system

Abstract

The system according to this embodiment aims to select the most suitable furniture and decorative items to match the style and function of the room desired by the user, and to provide a total coordinated look. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a supply unit, a feedback acquisition unit, and an adjustment unit. The input unit receives input from the user regarding the desired room style, functions, and budget. The analysis unit analyzes the information entered by the input unit and selects appropriate furniture and decorations. The supply unit periodically delivers the items selected by the analysis unit to the user. The feedback acquisition unit obtains feedback from the user. The adjustment unit adjusts the selection based on the feedback obtained by the feedback acquisition 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to select optimal furniture and decorations according to the style and functions of a room desired by a user and perform total coordination.

[0005] The system according to the embodiment aims to select optimal furniture and decorations according to the style and functions of a room desired by a user and perform total coordination.

Means for Solving the Problems

[0007] The system according to this embodiment can select the most suitable furniture and decorative items to match the style and function of the room desired by the user, and perform a total coordination. [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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The total coordination system according to an embodiment of the present invention is a system in which the AI ​​selects the most suitable furniture and decorative items and delivers them regularly, simply by the user specifying the desired room style, function, and budget. This total coordination system allows the user to input their desired room style, function, and budget, and the AI ​​analyzes this information to select the most suitable furniture and decorative items. The selected items are delivered to the user regularly, and the total coordination of the room progresses. The AI ​​adjusts the selection based on user feedback, ultimately achieving a room that satisfies the user. For example, the user inputs their desired room style, function, and budget. For instance, they might input information such as, "I want to furnish my living room in a modern style. My budget is 100,000 yen." This information is input to the AI. Next, the AI ​​analyzes the input information and selects the most suitable furniture and decorative items. The AI ​​selects the most suitable items based on the user's preferences. For example, it might select a modern-style sofa, table, and lighting. The selected items are delivered to the user regularly. For example, the selected items are delivered to the user once a month. This allows the user to gradually coordinate their room. Furthermore, the AI ​​adjusts the selection based on user feedback. For example, if a user provides feedback such as, "This sofa is a little too big," the AI ​​analyzes that feedback and incorporates it into future selections. This allows the system to create a room that satisfies the user. As a result, the total coordination system allows users to simply input their desired room style, functionality, and budget, and the AI ​​will select the optimal furniture and decorations and deliver them regularly.

[0029] The total coordination system according to this embodiment comprises an input unit, an analysis unit, a supply unit, a feedback acquisition unit, and an adjustment unit. The input unit takes input from the user regarding the desired room style, functions, and budget. For example, the user can input information such as, "I want to furnish my living room in a modern style. My budget is 100,000 yen." The analysis unit analyzes the information entered by the input unit and selects the most suitable furniture and decorative items. For example, the analysis unit selects a modern style sofa, table, lighting, etc., based on the user's preferences. The supply unit delivers the items selected by the analysis unit to the user on a regular basis. For example, the supply unit can deliver the selected items to the user once a month. The feedback acquisition unit acquires feedback from the user. For example, the feedback acquisition unit can take feedback such as, "This sofa is a little too big." The adjustment unit adjusts the selection based on the feedback acquired by the feedback acquisition unit. For example, the adjustment unit can analyze the user's feedback and reflect it in the next selection. As a result, the total coordination system according to this embodiment allows the AI ​​to select the most suitable furniture and decorative items and deliver them regularly simply by the user inputting their desired room style, functions, and budget.

[0030] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display room styles, functions, and budgets that the user has frequently entered in the past as suggestions. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest styles, functions, and budgets to be used at specific times of the day based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0031] The input unit can automatically complete the input content based on the user's current living situation and areas of interest. For example, if the user has recently moved, the input unit can suggest styles and features suitable for the new residence. Furthermore, if the user is interested in a particular interior style, the input unit can suggest furniture and decorations based on that style. Additionally, if the user owns a pet, the input unit can suggest pet-friendly furniture and decorations. This reduces the effort required for input by automatically completing the input content based on the user's current living situation and areas of interest. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can input the user's living situation data into a generating AI and have the generating AI perform automatic completion of the input content.

[0032] The input unit can suggest highly relevant styles and functions while considering the user's geographical location information. For example, if the user lives in an urban area, the input unit can suggest modern style furniture and decorations. If the user lives in the suburbs, it can suggest country style furniture and decorations. Furthermore, if the user lives by the sea, it can suggest marine style furniture and decorations. This allows for more appropriate suggestions by suggesting highly relevant styles and functions while considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant styles and functions.

