system

Abstract

The system according to the embodiment aims to enable even users who are not good at online shopping to easily select and order products. [Solution] A system according to an embodiment includes a receiving unit, a suggestion unit, and a selection unit. The receiving unit receives a user request. The suggestion unit suggests products based on the request received by the receiving unit. The selection unit receives a user selection from the products suggested by the suggestion unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that it is difficult for users who are not good at online shopping to select and order products.

[0005] The system according to the embodiment aims to enable even users who are not good at online shopping to easily select and order products. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a selection unit. The reception unit accepts a user's request. The proposal unit proposes a product based on the request accepted by the reception unit. The selection unit accepts the user's selection from the products proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even users who are not good at online shopping to easily select and order products. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An online shopping support system according to an embodiment of the present invention is a system designed to support people who are not good at online shopping, particularly the elderly. This system allows users to place orders by conversing with AI, much like conversing with an operator in a mail-order business. For example, when a user speaks to AI about ordering a product, the AI ​​understands the user's request and suggests appropriate products. Once the user selects a product, the AI ​​proceeds with the order process and also confirms the payment method and delivery address. This allows users to enjoy shopping with peace of mind and significantly reduces the hassle. This makes the online shopping support system easy to order, even for people who are not good at online shopping, particularly the elderly. Because the AI ​​understands the user's request and makes appropriate suggestions, users can enjoy shopping with peace of mind. Furthermore, because the AI ​​automates the ordering process, it significantly reduces the user's hassle.

[0029] The online shopping support system according to the embodiment includes a reception unit, a proposal unit, and a selection unit. The reception unit accepts a user request. The user request may include, but is not limited to, a request for a product type or a service. The reception unit may accept the user request using, for example, voice input or text input. The reception unit may also analyze the user request and convert it into an appropriate format. The proposal unit proposes products based on the request accepted by the reception unit. The proposal unit may propose products based on, for example, product selection criteria or a proposal algorithm. The proposal unit may use AI to select and propose products that best suit the user's request. The proposal unit may also propose products taking into account the user's past purchase history and preferences. The selection unit accepts the user's selection from the products proposed by the proposal unit. The selection unit may accept the user's selection based on, for example, a selection interface or a selection confirmation method. The selection unit may confirm the product selected by the user and proceed with the order process. This allows the online shopping support system according to the embodiment to efficiently accept user requests, propose products, and accept a selection.

[0030] The online shopping support system includes a learning unit that learns past purchase history or preferences. The learning unit learns past purchase history and preferences. Past purchase history includes, for example, but is not limited to, the types of products purchased and the purchase dates and times. The learning unit can use AI to analyze the user's past purchase history and learn the user's preferences. The learning unit can also use survey results and past selection history to obtain the user's preferences. In this way, the learning unit can learn the user's past purchase history and preferences, thereby enabling more appropriate product suggestions.

[0031] The online shopping support system includes a confirmation unit that confirms the payment method or delivery address. The confirmation unit confirms the payment method and delivery address. Payment methods include, but are not limited to, credit card and bank transfer. The confirmation unit can use AI to confirm the user's payment method and proceed with the appropriate procedure. The confirmation unit can also confirm the user's delivery address and obtain accurate delivery information. By confirming the payment method and delivery address, the confirmation unit can smoothly proceed with the order procedure.

[0032] The suggestion unit can suggest products based on the information learned by the learning unit. The suggestion unit suggests products based on the information learned by the learning unit. The learned information includes, for example, past purchase history and user preferences, but is not limited to such examples. The suggestion unit can use AI to analyze the information learned by the learning unit and suggest products that best suit the user's requirements. In this way, the suggestion unit can suggest products that match the user's preferences by suggesting products based on the learned information.

