system

Abstract

The system according to this embodiment aims to allow users to easily find the interior items they want and to manage prices, inventory, and delivery times. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit receives input from the user, such as photos of the room and the desired atmosphere. The analysis unit analyzes the information received by the reception unit and searches various e-commerce sites. The proposal unit proposes the most suitable furniture and interior based on the information obtained by the analysis unit. The management unit manages the price, inventory, and delivery time of the products proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is cumbersome to search multiple EC sites to find the interior that a user desires and manage prices, inventory, and delivery times.

[0005] The system according to the embodiment aims to easily find the interior that a user desires and manage prices, inventory, and delivery times.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit receives input from the user, such as photos of the room and the desired atmosphere. The analysis unit analyzes the information received by the reception unit and searches various e-commerce sites. The proposal unit proposes the most suitable furniture and interior based on the information obtained by the analysis unit. The management unit manages the price, inventory, and delivery time of the products proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily find the interior items they want and manage prices, inventory, and delivery times. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that provides consistent suggestions from multiple e-commerce sites and freelance agency services. This AI agent system automatically searches various e-commerce sites and suggests the most suitable furniture and interior design items based on the user's input of photos of a room and desired atmosphere. For example, if a user inputs a desire for a Scandinavian-style living room, the AI ​​agent system analyzes this information, searches various e-commerce sites, and finds the most suitable furniture and interior design items. The suggested products also take into account price, availability, and delivery time. For example, suggestions might include, "If this TV stand is sold out, how about this one with a similar feel?", "This rug will be on sale next week," or "Shall we arrange for the lighting and curtains to arrive on the same day?" This allows users to easily experience professional interior design without the hassle of complicated online shopping procedures or budget constraints. Just as the AI ​​agent system suggests the optimal route when a user inputs "I want to go from my home to the train station," room interiors can also be easily coordinated. Furthermore, the AI ​​agent system can also utilize freelance agency services. For example, if a user enters a request such as "I want this furniture assembled," the AI ​​agent system will find a suitable freelancer and handle negotiations and arrangements. This allows the user to realize their ideal room without any hassle. In this way, using the AI ​​agent system, users can easily enjoy a professional interior design experience without being troubled by complicated procedures or budget constraints. For example, it can suggest purchasing an antique table from one e-commerce site and combining it with a cuckoo clock from another e-commerce site. The AI ​​agent system can then suggest the most suitable furniture and interior items based on photos of the user's room and desired atmosphere, and manage prices, inventory, and delivery times.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit receives input from the user, such as photos of a room and the desired atmosphere. The input from the user, such as photos of a room and the desired atmosphere, includes, but is not limited to, styles such as modern, classic, and natural. The reception unit accepts input from the user, such as "I want a Scandinavian-style living room." The analysis unit analyzes the information received by the reception unit and searches various e-commerce sites. The analysis unit uses, for example, AI to find furniture and interior items that match the user's preferences. The proposal unit proposes the most suitable furniture and interior items based on the information obtained by the analysis unit. The proposal unit proposes a combination of products from different sites, such as a TV stand from one e-commerce site, a rug from another e-commerce site, and lighting from yet another e-commerce site. The management unit manages the price, inventory, and delivery time of the products proposed by the proposal unit. The management unit makes proposals, for example, taking into account the price, inventory, and delivery time of the proposed products. As a result, the AI ​​agent system according to this embodiment can suggest the most suitable furniture and interior design based on photos of the user's room and desired atmosphere, and manage prices, inventory, and delivery times.

[0030] The reception desk accepts photos of rooms and inputs of desired atmospheres from users. These inputs may include, but are not limited to, styles such as modern, classic, and natural. Specifically, users can upload photos of their rooms and select their desired interior style using their smartphones or computers. The reception desk provides an interface for receiving these inputs, making it easy for users to operate. For example, when receiving input from a user who wants a "Scandinavian-style living room," the reception desk allows the user to select their desired style using dropdown menus and checkboxes. It also provides a text box where users can freely enter specific requests. Furthermore, the reception desk has the functionality to automatically analyze the photos uploaded by users to understand the room layout and the placement of existing furniture. This allows for the collection of basic data for making suggestions based on the user's preferences. The reception desk centrally manages this data and sends it to the analysis department. This allows the reception desk to accurately understand user needs and support the efficient operation of the entire system.

[0031] The analysis department analyzes the information received by the reception department and searches each e-commerce site. For example, the analysis department uses AI to find furniture and interior items that match the user's preferences. Specifically, the AI ​​analyzes images of the room uploaded by the user to understand the size and shape of the room and the arrangement of existing furniture. It also extracts the characteristics of appropriate furniture and interior items based on the desired atmosphere and style entered by the user. This allows the AI ​​to set criteria for identifying the product that best suits the user's preferences. Next, the AI ​​crawls the database of each e-commerce site and searches for products that match the user's preferences. In this process, the AI ​​analyzes product images, descriptions, reviews, etc., to select the product that is closest to the user's preferences. For example, if a user wants a "Scandinavian-style living room," the AI ​​will prioritize searching for furniture and interior items with Scandinavian designs and create a list of suggested products. Furthermore, the analysis department can learn the user's past purchase history and preferences to provide more personalized suggestions. This allows the analysis department to quickly and accurately find the optimal product based on the user's preferences and provide it to the suggestion department.

[0032] The proposal department proposes optimal furniture and interior items based on information obtained by the analysis department. For example, the proposal department might combine items from different websites, such as a TV stand from one e-commerce site, a rug from another, and lighting from yet another. Specifically, based on the product list provided by the analysis department, the proposal department selects the most suitable items for the user's preferences and proposes a coordinated look. The proposal department considers the design, color, and size of each item to ensure overall balance. Furthermore, the proposal department provides 3D simulations so that users can visually confirm the proposed items. This allows users to visualize how the proposed furniture and interior items would look in their actual room. The proposal department also has a function to accept user feedback and adjust the proposals accordingly. For example, if a user dislikes a particular item, it will offer alternatives. Additionally, the proposal department provides links that allow users to directly purchase the proposed items, simplifying the purchase process. This enables the proposal department to provide optimal coordinated looks based on user preferences, thereby increasing user satisfaction.