[0033] The input unit can analyze the user's social media activity during input and suggest relevant styles and features. For example, the input unit can suggest relevant furniture and decorations based on the interior styles the user frequently views on Instagram. It can also suggest relevant styles and features based on interior images the user has saved on Pinterest®. Furthermore, it can suggest relevant furniture and decorations based on interior posts the user has "liked" on Facebook®. In this way, by analyzing the user's social media activity, it is possible to suggest styles and features that match the user's preferences. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media data into a generating AI and have the generating AI perform the generation of suggestions for relevant styles and features.

[0034] The analysis unit can improve the accuracy of its analysis by referring to the user's past selection history. For example, the analysis unit can select items that match the user's preferences based on furniture and decorative items the user has selected in the past. The analysis unit can also prioritize the selection of specific brands or designs based on the user's past selection history. Furthermore, the analysis unit can analyze the user's past selection history and select the item that will provide the highest satisfaction. In this way, the accuracy of the analysis can be improved by referring to the user's past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past selection data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0035] The analysis unit can customize the analysis results based on the user's current living situation during the analysis. For example, if the user moves to a new house, the analysis unit can select furniture and decorations suitable for that house. It can also select items suitable for the new family if the user changes their family structure. Furthermore, if the user starts keeping a pet, the analysis unit can select pet-friendly furniture and decorations. This allows for more appropriate suggestions by customizing the analysis results based on the user's current living situation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the analysis results.

[0036] The analysis unit can select appropriate furniture and decorations while considering the user's geographical location information during analysis. For example, if the user lives in an urban area, the analysis unit will select modern-style furniture and decorations. If the user lives in the suburbs, the analysis unit can also select country-style furniture and decorations. Furthermore, if the user lives by the sea, the analysis unit can also select marine-style furniture and decorations. By selecting the optimal furniture and decorations while considering the user's geographical location information, more appropriate suggestions can be made. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal furniture and decorations.

[0037] The analysis unit can analyze a user's social media activity during analysis and select relevant furniture and decorative items. For example, the analysis unit can select relevant furniture and decorative items based on the interior styles the user frequently views on Instagram. It can also select relevant styles and features based on interior images the user has saved on Pinterest. Furthermore, the analysis unit can select relevant furniture and decorative items based on interior posts the user has "liked" on Facebook. In this way, by analyzing a user's social media activity, it is possible to select furniture and decorative items that match the user's preferences. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the selection of relevant furniture and decorative items.

[0038] The delivery unit can select the optimal delivery method by referring to the user's past receiving history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the user's history of items received in the past. The delivery unit can also prioritize the delivery of specific brands or designs based on the user's past receiving history. Furthermore, the delivery unit can analyze the user's past receiving history and select the delivery method that will yield the highest satisfaction. In this way, the optimal delivery method can be selected by referring to the user's past receiving history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past receiving data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0039] The service provider can customize the offered items based on the user's current living situation at the time of delivery. For example, if the user moves to a new house, the service provider can provide items suitable for that house. Furthermore, if the user changes their family structure, the service provider can provide items suitable for the new family. Additionally, if the user starts keeping a pet, the service provider can provide pet-friendly items. By customizing the offered items based on the user's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI perform the customization of the offered items.

[0040] The delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user lives in an urban area, the delivery unit can provide modern-style items. If the user lives in the suburbs, the delivery unit can also provide country-style items. Furthermore, if the user lives by the sea, the delivery unit can provide marine-style items. By selecting the optimal delivery method by considering the user's geographical location information, more appropriate suggestions can be made. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0041] The service provider can analyze the user's social media activity and provide relevant items at the time of delivery. For example, the service provider can provide relevant items based on the interior styles the user frequently views on Instagram. It can also provide relevant items based on interior images the user has saved on Pinterest. Furthermore, the service provider can provide relevant items based on interior posts the user has "liked" on Facebook. In this way, by analyzing the user's social media activity, it is possible to provide items that match the user's preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant items.

[0042] The feedback acquisition unit can select the optimal feedback acquisition method by referring to the user's past feedback history when acquiring feedback. For example, if the user has provided detailed feedback in the past, the feedback acquisition unit can provide a detailed feedback form. Alternatively, if the user has provided concise feedback in the past, the feedback acquisition unit can provide a concise feedback form. Furthermore, the feedback acquisition unit can analyze the user's past feedback history and select the most effective feedback acquisition method. This allows the optimal feedback acquisition method to be selected by referring to the user's past feedback history. Some or all of the above processing in the feedback acquisition unit may be performed using AI, for example, or without AI. For example, the feedback acquisition unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal feedback acquisition method.