[0033] The confirmation unit can confirm the payment method and delivery address based on the product selected by the selection unit. The confirmation unit can confirm the payment method and delivery address based on the product selected by the selection unit. The confirmation unit can use AI to confirm the user's payment method and delivery address based on the product selected by the selection unit. The confirmation unit can also suggest an appropriate payment method and delivery address based on, for example, the price and delivery conditions of the selected product. In this way, the confirmation unit can smoothly proceed with the order procedure by confirming the payment method and delivery address based on the selected product.

[0034] The reception unit can analyze the user's past request history and select an appropriate reception method. The reception unit analyzes the user's past request history and selects an appropriate reception method. The past request history includes, for example, the type of request and the date and time of the request, but is not limited to these examples. The reception unit can use AI to analyze the user's past request history and select the optimal reception method. For example, products that the user has frequently requested in the past can be automatically displayed as candidates. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest products that will be used in a specific time period based on the user's past request history. In this way, the reception unit can select the optimal reception method by analyzing the past request history.

[0035] The reception unit can perform filtering based on the user's current situation and areas of interest at the time of reception. The reception unit can perform filtering based on the user's current situation and areas of interest at the time of reception. The current situation includes, for example, the user's current activity and areas of interest, but is not limited to such examples. The reception unit can use AI to analyze the user's current situation and areas of interest and perform appropriate filtering. For example, when the user inputs their current situation, products suitable for that situation can be suggested. Also, related products can be preferentially displayed based on the user's areas of interest. Furthermore, when the user is in a specific situation, product categories corresponding to that situation can be suggested. As a result, the reception unit can perform filtering based on the user's current situation and areas of interest, enabling more appropriate product suggestions.

[0036] The reception unit can select an appropriate reception means according to the user's input method at the time of reception. The reception unit selects an appropriate reception means according to the user's input method at the time of reception. Input methods include, but are not limited to, voice input, text input, and image input, for example. The reception unit can analyze the user's input method using AI and select the optimal reception means. For example, if the user inputs a request by voice, the reception unit can receive the request using voice recognition technology. Also, if the user inputs a request by text, the reception unit can also receive the request using text analysis technology. Furthermore, if the user inputs a request by image, the reception unit can also receive the request using image recognition technology. This allows the reception unit to select the optimal reception means according to the user's input method, enabling smoother request reception.

[0037] The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information when receiving the request. The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information when receiving the request. Geographical location information includes, but is not limited to, GPS data and address information, for example. The reception unit can use AI to analyze the user's geographical location information and prioritize receiving highly relevant requests. For example, if the user is in a specific area, products related to that area can be prioritized. Also, if the user is traveling, products related to the travel destination can be prioritized. Furthermore, if the user is at home, products that can be used at home can be prioritized. In this way, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and receive related requests at the time of reception. The reception unit can analyze the user's social media activity and receive related requests at the time of reception. Social media activity includes, for example, but is not limited to, posted content and like history. The reception unit can use AI to analyze the user's social media activity and receive related requests. For example, the reception unit can prioritize suggesting products that the user mentioned on social media. The reception unit can also analyze the content of the user's social media posts and suggest related products. Furthermore, the reception unit can suggest related products by referring to the activity of the user's friends on social media. In this way, the reception unit can accept related requests by analyzing the user's social media activity.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a call. Past feedback includes, but is not limited to, survey results and reviews, for example. The reception unit can use AI to analyze the user's past feedback and select the optimal reception method. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. It can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception interface can be customized by reflecting the user's feedback. In this way, the reception unit can provide the optimal reception method by reflecting the user's past feedback.

[0040] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. Examples of the importance of the product include, but are not limited to, sales data and user ratings. The suggestion unit can use AI to analyze the importance of the product and select an appropriate level of detail of the suggestion. For example, the suggestion unit can provide a detailed description for important products. The suggestion unit can also provide a concise description for general products. Furthermore, the suggestion unit can make a detailed suggestion for products in which the user is particularly interested. As a result, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product, thereby enabling more appropriate suggestions.