[0033] The Management Department manages the prices, inventory, and delivery times of products proposed by the Proposal Department. For example, the Management Department makes proposals considering the price, inventory, and delivery time of the proposed products. Specifically, the Management Department obtains price information and inventory status of products provided by each e-commerce site in real time and reflects this in the proposals. The Management Department also checks the delivery time of products and adjusts proposals to match the delivery date desired by the user. For example, if a user needs a product urgently, the Management Department will prioritize proposing products that are in stock and can be delivered quickly. Furthermore, the Management Department has a function to notify users if the price or inventory status of proposed products changes. This allows users to consider purchases based on the latest information. In addition, the Management Department manages multiple products in a centralized manner, simplifying the process when users purchase multiple products at once. For example, even when purchasing products from multiple e-commerce sites, the Management Department manages everything centrally and provides users with a single purchase procedure. In this way, the Management Department can enhance user convenience and provide a smooth purchasing experience.

[0034] The proposal department can propose combinations of products from different e-commerce sites. For example, the proposal department could combine a TV stand from one e-commerce site, a rug from another, and lighting from yet another e-commerce site. The proposal department could also propose, for example, purchasing an antique table from one e-commerce site and combining it with a cuckoo clock from another e-commerce site. By combining products from different e-commerce sites, the proposal department can provide users with the most suitable interior design.

[0035] The management department can make suggestions considering the price, stock availability, and delivery time of the proposed products. For example, the management department can make suggestions such as, "If this TV stand is sold out, how about this one with a similar feel?", "This rug will be on sale next week," or "Shall we arrange for the lighting and curtains to arrive on the same day?" By considering price, stock availability, and delivery time, the management department can make the best possible suggestions for the user. Specific criteria and methods to consider include, but are not limited to, price limits, stock availability, and delivery dates.

[0036] The reception department may include a scheduling department that arranges freelance services based on user requests. For example, when a user enters a request such as "I want this furniture assembled," the reception department will arrange for a freelance service. The reception department will find a suitable freelancer, negotiate with them, and make the arrangements. This reduces the burden on the user by arranging freelance services based on their requests. Freelance services include, but are not limited to, interior design and furniture assembly.

[0037] The arrangement department can find, negotiate with, and arrange for suitable freelancers based on the user's requests. For example, if a user enters a request such as "I want this furniture assembled," the arrangement department will find a suitable freelancer and handle the negotiation and arrangement. This allows the department to provide services that meet the user's needs by finding, negotiating with, and arranging for suitable freelancers. Suitable freelancers include, but are not limited to, skills, ratings, and fees.

[0038] The management department may include a budget management department that manages users' budgets and adjusts the prices of proposed products to stay within those budgets. For example, the management department manages users' budgets and adjusts the prices of proposed products to stay within those budgets. This allows for proposals that are tailored to the user's budget by managing the user's budget and adjusting the prices of proposed products to stay within those budgets. Budgets include, but are not limited to, the total budget and the budget allocation for each product.

[0039] The reception desk can analyze the user's past interior design preference history and suggest the optimal input method. For example, the reception desk can automatically display relevant options based on the interior design style the user has previously selected. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest an interior design style to be used at a specific time of day based on the user's past preference history. In this way, the optimal input method can be suggested by analyzing the user's past interior design preference history. Past interior design preference history includes, but is not limited to, past purchase history and survey results.

[0040] The reception desk can filter the user's current living situation and areas of interest when they input photos of their room and their desired atmosphere. For example, if the user has pets, the reception desk will prioritize displaying pet-friendly interior options. If the user has children, the reception desk will suggest interior options that take safety into consideration. If the user prefers an eco-friendly lifestyle, the reception desk will suggest environmentally friendly interior options. In this way, by filtering based on the user's current living situation and areas of interest, the reception desk can provide the user with the most suitable interior options. Current living situation includes, but is not limited to, family structure and lifestyle.

[0041] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location when the user inputs photos of their room or their desired atmosphere. For example, if the user lives in a cold climate, the reception desk will prioritize displaying interior options that prioritize warmth. If the user lives in an urban area, the reception desk will suggest space-efficient interior options. If the user lives by the sea, the reception desk will suggest sea-themed interior options. By prioritizing the retrieval of highly relevant information while considering the user's geographical location, the reception desk can provide the user with the most suitable interior options. Geographical location information includes, but is not limited to, GPS data and address information.

[0042] The reception desk can analyze the user's social media activity and obtain relevant information when the user inputs photos of their room and their desired atmosphere. For example, if the reception desk frequently posts about "Scandinavian-style interiors" on social media, it will prioritize displaying Scandinavian-style interior options. If the reception desk frequently posts about "minimalist lifestyles" on social media, it will suggest minimalist interior options. If the reception desk frequently posts about "DIY interiors" on social media, it will suggest DIY interior options. In this way, by analyzing the user's social media activity, it can obtain relevant information and provide the most suitable interior options. Social media activity includes, but is not limited to, the content of posts and the number of followers.

[0043] The analysis unit can optimize its analysis algorithm by referring to the user's past interior preference history during analysis. For example, the analysis unit prioritizes analyzing relevant options based on the interior style the user has previously selected. For example, the analysis unit predicts and analyzes the interior style the user will use at a specific time of day based on the user's past preference history. For example, the analysis unit analyzes the optimal combination of interior items based on the combination of interior items the user has previously selected. In this way, by referring to the user's past interior preference history, the analysis algorithm can be optimized and the best analysis results can be provided. Past interior preference history includes, but is not limited to, past purchase history and survey results.

[0044] The analysis unit can customize the analysis methods based on the user's current living situation. For example, if the user owns a pet, the analysis unit will prioritize analyzing pet-friendly interior options. If the user has children, the analysis unit will analyze interior options that take safety into consideration. If the user prefers an eco-friendly lifestyle, the analysis unit will analyze environmentally friendly interior options. By customizing the analysis methods based on the user's current living situation, the analysis unit can provide the user with the most optimal analysis results. Current living situation includes, but is not limited to, family structure and lifestyle.

[0045] The analysis unit can select the optimal analysis method during analysis, taking into account the user's geographical location information. For example, if the user lives in a cold region, the analysis unit will prioritize analyzing interior options that emphasize warmth. If the user lives in an urban area, the analysis unit will analyze interior options that are space-efficient. If the user lives by the sea, the analysis unit will analyze interior options with a sea theme. By selecting the optimal analysis method considering the user's geographical location information, the analysis unit can provide the user with the most optimal analysis results. Geographical location information includes, but is not limited to, GPS data and address information.

[0046] The analysis unit can analyze a user's social media activity during analysis and propose analysis methods. For example, if a user frequently posts about "Nordic-style interiors" on social media, the analysis unit will prioritize analyzing Nordic-style interior options. For example, if a user frequently posts about "minimalist lifestyles" on social media, the analysis unit will analyze minimalist interior options. For example, if a user frequently posts about "DIY interiors" on social media, the analysis unit will analyze DIY-friendly interior options. In this way, by analyzing a user's social media activity, the optimal analysis method can be proposed. Social media activity includes, but is not limited to, the content of posts and the number of followers.