[0043] The feedback acquisition unit can select the optimal method of acquiring feedback by considering the user's geographical location information. For example, if the user lives in an urban area, the feedback acquisition unit may prioritize online feedback acquisition. If the user lives in a suburban area, the feedback acquisition unit may also prioritize postal feedback acquisition. Furthermore, if the user lives overseas, the feedback acquisition unit may provide a multilingual feedback form. By selecting the optimal feedback acquisition method considering the user's geographical location information, more appropriate feedback can be obtained. Some or all of the above processing in the feedback acquisition unit may be performed using AI, for example, or without AI. For example, the feedback acquisition unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal feedback acquisition method.

[0044] The adjustment unit can improve the accuracy of adjustments by referring to the user's past feedback history during the adjustment process. For example, the adjustment unit can select items that suit the user's preferences based on feedback provided in the past. The adjustment unit can also prioritize the selection of specific brands or designs based on the user's past feedback history. Furthermore, the adjustment unit can analyze the user's past feedback history and select the items that have the highest satisfaction. In this way, the accuracy of adjustments can be improved by referring to the user's past feedback history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's past feedback data into a generating AI and have the generating AI perform the adjustment accuracy improvement.

[0045] The adjustment unit can customize the adjustment content based on the user's current living situation during the adjustment process. For example, if the user moves to a new house, the adjustment unit can select items suitable for that house. It can also select items suitable for the new family if the user changes their family structure. Furthermore, if the user starts keeping a pet, the adjustment unit can select pet-friendly items. This allows for more appropriate suggestions by customizing the adjustment content based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the adjustment content.

[0046] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, if the user lives in an urban area, the adjustment unit may select modern-style items. If the user lives in a suburban area, the adjustment unit may select country-style items. Furthermore, if the user lives by the sea, the adjustment unit may select marine-style items. By selecting the optimal adjustment method considering the user's geographical location information, more appropriate suggestions can be made. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal adjustment method.

[0047] The adjustment unit can analyze the user's social media activity and make relevant adjustments during the adjustment process. For example, the adjustment unit can select relevant items based on the interior styles the user frequently views on Instagram. It can also select relevant items based on interior images the user has saved on Pinterest. Furthermore, it can select relevant items based on interior posts the user has "liked" on Facebook. This allows for adjustments tailored to the user's preferences by analyzing their social media activity. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media data into a generating AI and have the generating AI perform the relevant adjustments.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The analysis unit can acquire user health data and select furniture and decorative items based on their health status. For example, if a user has allergies, it can select furniture made from allergen-free materials. If a user suffers from back pain, it can select chairs and sofas with back-friendly designs. Furthermore, if a user seeks relaxation, it can provide relaxing lighting and scents. This allows the system to provide optimal furniture and decorative items tailored to the user's health condition.

[0050] The service provider can acquire users' energy consumption data and suggest energy-efficient furniture and decorative items. For example, if a user wants to reduce energy consumption, they can suggest energy-saving lighting and appliances. If a user is environmentally conscious, they can also suggest furniture made from recycled materials. Furthermore, if a user wants to minimize energy consumption, they can suggest window placement and curtains that maximize natural light. This allows for optimal suggestions based on the user's energy consumption data.

[0051] The adjustment unit can acquire the user's sleep data and adjust furniture and decorations based on the quality of their sleep. For example, if the user is sleep-deprived, it can suggest relaxing lighting and music. If the user is seeking comfortable sleep, it can suggest a mattress and pillow of appropriate firmness. Furthermore, if the user wants to achieve deep sleep, it can suggest blackout curtains and soundproof furniture. This allows the system to provide optimal furniture and decorations based on the user's sleep data.

[0052] The analysis unit can analyze a user's purchase history and select furniture and decorative items based on past purchasing trends. For example, if a user prefers a particular brand, it can prioritize selecting items from that brand. Similarly, if a user prefers a specific design, it can select items based on that design. Furthermore, it can suggest items that complement items the user has purchased in the past. This allows the system to provide optimal furniture and decorative items based on the user's purchase history.

[0053] The service provider can also customize the items offered based on the user's hobbies and preferences. For example, if a user enjoys music, they can provide high-quality speakers and musical instruments. If a user enjoys reading, they can provide lighting and chairs to create a comfortable reading environment. Furthermore, if a user enjoys cooking, they can provide kitchenware and cooking utensils. This allows the service provider to offer items that are best suited to the user's hobbies and preferences.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The input section allows the user to enter their desired room style, functions, and budget. For example, a user might enter information such as, "I want to furnish my living room in a modern style. My budget is 100,000 yen." Step 2: The analysis unit analyzes the information entered by the input unit and selects the most suitable furniture and decorative items. For example, the analysis unit selects modern style sofas, tables, lighting, etc., based on the user's preferences. Step 3: The supply department regularly delivers the items selected by the analysis department to the user. For example, the supply department can deliver the selected items to the user once a month. Step 4: The feedback acquisition unit acquires user feedback. For example, the feedback acquisition unit can receive feedback such as, "This sofa is a little too big." Step 5: The adjustment unit adjusts the selection based on the feedback obtained by the feedback acquisition unit. For example, the adjustment unit can analyze user feedback and reflect it in the next selection.