[0041] The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. Product categories include, but are not limited to, electronic devices, clothing, and food. The suggestion unit can use AI to analyze the product category and select an appropriate suggestion algorithm. For example, in the case of electronic devices, the suggestion unit can make suggestions that emphasize technical details. In addition, in the case of clothing, the suggestion unit can make suggestions regarding design and materials. Furthermore, in the case of food, the suggestion unit can make suggestions regarding nutritional value and taste. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the product category.

[0042] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. Past suggestion results include, but are not limited to, examples of success rates of suggestions and user feedback. The suggestion unit can use AI to analyze the user's past suggestion results and improve the accuracy of suggestions. For example, related products can be suggested based on products the user has previously purchased. The suggestion unit can also analyze the user's preference trends from the user's past suggestion results and suggest optimal products. Furthermore, it can also suggest products that the user has previously rejected. In this way, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results.

[0043] The suggestion unit can determine the priority of suggestions based on the time of product submission when making a suggestion. The suggestion unit can determine the priority of suggestions based on the time of product submission when making a suggestion. The time of product submission includes, but is not limited to, the product release date or the product update date, for example. The suggestion unit can use AI to analyze the time of product submission and select an appropriate priority of suggestions. For example, the suggestion unit can prioritize suggestions for new products. Furthermore, the suggestion unit can also suggest seasonal products according to the season. Furthermore, the suggestion unit can prioritize suggestions for products on sale. In this way, the suggestion unit can make more appropriate suggestions by determining the priority of suggestions based on the time of product submission.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. Product relevance includes, for example, product category and user interest, but is not limited to these examples. The suggestion unit can use AI to analyze the relevance of products and select an appropriate order of suggestions. For example, the suggestion unit can prioritize suggesting products that are highly relevant to products previously purchased by the user. It can also prioritize suggesting products related to the user's current requirements. Furthermore, it can prioritize suggesting highly relevant products based on the user's areas of interest. This allows the suggestion unit to adjust the order of suggestions based on the relevance of products, enabling more appropriate suggestions.

[0045] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The user's level of expertise can include, but is not limited to, survey results and past selection history, for example. The suggestion unit can use AI to analyze the user's level of expertise and select appropriate technical terms to use. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terms. Also, if the user is a beginner, the suggestion unit can make a proposal using simple language. Furthermore, the suggestion unit can select appropriate terms to make a proposal according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise, thereby enabling more appropriate suggestions.

[0046] The selection unit can analyze the user's past selection history to select the optimal selection method when making a selection. The selection unit analyzes the user's past selection history to select the optimal selection method when making a selection. The past selection history includes, for example, but is not limited to, the type of product selected and the date and time of selection. The selection unit can use AI to analyze the user's past selection history and select the optimal selection method. For example, related products can be suggested based on products selected by the user in the past. The selection unit can also analyze the user's past selection history to suggest the optimal selection method. Furthermore, the selection unit can provide selection options by excluding products that the user has previously rejected. This allows the selection unit to select the optimal selection method by analyzing the past selection history.

[0047] The selection unit can customize the selection means based on the user's current situation at the time of selection. The selection unit customizes the selection means based on the user's current situation at the time of selection. The current situation includes, but is not limited to, the user's current activities and areas of interest, for example. The selection unit can use AI to analyze the user's current situation and select an appropriate selection means. For example, when the user inputs their current situation, selection options appropriate for that situation can be provided. Also, related products can be preferentially displayed based on the user's current situation. Furthermore, when the user is in a specific situation, selection options appropriate for that situation can be provided. In this way, the selection unit can customize the selection means based on the user's current situation, enabling a more appropriate selection.

[0048] The selection unit can improve the selection method by reflecting user feedback at the time of selection. The selection unit can improve the selection method by reflecting user feedback at the time of selection. User feedback includes, but is not limited to, survey results and reviews, for example. The selection unit can use AI to analyze the user feedback and select the optimal selection method. For example, the selection unit can suggest the optimal selection method based on feedback provided by the user in the past. It can also preferentially suggest a specific selection method based on the user's past feedback. Furthermore, the selection interface can be customized by reflecting user feedback. This allows the selection unit to improve the selection method by reflecting user feedback.