[0047] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, for expensive products, the proposal department will provide detailed information to ensure user satisfaction. For low-priced products, the proposal department will provide concise information and make a quick proposal. For products of particular interest to the user, the proposal department will provide detailed information to allow comparison with other options. By adjusting the level of detail in a proposal based on the importance of the product, the proposal department can provide the best possible proposal for the user. Product importance includes, but is not limited to, user reviews and sales performance.

[0048] The suggestion function can apply different suggestion algorithms depending on the product category when making suggestions. For example, for the furniture category, the suggestion function will make optimal suggestions based on size and material. For example, for the lighting category, the suggestion function will make optimal suggestions based on brightness and design. For example, for the decoration category, the suggestion function will make optimal suggestions based on theme and color scheme. By applying different suggestion algorithms depending on the product category, the system can provide the user with the most suitable suggestions. Product categories include, but are not limited to, furniture, interior accessories, and lighting.

[0049] The proposal department can prioritize proposals based on the product submission timing. For example, it will prioritize proposals for products that are urgently needed. For example, it will postpone proposals for products that are needed long-term. For example, it will propose products that users need at a specific time, according to that time. By prioritizing proposals based on the product submission timing, the department can provide the best possible proposals for users. Product submission timing includes, but is not limited to, release dates and arrival dates.

[0050] The suggestion department can adjust the order of suggestions based on the relevance of the products. For example, it might prioritize suggesting decorative items related to the furniture selected by the user. For example, it might prioritize suggesting curtains and rugs related to the lighting selected by the user. For example, it might prioritize suggesting cushions and blankets related to the sofa selected by the user. By adjusting the order of suggestions based on the relevance of the products, the department can provide the user with the most suitable suggestions. Product relevance includes, but is not limited to, items of the same brand or category.

[0051] The management department can optimize its management algorithms by referring to the user's past purchase history during management. For example, the management department can propose the optimal management method based on the inventory status of products the user has purchased in the past. For example, the management department can predict and propose the management method for products needed at a specific time period based on the user's past purchase history. For example, the management department can propose the optimal management method based on the combination of products the user has purchased in the past. In this way, by referring to the user's past purchase history, the management algorithm can be optimized and the optimal management method can be provided. Past purchase history includes, but is not limited to, the purchase date, purchased products, and purchase amount.

[0052] The management department can customize management methods based on the user's current living situation during management. For example, if the user owns a pet, the management department will prioritize suggesting pet-friendly management options. For example, if the user has children, the management department will suggest safety-conscious management options. For example, if the user prefers an eco-friendly lifestyle, the management department will suggest environmentally friendly management options. By customizing management methods based on the user's current living situation, the management department can provide the user with the most suitable management method. Current living situation includes, but is not limited to, family structure and lifestyle.

[0053] The management department can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user lives in a cold region, the management department will prioritize suggesting management options that prioritize warmth. For example, if the user lives in an urban area, the management department will suggest space-efficient management options. For example, if the user lives by the sea, the management department will suggest sea-themed management options. By selecting the optimal management method considering the user's geographical location information, the management department can provide the user with the most suitable management method. Geographical location information includes, but is not limited to, GPS data and address information.

[0054] The management department can analyze users' social media activity during management and propose management methods. For example, if a user frequently posts about "Nordic-style interiors" on social media, the management department will prioritize suggesting Nordic-style management options. For example, if a user frequently posts about "minimalist lifestyles" on social media, the management department will propose minimalist management options. For example, if a user frequently posts about "DIY interiors" on social media, the management department will propose DIY-friendly management options. In this way, by analyzing users' social media activity, the management department can propose the most suitable management method. Social media activity includes, but is not limited to, the content of posts and the number of followers.

[0055] The scheduling unit can optimize its scheduling algorithm by referring to the user's past use history of freelance services. For example, the scheduling unit can suggest the most suitable freelancer based on the user's past evaluations of freelancers. For example, the scheduling unit can predict and suggest the necessary scheduling method for a specific time period based on the user's past usage history. For example, the scheduling unit can suggest the most suitable scheduling method based on the skill sets of freelancers the user has used in the past. In this way, by referring to the user's past use history of freelance services, the scheduling algorithm can be optimized and the most suitable scheduling method can be provided. Past use history of freelance services includes, but is not limited to, the date of use, the services used, and evaluations.

[0056] The booking system can select the most suitable booking method by considering the user's geographical location information during the booking process. For example, if the user lives in a cold region, the booking system will prioritize suggesting booking options that prioritize warmth. If the user lives in an urban area, the booking system will suggest space-efficient booking options. If the user lives by the sea, the booking system will suggest sea-themed booking options. By selecting the most suitable booking method by considering the user's geographical location information, the system can provide the user with the most suitable booking method. Geographical location information includes, but is not limited to, GPS data and address information.

[0057] The budget management department can analyze users' past spending habits to select the optimal budget management method during budget management. For example, the budget management department can propose the optimal budget management method based on the prices of products the user has purchased in the past. For example, the budget management department can predict and propose the necessary budget management method for a specific time period based on the user's past spending habits. For example, the budget management department can propose the optimal budget management method based on combinations of products the user has purchased in the past. In this way, by analyzing the user's past spending habits, the optimal budget management method can be selected, and the optimal budget management can be provided to the user. Past spending habits include, but are not limited to, purchase dates, purchased items, and purchase amounts.

[0058] The budget management department can select the optimal budget management method when managing the budget, taking into account the user's geographical location. For example, if the user lives in a cold region, the budget management department will prioritize suggesting budget management options that prioritize warmth. For example, if the user lives in an urban area, the budget management department will suggest budget management options that are space-efficient. For example, if the user lives by the sea, the budget management department will suggest budget management options with a sea theme. By selecting the optimal budget management method considering the user's geographical location, the budget management department can provide the user with the best possible budget management. Geographical location information includes, but is not limited to, GPS data and address information.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can suggest interior styles that match the user's preferences and lifestyle based on the user's input. For example, if a user inputs "I like a natural atmosphere," the reception desk will prioritize suggesting natural-style furniture and interiors. If a user inputs "I have a pet," the reception desk can also suggest furniture made from pet-friendly materials and designs. Furthermore, if a user inputs "I live an eco-friendly lifestyle," the reception desk can suggest environmentally conscious materials and products. This makes it possible to suggest the most suitable interior styles according to the user's lifestyle and preferences.

[0061] The suggestion function can refer to the user's past purchase history based on their input and suggest related products. For example, it can suggest interior items that complement furniture the user has purchased in the past. It can also suggest related products based on the style and color scheme of items the user has purchased in the past. Furthermore, it can suggest high-quality products by referring to the ratings and reviews of items the user has purchased in the past. This enables optimal suggestions based on the user's past purchase history.