[0056] (Example of form 2) The total coordination system according to an embodiment of the present invention is a system in which the AI ​​selects the most suitable furniture and decorative items and delivers them regularly, simply by the user specifying the desired room style, function, and budget. This total coordination system allows the user to input their desired room style, function, and budget, and the AI ​​analyzes this information to select the most suitable furniture and decorative items. The selected items are delivered to the user regularly, and the total coordination of the room progresses. The AI ​​adjusts the selection based on user feedback, ultimately achieving a room that satisfies the user. For example, the user inputs their desired room style, function, and budget. For instance, they might input information such as, "I want to furnish my living room in a modern style. My budget is 100,000 yen." This information is input to the AI. Next, the AI ​​analyzes the input information and selects the most suitable furniture and decorative items. The AI ​​selects the most suitable items based on the user's preferences. For example, it might select a modern-style sofa, table, and lighting. The selected items are delivered to the user regularly. For example, the selected items are delivered to the user once a month. This allows the user to gradually coordinate their room. Furthermore, the AI ​​adjusts the selection based on user feedback. For example, if a user provides feedback such as, "This sofa is a little too big," the AI ​​analyzes that feedback and incorporates it into future selections. This allows the system to create a room that satisfies the user. As a result, the total coordination system allows users to simply input their desired room style, functionality, and budget, and the AI ​​will select the optimal furniture and decorations and deliver them regularly.

[0057] The total coordination system according to this embodiment comprises an input unit, an analysis unit, a supply unit, a feedback acquisition unit, and an adjustment unit. The input unit takes input from the user regarding the desired room style, functions, and budget. For example, the user can input information such as, "I want to furnish my living room in a modern style. My budget is 100,000 yen." The analysis unit analyzes the information entered by the input unit and selects the most suitable furniture and decorative items. For example, the analysis unit selects a modern style sofa, table, lighting, etc., based on the user's preferences. The supply unit delivers the items selected by the analysis unit to the user on a regular basis. For example, the supply unit can deliver the selected items to the user once a month. The feedback acquisition unit acquires feedback from the user. For example, the feedback acquisition unit can take feedback such as, "This sofa is a little too big." The adjustment unit adjusts the selection based on the feedback acquired by the feedback acquisition unit. For example, the adjustment unit can analyze the user's feedback and reflect it in the next selection. As a result, the total coordination system according to this embodiment allows the AI ​​to select the most suitable furniture and decorative items and deliver them regularly simply by the user inputting their desired room style, functions, and budget.

[0058] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is stressed, the input unit can provide a simple interface and minimize the input steps. If the user is relaxed, the input unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the input unit can prioritize voice input, allowing them to quickly input their desired room style, features, and budget. This improves the user's input experience by providing an input interface design that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0059] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display room styles, functions, and budgets that the user has frequently entered in the past as suggestions. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest styles, functions, and budgets to be used at specific times of the day based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0060] The input unit can automatically complete the input content based on the user's current living situation and areas of interest. For example, if the user has recently moved, the input unit can suggest styles and features suitable for the new residence. Furthermore, if the user is interested in a particular interior style, the input unit can suggest furniture and decorations based on that style. Additionally, if the user owns a pet, the input unit can suggest pet-friendly furniture and decorations. This reduces the effort required for input by automatically completing the input content based on the user's current living situation and areas of interest. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can input the user's living situation data into a generating AI and have the generating AI perform automatic completion of the input content.

[0061] The input unit can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, if the user is tired, the input unit may prioritize displaying the most important input items (such as budget). If the user is excited, the input unit may also prioritize displaying detailed input items (such as style or features). Furthermore, if the user is relaxed, the input unit may display all input items equally. This improves the user's input experience by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0062] The input unit can suggest highly relevant styles and functions while considering the user's geographical location information. For example, if the user lives in an urban area, the input unit can suggest modern style furniture and decorations. If the user lives in the suburbs, it can suggest country style furniture and decorations. Furthermore, if the user lives by the sea, it can suggest marine style furniture and decorations. This allows for more appropriate suggestions by suggesting highly relevant styles and functions while considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant styles and functions.