[0049] The selection unit can select the optimal selection method by taking into consideration the user's geographical location information when making a selection. The selection unit can select the optimal selection method by taking into consideration the user's geographical location information when making a selection. Geographical location information includes, but is not limited to, GPS data and address information, for example. The selection unit can use AI to analyze the user's geographical location information and select the optimal selection method. For example, if the user is in a specific area, products related to that area can be preferentially suggested. Also, if the user is traveling, products related to the travel destination can be preferentially suggested. Furthermore, if the user is at home, products that can be used at home can be preferentially suggested. In this way, the selection unit can select the optimal selection method by taking into consideration the user's geographical location information.

[0050] The selection unit can analyze the user's social media activity at the time of selection to suggest a means of selection. The selection unit can analyze the user's social media activity at the time of selection to suggest a means of selection. Social media activity includes, for example, but is not limited to, posted content and like history. The selection unit can use AI to analyze the user's social media activity and select an appropriate means of selection. For example, the selection unit can prioritize and suggest products that the user mentioned on social media. The selection unit can also analyze the content of the user's social media posts to suggest related products. Furthermore, the selection unit can suggest related products based on the activity of the user's friends on social media. In this way, the selection unit can suggest related means of selection by analyzing the user's social media activity.

[0051] The selection unit can customize the selection method by reflecting the user's past feedback when making a selection. The selection unit can customize the selection method by reflecting the user's past feedback when making a selection. Past feedback includes, but is not limited to, survey results and reviews, for example. The selection unit can use AI to analyze the user's past feedback and select the optimal selection method. For example, the selection unit can suggest the optimal selection method based on feedback provided by the user in the past. It can also preferentially suggest a specific selection method based on the user's past feedback. Furthermore, the selection interface can be customized by reflecting the user's feedback. In this way, the selection unit can provide the optimal selection method by reflecting the user's past feedback.

[0052] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit can optimize the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, examples of learning success rates and user feedback. The learning unit can analyze past learning data using AI to optimize the learning algorithm. For example, the optimal algorithm is selected based on the past learning data. The parameters of the algorithm can also be adjusted from the past learning data. Furthermore, the accuracy of the algorithm can be improved by analyzing the past learning data. In this way, the learning unit can optimize the learning algorithm by referring to the past learning data.

[0053] During learning, the learning unit can analyze fluctuations in the user's purchasing history and adjust the update frequency of the learning data. During learning, the learning unit can analyze fluctuations in the user's purchasing history and adjust the update frequency of the learning data. Fluctuations in purchasing history include, but are not limited to, purchase frequency and types of purchased products, for example. The learning unit can use AI to analyze fluctuations in the user's purchasing history and select an appropriate update frequency. For example, if the user's purchasing history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Also, if the user's purchasing history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze fluctuation patterns in the user's purchasing history and set an optimal update frequency. As a result, the learning unit can adjust the update frequency of the learning data by analyzing fluctuations in the user's purchasing history.

[0054] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit can adjust the learning algorithm by reflecting user feedback during learning. User feedback includes, but is not limited to, survey results and reviews, for example. The learning unit can analyze user feedback using AI and adjust the learning algorithm. For example, the learning algorithm can be adjusted based on feedback provided by the user. The parameters of the algorithm can also be optimized based on user feedback. Furthermore, the accuracy of the algorithm can be improved by analyzing user feedback. In this way, the learning unit can adjust the learning algorithm by reflecting user feedback.