[0062] The management department can manage user budgets based on user input and adjust the prices of suggested products to stay within budget. For example, it can select and suggest the most suitable products based on the user's set budget. It can also suggest a balanced combination of high-priced and low-priced products according to the user's budget. Furthermore, it can utilize sales and discount information to provide cost-effective suggestions according to the user's budget. This enables optimal suggestions tailored to the user's budget.

[0063] The scheduling system can arrange the most suitable freelancer based on the user's input, taking into account the user's geographical location. For example, if the user lives in an urban area, it will prioritize arranging a nearby freelancer. If the user lives in a cold region, it can arrange a freelancer with skills suited to cold climates. Furthermore, if the user lives by the sea, it can arrange a freelancer who can provide services appropriate for the seaside environment. This enables the arrangement of the most suitable freelancer based on the user's geographical location.

[0064] The analysis unit can analyze a user's social media activity based on their input and obtain relevant information. For example, if a user frequently posts about "Nordic-style interiors" on social media, it will prioritize analyzing Nordic-style interior options. Similarly, if a user frequently posts about "minimalist lifestyles," it can analyze minimalist interior options. Furthermore, if a user frequently posts about "DIY interiors," it can analyze DIY-friendly interior options. This enables optimal analysis based on the user's social media activity.

[0065] The management department can customize management methods based on user input, taking into account the user's current living situation. For example, if a user owns a pet, pet-friendly management options will be prioritized. Similarly, if a user has children, safety-conscious management options can be suggested. Furthermore, if a user prefers an eco-friendly lifestyle, environmentally friendly management options can be suggested. This enables optimal management tailored to the user's current living situation.

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

[0067] Step 1: The reception desk accepts photos of the room and input of the desired atmosphere from the user. The input of photos of the room and the desired atmosphere from the user may include, but is not limited to, styles such as modern, classic, and natural. The reception desk accepts, for example, input from the user such as "I want a Scandinavian-style living room." Step 2: The analysis unit analyzes the information received by the reception unit and searches each e-commerce site. The analysis unit uses AI, for example, to find furniture and interior items that match the user's preferences. Step 3: The proposal department proposes the most suitable furniture and interior items based on the information obtained by the analysis department. For example, the proposal department may combine products from different websites, such as a TV stand from one e-commerce site, a rug from another e-commerce site, and lighting from yet another e-commerce site. Step 4: The management department manages the price, inventory, and delivery time of the products proposed by the proposal department. For example, the management department makes proposals considering the price, inventory, and delivery time of the proposed products.

[0068] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that provides consistent suggestions from multiple e-commerce sites and freelance agency services. This AI agent system automatically searches various e-commerce sites and suggests the most suitable furniture and interior design items based on the user's input of photos of a room and desired atmosphere. For example, if a user inputs a desire for a Scandinavian-style living room, the AI ​​agent system analyzes this information, searches various e-commerce sites, and finds the most suitable furniture and interior design items. The suggested products also take into account price, availability, and delivery time. For example, suggestions might include, "If this TV stand is sold out, how about this one with a similar feel?", "This rug will be on sale next week," or "Shall we arrange for the lighting and curtains to arrive on the same day?" This allows users to easily experience professional interior design without the hassle of complicated online shopping procedures or budget constraints. Just as the AI ​​agent system suggests the optimal route when a user inputs "I want to go from my home to the train station," room interiors can also be easily coordinated. Furthermore, the AI ​​agent system can also utilize freelance agency services. For example, if a user enters a request such as "I want this furniture assembled," the AI ​​agent system will find a suitable freelancer and handle negotiations and arrangements. This allows the user to realize their ideal room without any hassle. In this way, using the AI ​​agent system, users can easily enjoy a professional interior design experience without being troubled by complicated procedures or budget constraints. For example, it can suggest purchasing an antique table from one e-commerce site and combining it with a cuckoo clock from another e-commerce site. The AI ​​agent system can then suggest the most suitable furniture and interior items based on photos of the user's room and desired atmosphere, and manage prices, inventory, and delivery times.

[0069] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a management unit. The reception unit receives input from the user, such as photos of a room and the desired atmosphere. The input from the user, such as photos of a room and the desired atmosphere, includes, but is not limited to, styles such as modern, classic, and natural. The reception unit accepts input from the user, such as "I want a Scandinavian-style living room." The analysis unit analyzes the information received by the reception unit and searches various e-commerce sites. The analysis unit uses, for example, AI to find furniture and interior items that match the user's preferences. The proposal unit proposes the most suitable furniture and interior items based on the information obtained by the analysis unit. The proposal unit proposes a combination of products from different sites, such as a TV stand from one e-commerce site, a rug from another e-commerce site, and lighting from yet another e-commerce site. The management unit manages the price, inventory, and delivery time of the products proposed by the proposal unit. The management unit makes proposals, for example, taking into account the price, inventory, and delivery time of the proposed products. As a result, the AI ​​agent system according to this embodiment can suggest the most suitable furniture and interior design based on photos of the user's room and desired atmosphere, and manage prices, inventory, and delivery times.

[0070] The reception desk accepts photos of rooms and inputs of desired atmospheres from users. These inputs may include, but are not limited to, styles such as modern, classic, and natural. Specifically, users can upload photos of their rooms and select their desired interior style using their smartphones or computers. The reception desk provides an interface for receiving these inputs, making it easy for users to operate. For example, when receiving input from a user who wants a "Scandinavian-style living room," the reception desk allows the user to select their desired style using dropdown menus and checkboxes. It also provides a text box where users can freely enter specific requests. Furthermore, the reception desk has the functionality to automatically analyze the photos uploaded by users to understand the room layout and the placement of existing furniture. This allows for the collection of basic data for making suggestions based on the user's preferences. The reception desk centrally manages this data and sends it to the analysis department. This allows the reception desk to accurately understand user needs and support the efficient operation of the entire system.

[0071] The analysis department analyzes the information received by the reception department and searches each e-commerce site. For example, the analysis department uses AI to find furniture and interior items that match the user's preferences. Specifically, the AI ​​analyzes images of the room uploaded by the user to understand the size and shape of the room and the arrangement of existing furniture. It also extracts the characteristics of appropriate furniture and interior items based on the desired atmosphere and style entered by the user. This allows the AI ​​to set criteria for identifying the product that best suits the user's preferences. Next, the AI ​​crawls the database of each e-commerce site and searches for products that match the user's preferences. In this process, the AI ​​analyzes product images, descriptions, reviews, etc., to select the product that is closest to the user's preferences. For example, if a user wants a "Scandinavian-style living room," the AI ​​will prioritize searching for furniture and interior items with Scandinavian designs and create a list of suggested products. Furthermore, the analysis department can learn the user's past purchase history and preferences to provide more personalized suggestions. This allows the analysis department to quickly and accurately find the optimal product based on the user's preferences and provide it to the suggestion department.