[0063] The input unit can analyze the user's social media activity during input and suggest relevant styles and features. For example, it can suggest relevant furniture and decorations based on the interior styles the user frequently views on Instagram. It can also suggest relevant styles and features based on interior images the user has saved on Pinterest. Furthermore, it can suggest relevant furniture and decorations based on interior posts the user has "liked" on Facebook. In this way, by analyzing the user's social media activity, it is possible to suggest styles and features that match the user's preferences. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media data into a generating AI and have the generating AI perform the generation of suggestions for relevant styles and features.

[0064] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and select the most suitable furniture and decorations. If the user is in a hurry, the analysis unit can perform a quick analysis and select basic furniture and decorations. Furthermore, if the user is excited, the analysis unit can prioritize the selection of visually appealing furniture and decorations. By adjusting the analysis algorithm according to the user's emotions, the accuracy of the analysis results can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0065] The analysis unit can improve the accuracy of its analysis by referring to the user's past selection history. For example, the analysis unit can select items that match the user's preferences based on furniture and decorative items the user has selected in the past. The analysis unit can also prioritize the selection of specific brands or designs based on the user's past selection history. Furthermore, the analysis unit can analyze the user's past selection history and select the item that will provide the highest satisfaction. In this way, the accuracy of the analysis can be improved by referring to the user's past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past selection data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0066] The analysis unit can customize the analysis results based on the user's current living situation during the analysis. For example, if the user moves to a new house, the analysis unit can select furniture and decorations suitable for that house. It can also select items suitable for the new family if the user changes their family structure. Furthermore, if the user starts keeping a pet, the analysis unit can select pet-friendly furniture and decorations. This allows for more appropriate suggestions by customizing the analysis results based on the user's current living situation. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the analysis results.

[0067] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, it is possible to help the user understand the results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0068] The analysis unit can select appropriate furniture and decorations while considering the user's geographical location information during analysis. For example, if the user lives in an urban area, the analysis unit will select modern-style furniture and decorations. If the user lives in the suburbs, the analysis unit can also select country-style furniture and decorations. Furthermore, if the user lives by the sea, the analysis unit can also select marine-style furniture and decorations. By selecting the optimal furniture and decorations while considering the user's geographical location information, more appropriate suggestions can be made. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal furniture and decorations.

[0069] The analysis unit can analyze a user's social media activity during analysis and select relevant furniture and decorative items. For example, the analysis unit can select relevant furniture and decorative items based on the interior styles the user frequently views on Instagram. It can also select relevant styles and features based on interior images the user has saved on Pinterest. Furthermore, the analysis unit can select relevant furniture and decorative items based on interior posts the user has "liked" on Facebook. In this way, by analyzing a user's social media activity, it is possible to select furniture and decorative items that match the user's preferences. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the selection of relevant furniture and decorative items.

[0070] The service provider can estimate the user's emotions and adjust the service schedule based on those emotions. For example, if the user is relaxed, the service provider will deliver items at a leisurely pace. If the user is in a hurry, the service provider can deliver items quickly. Furthermore, if the user is excited, the service provider can prioritize delivering visually appealing items. By adjusting the service schedule according to the user's emotions, user satisfaction can be improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The delivery unit can select the optimal delivery method by referring to the user's past receiving history at the time of delivery. For example, the delivery unit can select the optimal delivery method based on the user's history of items received in the past. The delivery unit can also prioritize the delivery of specific brands or designs based on the user's past receiving history. Furthermore, the delivery unit can analyze the user's past receiving history and select the delivery method that will yield the highest satisfaction. In this way, the optimal delivery method can be selected by referring to the user's past receiving history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's past receiving data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0072] The service provider can customize the offered items based on the user's current living situation at the time of delivery. For example, if the user moves to a new house, the service provider can provide items suitable for that house. Furthermore, if the user changes their family structure, the service provider can provide items suitable for the new family. Additionally, if the user starts keeping a pet, the service provider can provide pet-friendly items. By customizing the offered items based on the user's current living situation, more appropriate suggestions can be made. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI perform the customization of the offered items.

