 12 and presents solutions to the user's potential problems and desires. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, collection unit, and solution presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates product information based on user needs using generation AI. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated product information to the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the user's past conversation history and behavioral data. The solution presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents solutions to the user's potential problems and desires. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, collection unit, and solution presentation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user requests. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates product information based on user needs using generation AI. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the generated product information to the user. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects the user's past conversation history and behavioral data. The solution presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents solutions to the user's potential problems and desires. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, collection unit, and solution presentation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user requests. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates product information based on user needs using generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated product information to the user. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects the user's past conversation history and behavioral data. The solution presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents solutions to the user's potential problems and desires. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception desk that accepts user requests, A generation unit analyzes the requests received by the reception unit and generates product information based on the user's needs, A providing unit that provides the product information generated by the generation unit to the user, A collection unit that collects the user's past conversation history and behavioral data, The system includes a solution presentation unit that verbalizes the user's potential problems and desires based on the information provided by the aforementioned provision unit and presents solutions to them. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects the user's past conversation history and behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the collected data, we generate product information that is tailored to user needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated product information to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned solution presentation unit, We articulate the user's potential problems and desires, and then propose solutions to address them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts how requests are processed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past request history and select the optimal method of processing requests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a request is received, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate the user's emotions and adjust the way product information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating product information, adjust the level of detail based on the importance of the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating product information, different generation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the product information generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating product information, the generation priority is determined based on the product submission date. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating product information, the generation order is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, we provide the most relevant information based on the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is During data collection, the system analyzes the user's past behavioral data to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned solution presentation unit, It estimates the user's emotions and adjusts how solutions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned solution presentation unit, When presenting solutions, we analyze the user's past challenges and needs to select the most suitable solution. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned solution presentation unit, When presenting a solution, customize the solution based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned solution presentation unit, It estimates the user's emotions and determines the priority of solutions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned solution presentation unit, When presenting solutions, the optimal solution will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned solution presentation unit, When proposing solutions, we analyze the user's social media activity and suggest solutions accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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 reception desk that accepts user requests, A generation unit analyzes the requests received by the reception unit and generates product information based on the user's needs, A providing unit that provides the product information generated by the generation unit to the user, A collection unit that collects the user's past conversation history and behavioral data, The system includes a solution presentation unit that verbalizes the user's potential problems and desires based on the information provided by the aforementioned provision unit, and presents solutions to those problems. A system characterized by the following features.

2. The aforementioned collection unit is Collects the user's past conversation history and behavioral data. The system according to feature 1.

3. The generating unit is Based on the collected data, we generate product information that is tailored to user needs. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated product information to the user. The system according to feature 1.

5. The aforementioned solution presentation unit, We articulate the user's potential problems and desires, and then propose solutions to address them. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts how requests are processed based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past request history and select the optimal method of processing requests. The system according to feature 1.

8. The aforementioned reception unit is When a request is received, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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