[0072] The proposal department proposes optimal furniture and interior items based on information obtained by the analysis department. For example, the proposal department might combine items from different websites, such as a TV stand from one e-commerce site, a rug from another, and lighting from yet another. Specifically, based on the product list provided by the analysis department, the proposal department selects the most suitable items for the user's preferences and proposes a coordinated look. The proposal department considers the design, color, and size of each item to ensure overall balance. Furthermore, the proposal department provides 3D simulations so that users can visually confirm the proposed items. This allows users to visualize how the proposed furniture and interior items would look in their actual room. The proposal department also has a function to accept user feedback and adjust the proposals accordingly. For example, if a user dislikes a particular item, it will offer alternatives. Additionally, the proposal department provides links that allow users to directly purchase the proposed items, simplifying the purchase process. This enables the proposal department to provide optimal coordinated looks based on user preferences, thereby increasing user satisfaction.

[0073] The Management Department manages the prices, inventory, and delivery times of products proposed by the Proposal Department. For example, the Management Department makes proposals considering the price, inventory, and delivery time of the proposed products. Specifically, the Management Department obtains price information and inventory status of products provided by each e-commerce site in real time and reflects this in the proposals. The Management Department also checks the delivery time of products and adjusts proposals to match the delivery date desired by the user. For example, if a user needs a product urgently, the Management Department will prioritize proposing products that are in stock and can be delivered quickly. Furthermore, the Management Department has a function to notify users if the price or inventory status of proposed products changes. This allows users to consider purchases based on the latest information. In addition, the Management Department manages multiple products in a centralized manner, simplifying the process when users purchase multiple products at once. For example, even when purchasing products from multiple e-commerce sites, the Management Department manages everything centrally and provides users with a single purchase procedure. In this way, the Management Department can enhance user convenience and provide a smooth purchasing experience.

[0074] The proposal department can propose combinations of products from different e-commerce sites. For example, the proposal department could combine a TV stand from one e-commerce site, a rug from another, and lighting from yet another e-commerce site. The proposal department could also propose, for example, purchasing an antique table from one e-commerce site and combining it with a cuckoo clock from another e-commerce site. By combining products from different e-commerce sites, the proposal department can provide users with the most suitable interior design.

[0075] The management department can make suggestions considering the price, stock availability, and delivery time of the proposed products. For example, the management department can make suggestions such as, "If this TV stand is sold out, how about this one with a similar feel?", "This rug will be on sale next week," or "Shall we arrange for the lighting and curtains to arrive on the same day?" By considering price, stock availability, and delivery time, the management department can make the best possible suggestions for the user. Specific criteria and methods to consider include, but are not limited to, price limits, stock availability, and delivery dates.

[0076] The reception department may include a scheduling department that arranges freelance services based on user requests. For example, when a user enters a request such as "I want this furniture assembled," the reception department will arrange for a freelance service. The reception department will find a suitable freelancer, negotiate with them, and make the arrangements. This reduces the burden on the user by arranging freelance services based on their requests. Freelance services include, but are not limited to, interior design and furniture assembly.

[0077] The arrangement department can find, negotiate with, and arrange for suitable freelancers based on the user's requests. For example, if a user enters a request such as "I want this furniture assembled," the arrangement department will find a suitable freelancer and handle the negotiation and arrangement. This allows the department to provide services that meet the user's needs by finding, negotiating with, and arranging for suitable freelancers. Suitable freelancers include, but are not limited to, skills, ratings, and fees.

[0078] The management department may include a budget management department that manages users' budgets and adjusts the prices of proposed products to stay within those budgets. For example, the management department manages users' budgets and adjusts the prices of proposed products to stay within those budgets. This allows for proposals that are tailored to the user's budget by managing the user's budget and adjusting the prices of proposed products to stay within those budgets. Budgets include, but are not limited to, the total budget and the budget allocation for each product.

[0079] The reception desk can estimate the user's emotions and adjust the input method for room photos and desired atmosphere based on the estimated emotions. For example, if the user is stressed, the reception desk will provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk will provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk will prioritize voice input to allow for quick input of room photos and desired atmosphere. This allows for the provision of an optimal input experience for the user by adjusting the input method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception desk can analyze the user's past interior design preference history and suggest the optimal input method. For example, the reception desk can automatically display relevant options based on the interior design style the user has previously selected. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest an interior design style to be used at a specific time of day based on the user's past preference history. In this way, the optimal input method can be suggested by analyzing the user's past interior design preference history. Past interior design preference history includes, but is not limited to, past purchase history and survey results.

[0081] The reception desk can filter the user's current living situation and areas of interest when they input photos of their room and their desired atmosphere. For example, if the user has pets, the reception desk will prioritize displaying pet-friendly interior options. If the user has children, the reception desk will suggest interior options that take safety into consideration. If the user prefers an eco-friendly lifestyle, the reception desk will suggest environmentally friendly interior options. In this way, by filtering based on the user's current living situation and areas of interest, the reception desk can provide the user with the most suitable interior options. Current living situation includes, but is not limited to, family structure and lifestyle.

[0082] The reception desk can estimate the user's emotions and prioritize input content based on those emotions. For example, if the user is stressed, the reception desk will prioritize displaying important input items and postpone other items. If the user is relaxed, the reception desk will display detailed input items in order, allowing the user to choose freely. If the user is in a hurry, the reception desk will display only the most important input items, allowing for quick completion. This provides the user with an optimal input experience by prioritizing input content based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location when the user inputs photos of their room or their desired atmosphere. For example, if the user lives in a cold climate, the reception desk will prioritize displaying interior options that prioritize warmth. If the user lives in an urban area, the reception desk will suggest space-efficient interior options. If the user lives by the sea, the reception desk will suggest sea-themed interior options. By prioritizing the retrieval of highly relevant information while considering the user's geographical location, the reception desk can provide the user with the most suitable interior options. Geographical location information includes, but is not limited to, GPS data and address information.