[0073] The service provider can estimate the user's emotions and determine the priority of items to offer based on those emotions. For example, if the user is relaxed, the service provider will offer items at a leisurely pace. If the user is in a hurry, the service provider can also offer items quickly. Furthermore, if the user is excited, the service provider can prioritize offering visually appealing items. This improves user satisfaction by prioritizing items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The delivery unit can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user lives in an urban area, the delivery unit can provide modern-style items. If the user lives in the suburbs, the delivery unit can also provide country-style items. Furthermore, if the user lives by the sea, the delivery unit can provide marine-style items. By selecting the optimal delivery method by considering the user's geographical location information, more appropriate suggestions can be made. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0075] The service provider can analyze the user's social media activity and provide relevant items at the time of delivery. For example, the service provider can provide relevant items based on the interior styles the user frequently views on Instagram. It can also provide relevant items based on interior images the user has saved on Pinterest. Furthermore, the service provider can provide relevant items based on interior posts the user has "liked" on Facebook. In this way, by analyzing the user's social media activity, it is possible to provide items that match the user's preferences. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant items.

[0076] The feedback acquisition unit can estimate the user's emotions and adjust the feedback acquisition method based on the estimated user emotions. For example, if the user is relaxed, the feedback acquisition unit may request detailed feedback. If the user is in a hurry, the feedback acquisition unit may request concise feedback. Furthermore, if the user is excited, the feedback acquisition unit may provide a visually appealing feedback form. By adjusting the feedback acquisition method according to the user's emotions, more appropriate feedback can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback acquisition unit may be performed using AI or not using AI. For example, the feedback acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0077] The feedback acquisition unit can select the optimal feedback acquisition method by referring to the user's past feedback history when acquiring feedback. For example, if the user has provided detailed feedback in the past, the feedback acquisition unit can provide a detailed feedback form. Alternatively, if the user has provided concise feedback in the past, the feedback acquisition unit can provide a concise feedback form. Furthermore, the feedback acquisition unit can analyze the user's past feedback history and select the most effective feedback acquisition method. This allows the optimal feedback acquisition method to be selected by referring to the user's past feedback history. Some or all of the above processing in the feedback acquisition unit may be performed using AI, for example, or without AI. For example, the feedback acquisition unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal feedback acquisition method.

[0078] The feedback acquisition unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback acquisition unit may prioritize detailed feedback. If the user is in a hurry, the feedback acquisition unit may also prioritize concise feedback. Furthermore, if the user is excited, the feedback acquisition unit may also prioritize visually appealing feedback. By prioritizing feedback according to the user's emotions, more appropriate feedback can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback acquisition unit may be performed using AI, or not using AI. For example, the feedback acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0079] The feedback acquisition unit can select the optimal method of acquiring feedback by considering the user's geographical location information. For example, if the user lives in an urban area, the feedback acquisition unit may prioritize online feedback acquisition. If the user lives in a suburban area, the feedback acquisition unit may also prioritize postal feedback acquisition. Furthermore, if the user lives overseas, the feedback acquisition unit may provide a multilingual feedback form. By selecting the optimal feedback acquisition method considering the user's geographical location information, more appropriate feedback can be obtained. Some or all of the above processing in the feedback acquisition unit may be performed using AI, for example, or without AI. For example, the feedback acquisition unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal feedback acquisition method.

[0080] The adjustment unit can estimate the user's emotions and adjust the adjustment algorithm based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can perform detailed adjustments and select the most suitable furniture and decorations. If the user is in a hurry, the adjustment unit can also perform quick adjustments and select basic furniture and decorations. Furthermore, if the user is excited, the adjustment unit can prioritize the selection of visually appealing furniture and decorations. This allows for more appropriate adjustments by adjusting the adjustment algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0081] The adjustment unit can improve the accuracy of adjustments by referring to the user's past feedback history during the adjustment process. For example, the adjustment unit can select items that suit the user's preferences based on feedback provided in the past. The adjustment unit can also prioritize the selection of specific brands or designs based on the user's past feedback history. Furthermore, the adjustment unit can analyze the user's past feedback history and select the items that have the highest satisfaction. In this way, the accuracy of adjustments can be improved by referring to the user's past feedback history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's past feedback data into a generating AI and have the generating AI perform the adjustment accuracy improvement.

[0082] The adjustment unit can customize the adjustment content based on the user's current living situation during the adjustment process. For example, if the user moves to a new house, the adjustment unit can select items suitable for that house. It can also select items suitable for the new family if the user changes their family structure. Furthermore, if the user starts keeping a pet, the adjustment unit can select pet-friendly items. This allows for more appropriate suggestions by customizing the adjustment content based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the adjustment content.

[0083] The adjustment unit can estimate the user's emotions and adjust the display method of the adjustment results based on the estimated user emotions. For example, if the user is tense, the adjustment unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit can provide a concise display method. This allows for improved user understanding by adjusting the display method of the adjustment results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The adjustment unit can select the optimal adjustment method during the adjustment process, taking into account the user's geographical location information. For example, if the user lives in an urban area, the adjustment unit may select modern-style items. If the user lives in a suburban area, the adjustment unit may select country-style items. Furthermore, if the user lives by the sea, the adjustment unit may select marine-style items. By selecting the optimal adjustment method considering the user's geographical location information, more appropriate suggestions can be made. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal adjustment method.

[0085] The adjustment unit can analyze the user's social media activity and make relevant adjustments during the adjustment process. For example, the adjustment unit can select relevant items based on the interior styles the user frequently views on Instagram. It can also select relevant items based on interior images the user has saved on Pinterest. Furthermore, it can select relevant items based on interior posts the user has "liked" on Facebook. This allows for adjustments tailored to the user's preferences by analyzing their social media activity. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media data into a generating AI and have the generating AI perform the relevant adjustments. === Hard Collateral 1-1 === Each of the multiple elements described above, including the input unit, analysis unit, delivery unit, feedback acquisition unit, and adjustment unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the reception device 38 of the smart device 14, where the user inputs the desired room style, functions, and budget. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the input information is analyzed and the optimal furniture and decorations are selected. The delivery unit is implemented, for example, by the output device 40 of the smart device 14, where the selected items are delivered to the user periodically. The feedback acquisition unit is implemented, for example, by the reception device 38 of the smart device 14, where user feedback is acquired. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the selection is adjusted based on the acquired feedback. The input unit can, for example, use the camera 42 of the smart device 14 to input the user's facial expression data into the generating AI, causing the generating AI to perform emotion estimation. === Hard Collateral 1-2 === Each of the multiple elements described above, including the input unit, analysis unit, delivery unit, feedback acquisition unit, and adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the smart glasses 214, and the user inputs the desired room style, function, and budget. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and analyzes the input information to select the most suitable furniture and decorations. The delivery unit is implemented by the speaker 240 of the smart glasses 214, and delivers the selected items to the user periodically. The feedback acquisition unit is implemented by the microphone 238 of the smart glasses 214, and acquires user feedback. The adjustment unit is implemented by the identification processing unit 290 of the data processing unit 12, and adjusts the selection based on the acquired feedback. The input unit can input user facial expression data to the generating AI using the camera 42 of the smart glasses 214, and cause the generating AI to perform emotion estimation. === Hard Collateral 1-3 === Each of the multiple elements described above, including the input unit, analysis unit, delivery unit, feedback acquisition unit, and adjustment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the headset terminal 314, and the user inputs the desired room style, functions, and budget. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the input information to select the most suitable furniture and decorations. The delivery unit is implemented by, for example, the speaker 240 of the headset terminal 314, and delivers the selected items to the user periodically. The feedback acquisition unit is implemented by, for example, the microphone 238 of the headset terminal 314, and acquires user feedback. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and adjusts the selection based on the acquired feedback. The input unit can, for example, input user facial expression data to the generating AI using the camera 42 of the headset terminal 314, and have the generating AI perform emotion estimation. === Hard Collateral 1-4 === Each of the multiple elements described above, including the input unit, analysis unit, delivery unit, feedback acquisition unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the robot 414, which inputs the desired room style, functions, and budget of the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input information and selects the most suitable furniture and decorations. The delivery unit is implemented by, for example, the speaker 240 of the robot 414, which periodically delivers the selected items to the user. The feedback acquisition unit is implemented by, for example, the microphone 238 of the robot 414, which acquires user feedback. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which adjusts the selection based on the acquired feedback. The input unit can, for example, use the camera 42 of the robot 414 to input the user's facial expression data into the generating AI, causing the generating AI to perform emotion estimation.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can acquire user health data and select furniture and decorative items based on their health status. For example, if a user has allergies, it can select furniture made from allergen-free materials. If a user suffers from back pain, it can select chairs and sofas with back-friendly designs. Furthermore, if a user seeks relaxation, it can provide relaxing lighting and scents. This allows the system to provide optimal furniture and decorative items tailored to the user's health condition.

[0088] The input unit can analyze the user's voice tone and speed, estimate their emotions, and adjust the input interface accordingly. For example, if the user speaks quickly, it can estimate they are in a hurry and provide concise input options. If the user speaks slowly, it can estimate they are relaxed and provide more detailed input options. Furthermore, if the user's voice trembles, it can estimate they are nervous and provide a simple, highly visible interface. This allows the input interface to be optimized based on the characteristics of the user's voice.

[0089] The service provider can acquire users' energy consumption data and suggest energy-efficient furniture and decorative items. For example, if a user wants to reduce energy consumption, they can suggest energy-saving lighting and appliances. If a user is environmentally conscious, they can also suggest furniture made from recycled materials. Furthermore, if a user wants to minimize energy consumption, they can suggest window placement and curtains that maximize natural light. This allows for optimal suggestions based on the user's energy consumption data.