[0084] The reception desk can analyze the user's social media activity and obtain relevant information when the user inputs photos of their room and their desired atmosphere. For example, if the reception desk frequently posts about "Scandinavian-style interiors" on social media, it will prioritize displaying Scandinavian-style interior options. If the reception desk frequently posts about "minimalist lifestyles" on social media, it will suggest minimalist interior options. If the reception desk frequently posts about "DIY interiors" on social media, it will suggest DIY interior options. In this way, by analyzing the user's social media activity, it can obtain relevant information and provide the most suitable interior options. Social media activity includes, but is not limited to, the content of posts and the number of followers.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and suggests multiple options. If the user is in a hurry, for example, the analysis unit performs a concise analysis and quickly suggests the most suitable option. If the user is stressed, for example, the analysis unit performs a simple analysis to reduce the user's burden. In this way, by adjusting the analysis method based on the user's emotions, the system can provide the user with the optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The analysis unit can optimize its analysis algorithm by referring to the user's past interior preference history during analysis. For example, the analysis unit prioritizes analyzing relevant options based on the interior style the user has previously selected. For example, the analysis unit predicts and analyzes the interior style the user will use at a specific time of day based on the user's past preference history. For example, the analysis unit analyzes the optimal combination of interior items based on the combination of interior items the user has previously selected. In this way, by referring to the user's past interior preference history, the analysis algorithm can be optimized and the best analysis results can be provided. Past interior preference history includes, but is not limited to, past purchase history and survey results.

[0087] The analysis unit can customize the analysis methods based on the user's current living situation. For example, if the user owns a pet, the analysis unit will prioritize analyzing pet-friendly interior options. If the user has children, the analysis unit will analyze interior options that take safety into consideration. If the user prefers an eco-friendly lifestyle, the analysis unit will analyze environmentally friendly interior options. By customizing the analysis methods based on the user's current living situation, the analysis unit can provide the user with the most optimal analysis results. Current living situation includes, but is not limited to, family structure and lifestyle.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The analysis unit can select the optimal analysis method during analysis, taking into account the user's geographical location information. For example, if the user lives in a cold region, the analysis unit will prioritize analyzing interior options that emphasize warmth. If the user lives in an urban area, the analysis unit will analyze interior options that are space-efficient. If the user lives by the sea, the analysis unit will analyze interior options with a sea theme. By selecting the optimal analysis method considering the user's geographical location information, the analysis unit can provide the user with the most optimal analysis results. Geographical location information includes, but is not limited to, GPS data and address information.

[0090] The analysis unit can analyze a user's social media activity during analysis and propose analysis methods. For example, if a user frequently posts about "Nordic-style interiors" on social media, the analysis unit will prioritize analyzing Nordic-style interior options. For example, if a user frequently posts about "minimalist lifestyles" on social media, the analysis unit will analyze minimalist interior options. For example, if a user frequently posts about "DIY interiors" on social media, the analysis unit will analyze DIY-friendly interior options. In this way, by analyzing a user's social media activity, the optimal analysis method can be proposed. Social media activity includes, but is not limited to, the content of posts and the number of followers.

[0091] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions and offer multiple options. If the user is in a hurry, for example, the suggestion function will provide concise suggestions and quickly present the most suitable option. If the user is stressed, for example, the suggestion function will provide simple suggestions to reduce the user's burden. In this way, by adjusting the way suggestions are presented based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, for expensive products, the proposal department will provide detailed information to ensure user satisfaction. For low-priced products, the proposal department will provide concise information and make a quick proposal. For products of particular interest to the user, the proposal department will provide detailed information to allow comparison with other options. By adjusting the level of detail in a proposal based on the importance of the product, the proposal department can provide the best possible proposal for the user. Product importance includes, but is not limited to, user reviews and sales performance.

[0093] The suggestion function can apply different suggestion algorithms depending on the product category when making suggestions. For example, for the furniture category, the suggestion function will make optimal suggestions based on size and material. For example, for the lighting category, the suggestion function will make optimal suggestions based on brightness and design. For example, for the decoration category, the suggestion function will make optimal suggestions based on theme and color scheme. By applying different suggestion algorithms depending on the product category, the system can provide the user with the most suitable suggestions. Product categories include, but are not limited to, furniture, interior accessories, and lighting.

[0094] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function will provide longer suggestions with detailed explanations. If the user is excited, the suggestion function will provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The proposal department can prioritize proposals based on the product submission timing. For example, it will prioritize proposals for products that are urgently needed. For example, it will postpone proposals for products that are needed long-term. For example, it will propose products that users need at a specific time, according to that time. By prioritizing proposals based on the product submission timing, the department can provide the best possible proposals for users. Product submission timing includes, but is not limited to, release dates and arrival dates.

[0096] The suggestion department can adjust the order of suggestions based on the relevance of the products. For example, it might prioritize suggesting decorative items related to the furniture selected by the user. For example, it might prioritize suggesting curtains and rugs related to the lighting selected by the user. For example, it might prioritize suggesting cushions and blankets related to the sofa selected by the user. By adjusting the order of suggestions based on the relevance of the products, the department can provide the user with the most suitable suggestions. Product relevance includes, but is not limited to, items of the same brand or category.

[0097] The management unit can estimate the user's emotions and adjust management methods based on those estimated emotions. For example, if the user is relaxed, the management unit can provide detailed management options and allow the user to choose freely. If the user is in a hurry, the management unit can provide concise management options to enable quick management. If the user is stressed, the management unit can provide simple management options to reduce the user's burden. This allows the management unit to provide the optimal management method for the user by adjusting management methods based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The management department can optimize its management algorithms by referring to the user's past purchase history during management. For example, the management department can propose the optimal management method based on the inventory status of products the user has purchased in the past. For example, the management department can predict and propose the management method for products needed at a specific time period based on the user's past purchase history. For example, the management department can propose the optimal management method based on the combination of products the user has purchased in the past. In this way, by referring to the user's past purchase history, the management algorithm can be optimized and the optimal management method can be provided. Past purchase history includes, but is not limited to, the purchase date, purchased products, and purchase amount.

[0099] The management department can customize management methods based on the user's current living situation during management. For example, if the user owns a pet, the management department will prioritize suggesting pet-friendly management options. For example, if the user has children, the management department will suggest safety-conscious management options. For example, if the user prefers an eco-friendly lifestyle, the management department will suggest environmentally friendly management options. By customizing management methods based on the user's current living situation, the management department can provide the user with the most suitable management method. Current living situation includes, but is not limited to, family structure and lifestyle.

[0100] The management unit can estimate the user's emotions and adjust the display method of management results based on the estimated user emotions. For example, if the user is tense, the management unit provides a simple and highly visible display method. For example, if the user is relaxed, the management unit provides a display method that includes detailed information. For example, if the user is in a hurry, the management unit provides a display method that gets straight to the point. In this way, by adjusting the display method of management results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The management department can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user lives in a cold region, the management department will prioritize suggesting management options that prioritize warmth. For example, if the user lives in an urban area, the management department will suggest space-efficient management options. For example, if the user lives by the sea, the management department will suggest sea-themed management options. By selecting the optimal management method considering the user's geographical location information, the management department can provide the user with the most suitable management method. Geographical location information includes, but is not limited to, GPS data and address information.

[0102] The management department can analyze users' social media activity during management and propose management methods. For example, if a user frequently posts about "Nordic-style interiors" on social media, the management department will prioritize suggesting Nordic-style management options. For example, if a user frequently posts about "minimalist lifestyles" on social media, the management department will propose minimalist management options. For example, if a user frequently posts about "DIY interiors" on social media, the management department will propose DIY-friendly management options. In this way, by analyzing users' social media activity, the management department can propose the most suitable management method. Social media activity includes, but is not limited to, the content of posts and the number of followers.

[0103] The booking system can estimate the user's emotions and adjust the booking method based on the estimated emotions. For example, if the user is relaxed, the booking system can provide detailed booking options and allow the user to choose freely. If the user is in a hurry, the booking system can provide concise booking options to enable quick booking. If the user is stressed, the booking system can provide simple booking options to reduce the user's burden. In this way, by adjusting the booking method based on the user's emotions, the system can provide the optimal booking method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The scheduling unit can optimize its scheduling algorithm by referring to the user's past use history of freelance services. For example, the scheduling unit can suggest the most suitable freelancer based on the user's past evaluations of freelancers. For example, the scheduling unit can predict and suggest the necessary scheduling method for a specific time period based on the user's past usage history. For example, the scheduling unit can suggest the most suitable scheduling method based on the skill sets of freelancers the user has used in the past. In this way, by referring to the user's past use history of freelance services, the scheduling algorithm can be optimized and the most suitable scheduling method can be provided. Past use history of freelance services includes, but is not limited to, the date of use, the services used, and evaluations.

[0105] The ordering unit can estimate the user's emotions and determine the priority of orders based on those emotions. For example, if the user is in a hurry, the ordering unit will prioritize the most important ordering items. If the user is relaxed, the ordering unit will prioritize detailed ordering items in order. If the user is stressed, the ordering unit will prioritize simple ordering items. This allows the ordering unit to provide the user with the optimal ordering method by prioritizing orders based on their 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.

[0106] The booking system can select the most suitable booking method by considering the user's geographical location information during the booking process. For example, if the user lives in a cold region, the booking system will prioritize suggesting booking options that prioritize warmth. If the user lives in an urban area, the booking system will suggest space-efficient booking options. If the user lives by the sea, the booking system will suggest sea-themed booking options. By selecting the most suitable booking method by considering the user's geographical location information, the system can provide the user with the most suitable booking method. Geographical location information includes, but is not limited to, GPS data and address information.

[0107] The budget management unit can estimate the user's emotions and adjust the budget management method based on the estimated emotions. For example, if the user is relaxed, the budget management unit can provide detailed budget management options and allow the user to choose freely. For example, if the user is in a hurry, the budget management unit can provide concise budget management options to enable quick management. For example, if the user is stressed, the budget management unit can provide simple budget management options to reduce the user's burden. In this way, by adjusting the budget management method based on the user's emotions, the optimal budget management method can be provided for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The budget management department can analyze users' past spending habits to select the optimal budget management method during budget management. For example, the budget management department can propose the optimal budget management method based on the prices of products the user has purchased in the past. For example, the budget management department can predict and propose the necessary budget management method for a specific time period based on the user's past spending habits. For example, the budget management department can propose the optimal budget management method based on combinations of products the user has purchased in the past. In this way, by analyzing the user's past spending habits, the optimal budget management method can be selected, and the optimal budget management can be provided to the user. Past spending habits include, but are not limited to, purchase dates, purchased items, and purchase amounts.

[0109] The budget management department can estimate the user's emotions and determine budget management priorities based on those emotions. For example, if the user is in a hurry, the budget management department will prioritize the most important budget management items. If the user is relaxed, the budget management department will process detailed budget management items in order. If the user is stressed, the budget management department will prioritize simple budget management items. This allows for optimal budget management for the user by prioritizing budget management based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The budget management department can select the optimal budget management method when managing the budget, taking into account the user's geographical location. For example, if the user lives in a cold region, the budget management department will prioritize suggesting budget management options that prioritize warmth. For example, if the user lives in an urban area, the budget management department will suggest budget management options that are space-efficient. For example, if the user lives by the sea, the budget management department will suggest budget management options with a sea theme. By selecting the optimal budget management method considering the user's geographical location, the budget management department can provide the user with the best possible budget management. Geographical location information includes, but is not limited to, GPS data and address information.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception desk can suggest interior styles that match the user's preferences and lifestyle based on the user's input. For example, if a user inputs "I like a natural atmosphere," the reception desk will prioritize suggesting natural-style furniture and interiors. If a user inputs "I have a pet," the reception desk can also suggest furniture made from pet-friendly materials and designs. Furthermore, if a user inputs "I live an eco-friendly lifestyle," the reception desk can suggest environmentally conscious materials and products. This makes it possible to suggest the most suitable interior styles according to the user's lifestyle and preferences.

[0113] The analysis unit can estimate the user's emotions based on their input and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a simple and rapid analysis to reduce the user's burden. If the user is relaxed, it can perform a detailed analysis and present multiple options. Furthermore, if the user is excited, it can provide visually appealing analysis results. This makes it possible to provide optimal analysis results tailored to the user's emotions.

[0114] The suggestion function can refer to the user's past purchase history based on their input and suggest related products. For example, it can suggest interior items that complement furniture the user has purchased in the past. It can also suggest related products based on the style and color scheme of items the user has purchased in the past. Furthermore, it can suggest high-quality products by referring to the ratings and reviews of items the user has purchased in the past. This enables optimal suggestions based on the user's past purchase history.

[0115] The management department can manage user budgets based on user input and adjust the prices of suggested products to stay within budget. For example, it can select and suggest the most suitable products based on the user's set budget. It can also suggest a balanced combination of high-priced and low-priced products according to the user's budget. Furthermore, it can utilize sales and discount information to provide cost-effective suggestions according to the user's budget. This enables optimal suggestions tailored to the user's budget.

[0116] The scheduling system can arrange the most suitable freelancer based on the user's input, taking into account the user's geographical location. For example, if the user lives in an urban area, it will prioritize arranging a nearby freelancer. If the user lives in a cold region, it can arrange a freelancer with skills suited to cold climates. Furthermore, if the user lives by the sea, it can arrange a freelancer who can provide services appropriate for the seaside environment. This enables the arrangement of the most suitable freelancer based on the user's geographical location.