[0090] The feedback acquisition unit can also estimate the user's emotions and adjust the format of the feedback based on the estimated emotions. For example, if the user is stressed, it can provide feedback in the form of concise multiple-choice options. If the user is relaxed, it can request detailed text-based feedback. Furthermore, if the user is excited, it can acquire feedback using a visually appealing interface. This allows for the provision of the most appropriate feedback format according to the user's emotions.

[0091] The adjustment unit can acquire the user's sleep data and adjust furniture and decorations based on the quality of their sleep. For example, if the user is sleep-deprived, it can suggest relaxing lighting and music. If the user is seeking comfortable sleep, it can suggest a mattress and pillow of appropriate firmness. Furthermore, if the user wants to achieve deep sleep, it can suggest blackout curtains and soundproof furniture. This allows the system to provide optimal furniture and decorations based on the user's sleep data.

[0092] The service provider can estimate the user's emotions and personalize the items offered based on those estimates. For example, if the user is relaxed, it can offer items with relaxation effects. If the user is excited, it can offer visually appealing items. Furthermore, if the user is stressed, it can offer items with stress-reducing effects. This allows the service provider to offer the most suitable items according to the user's emotions.

[0093] The analysis unit can analyze a user's purchase history and select furniture and decorative items based on past purchasing trends. For example, if a user prefers a particular brand, it can prioritize selecting items from that brand. Similarly, if a user prefers a specific design, it can select items based on that design. Furthermore, it can suggest items that complement items the user has purchased in the past. This allows the system to provide optimal furniture and decorative items based on the user's purchase history.

[0094] The input unit can also estimate the user's emotions and adjust the confirmation method based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible confirmation screen. If the user is relaxed, it can provide a more detailed confirmation screen. Furthermore, if the user is in a hurry, it can provide a concise confirmation screen that gets straight to the point. This allows the system to provide the most appropriate confirmation method according to the user's emotions.

[0095] The service provider can also customize the items offered based on the user's hobbies and preferences. For example, if a user enjoys music, they can provide high-quality speakers and musical instruments. If a user enjoys reading, they can provide lighting and chairs to create a comfortable reading environment. Furthermore, if a user enjoys cooking, they can provide kitchenware and cooking utensils. This allows the service provider to offer items that are best suited to the user's hobbies and preferences.

[0096] The feedback acquisition unit can also estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is relaxed, it can select a time to request detailed feedback. If the user is in a hurry, it can select a time to request concise feedback. Furthermore, if the user is excited, it can select a time to provide a visually appealing feedback form. This allows for the provision of optimal feedback timing tailored to the user's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The input section allows the user to enter their desired room style, functions, and budget. For example, a user might enter information such as, "I want to furnish my living room in a modern style. My budget is 100,000 yen." Step 2: The analysis unit analyzes the information entered by the input unit and selects the most suitable furniture and decorative items. For example, the analysis unit selects modern style sofas, tables, lighting, etc., based on the user's preferences. Step 3: The supply department regularly delivers the items selected by the analysis department to the user. For example, the supply department can deliver the selected items to the user once a month. Step 4: The feedback acquisition unit acquires user feedback. For example, the feedback acquisition unit can receive feedback such as, "This sofa is a little too big." Step 5: The adjustment unit adjusts the selection based on the feedback obtained by the feedback acquisition unit. For example, the adjustment unit can analyze user feedback and reflect it in the next selection.

[0099] 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.

[0100] 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 the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0101] 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.

[0102] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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. 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.

[0117] 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.

[0118] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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. 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.

[0133] 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.

[0134] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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. 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.

[0150] 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.

[0151] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] [Explanation of symbols]

[0171] 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. An input section where the user enters their desired room style, functions, and budget, An analysis unit analyzes the information input by the aforementioned input unit and selects appropriate furniture and decorative items, A supply unit that periodically delivers items selected by the analysis unit to the user, A feedback acquisition unit that obtains user feedback, The system includes an adjustment unit that adjusts the selection based on the feedback obtained by the feedback acquisition unit. A system characterized by the following features.

2. The aforementioned input unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

3. The aforementioned input unit is It analyzes the user's past input history and suggests appropriate input methods. The system according to feature 1.

4. The aforementioned input unit is As you input information, the system automatically completes the input based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned input unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned input unit is When users input data, the system suggests highly relevant styles and features based on their geographical location. The system according to feature 1.

7. The aforementioned input unit is During input, the system analyzes the user's social media activity and suggests relevant styles and features. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

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