[0117] The reception desk can estimate the user's emotions and adjust the input interface based on those estimates. For example, if the user is stressed, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick completion. This makes it possible to provide an optimal input experience tailored to the user's emotions.

[0118] The analysis unit can analyze a user's social media activity based on their input and obtain relevant information. For example, if a user frequently posts about "Nordic-style interiors" on social media, it will prioritize analyzing Nordic-style interior options. Similarly, if a user frequently posts about "minimalist lifestyles," it can analyze minimalist interior options. Furthermore, if a user frequently posts about "DIY interiors," it can analyze DIY-friendly interior options. This enables optimal analysis based on the user's social media activity.

[0119] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those estimates. For example, if the user is relaxed, it can provide detailed suggestions and offer multiple options. If the user is in a hurry, it can provide concise suggestions and quickly present the most suitable option. Furthermore, if the user is stressed, it can provide simple suggestions to reduce the user's burden. This enables the provision of optimal suggestions tailored to the user's emotions.

[0120] The management department can customize management methods based on user input, taking into account the user's current living situation. For example, if a user owns a pet, pet-friendly management options will be prioritized. Similarly, if a user has children, safety-conscious management options can be suggested. Furthermore, if a user prefers an eco-friendly lifestyle, environmentally friendly management options can be suggested. This enables optimal management tailored to the user's current living situation.

[0121] The scheduling unit can estimate the user's emotions and determine the priority of scheduling based on those emotions. For example, if the user is in a hurry, it will prioritize the most important scheduling items. If the user is relaxed, it can process detailed scheduling items in order. Furthermore, if the user is stressed, it can prioritize simple scheduling items. This enables optimal scheduling tailored to the user's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk accepts photos of the room and input of the desired atmosphere from the user. The input of photos of the room and the desired atmosphere from the user may include, but is not limited to, styles such as modern, classic, and natural. The reception desk accepts, for example, input from the user such as "I want a Scandinavian-style living room." Step 2: The analysis unit analyzes the information received by the reception unit and searches each e-commerce site. The analysis unit uses AI, for example, to find furniture and interior items that match the user's preferences. Step 3: The proposal department proposes the most suitable furniture and interior items based on the information obtained by the analysis department. For example, the proposal department may combine products from different websites, such as a TV stand from one e-commerce site, a rug from another e-commerce site, and lighting from yet another e-commerce site. Step 4: The management department manages the price, inventory, and delivery time of the products proposed by the proposal department. For example, the management department makes proposals considering the price, inventory, and delivery time of the proposed products.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user, such as photos of the room and the desired atmosphere. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and searches for various e-commerce sites. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable furniture and interior based on the information obtained by the analysis unit. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the price, inventory, and delivery time of the products proposed by the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user, such as photos of the room and the desired atmosphere. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to search for various e-commerce sites. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable furniture and interior based on the information obtained by the analysis unit. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and manages the price, inventory, and delivery time of the products proposed by the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and management 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 control unit 46A of the headset terminal 314 and receives input from the user, such as photos of the room and the desired atmosphere. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit to search for various e-commerce sites. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable furniture and interior based on the information obtained by the analysis unit. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the price, inventory, and delivery time of the products proposed by the proposal unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and management 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 control unit 46A of the robot 414 and receives input from the user, such as photos of the room and the desired atmosphere. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and searches each e-commerce site. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable furniture and interior based on the information obtained by the analysis unit. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the price, inventory, and delivery time of the products proposed by the proposal unit. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A reception area that accepts photos of rooms and inputs about the desired atmosphere from users, The analysis unit analyzes the information received by the reception unit and searches for each e-commerce site, Based on the information obtained by the analysis unit, the proposal unit proposes the most suitable furniture and interior design, The system comprises a management unit that manages the price, inventory, and delivery time of the products proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Propose a combination of products from different e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, We will make a proposal considering the price, availability, and delivery time of the proposed product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It includes a scheduling department that arranges freelance services based on the user's requests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned ordering unit, We find suitable freelancers based on the user's requirements and handle negotiations and arrangements. The system described in Appendix 4, characterized by the features described herein. (Note 6) The aforementioned management department, It includes a budget management unit that manages the user's budget and adjusts the price of proposed products to stay within that budget. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for room photos and desired atmosphere based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past interior design preference history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input photos of their rooms or their desired atmosphere, the system filters the results based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input photos of their rooms or their desired atmosphere, the system prioritizes retrieving highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input photos of their room and their desired atmosphere, the system analyzes their social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past interior design preference history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the optimal analysis method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, we analyze users' social media activity and propose analytical methods. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, It estimates user sentiment and adjusts management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During management, the management algorithm is optimized by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During management, the management methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, It estimates the user's emotions and adjusts how management results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, During management, the optimal management method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, During management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned ordering unit, It estimates the user's emotions and adjusts the arrangement method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned ordering unit, When making a booking, the booking algorithm is optimized by referring to the user's past usage history of freelance services. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned ordering unit, It estimates the user's emotions and determines the priority of arrangements based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned ordering unit, When making arrangements, the optimal arrangement method is selected considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned budget management department, Estimate user sentiment and adjust budget management methods based on the estimated user sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 36) The aforementioned budget management department, When managing budgets, analyze users' past spending behavior to select the optimal budget management method. The system described in Appendix 6, characterized by the features described herein. (Note 37) The aforementioned budget management department, Estimate user sentiment and prioritize budget management based on the estimated user sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 38) The aforementioned budget management department, When managing the budget, the optimal budget management method is selected by considering the user's geographical location information. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 area that accepts photos of rooms and inputs about the desired atmosphere from users, The analysis unit analyzes the information received by the reception unit and searches for each e-commerce site, Based on the information obtained by the analysis unit, the proposal unit proposes the most suitable furniture and interior design, The system comprises a management unit that manages the price, inventory, and delivery time of the products proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, Propose a combination of products from different e-commerce sites. The system according to feature 1.

3. The aforementioned management department, We will make a proposal considering the price, availability, and delivery time of the proposed product. The system according to feature 1.

4. The aforementioned reception unit is It includes a scheduling department that arranges freelance services based on the user's requests. The system according to feature 1.

5. The aforementioned ordering unit, We find suitable freelancers based on the user's requirements and handle negotiations and arrangements. The system according to feature 4.

6. The aforementioned management department, It includes a budget management unit that manages the user's budget and adjusts the price of proposed products to stay within that budget. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for room photos and desired atmosphere based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past interior design preference history and suggest the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users input photos of their rooms or their desired atmosphere, the system filters the results based on their current living situation and areas of interest. The system according to feature 1.

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