[0055] During learning, the learning unit can weight the learning data based on the time when the purchase history was submitted. During learning, the learning unit weights the learning data based on the time when the purchase history was submitted. The time when the purchase history was submitted includes, for example, but is not limited to, the date and time of purchase and the type of purchased product. The learning unit can use AI to analyze the time when the purchase history was submitted and perform appropriate weighting. For example, the learning unit weights the learning data based on recent purchase history. The learning unit can also weight the learning data based on past purchase history. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time when the purchase history was submitted. This allows the learning unit to weight the learning data based on the time when the purchase history was submitted, enabling more appropriate learning.

[0056] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit can adjust the learning algorithm by reflecting user feedback during learning. User feedback includes, but is not limited to, survey results and reviews, for example. The learning unit can analyze user feedback using AI and adjust the learning algorithm. For example, the learning algorithm can be adjusted based on feedback provided by the user. The parameters of the algorithm can also be optimized based on user feedback. Furthermore, the accuracy of the algorithm can be improved by analyzing user feedback. In this way, the learning unit can adjust the learning algorithm by reflecting user feedback.

[0057] During learning, the learning unit can predict future purchasing trends based on the user's purchasing history. During learning, the learning unit predicts future purchasing trends based on the user's purchasing history. The purchasing history includes, for example, but is not limited to, the type of product purchased and the date and time of purchase. The learning unit can use AI to analyze the user's purchasing history and predict future purchasing trends. For example, the learning unit can analyze the user's past purchasing history and predict future purchasing trends. It can also predict future purchasing trends based on the user's purchasing patterns. Furthermore, it can predict purchasing trends for specific product categories based on the user's purchasing history. This allows the learning unit to predict future purchasing trends based on the user's purchasing history, enabling more appropriate product suggestions.

[0058] The confirmation unit can select the optimal confirmation method by analyzing the user's past confirmation history at the time of confirmation. The confirmation unit can select the optimal confirmation method by analyzing the user's past confirmation history at the time of confirmation. The past confirmation history includes, for example, the type of confirmation and the date and time of confirmation, but is not limited to these examples. The confirmation unit can use AI to analyze the user's past confirmation history and select the optimal confirmation method. For example, the confirmation unit can suggest the optimal confirmation method based on the confirmation methods used by the user in the past. The confirmation unit can also analyze the user's past confirmation history to analyze preferences and suggest the optimal confirmation method. Furthermore, the confirmation unit can suggest a method by excluding confirmation methods that the user has rejected in the past. In this way, the confirmation unit can select the optimal confirmation method by analyzing the past confirmation history.

[0059] The confirmation unit can customize the confirmation method based on the user's current situation at the time of confirmation. The confirmation unit customizes the confirmation method based on the user's current situation at the time of confirmation. The current situation includes, for example, the user's current activities and areas of interest, but is not limited to such examples. The confirmation unit can use AI to analyze the user's current situation and select an appropriate confirmation method. For example, when the user inputs their current situation, a confirmation method appropriate for that situation is provided. Also, based on the user's current situation, related confirmation methods can be preferentially displayed. Furthermore, when the user is in a specific situation, a confirmation method appropriate for that situation can be provided. As a result, the confirmation unit can customize the confirmation method based on the user's current situation, enabling more appropriate confirmation.

[0060] The confirmation unit can improve the confirmation method by reflecting user feedback during confirmation. The confirmation unit can improve the confirmation method by reflecting user feedback during confirmation. User feedback includes, but is not limited to, survey results and reviews, for example. The confirmation unit can use AI to analyze the user feedback and select the optimal confirmation method. For example, the confirmation unit can suggest the optimal confirmation method based on feedback provided by the user in the past. It can also preferentially suggest specific confirmation methods based on the user's past feedback. Furthermore, the confirmation interface can be customized by reflecting user feedback. This allows the confirmation unit to improve the confirmation method by reflecting user feedback.

[0061] The confirmation unit can select the optimal confirmation method by taking into consideration the user's geographical location information when confirming. The confirmation unit can select the optimal confirmation method by taking into consideration the user's geographical location information when confirming. Geographical location information includes, but is not limited to, GPS data and address information, for example. The confirmation unit can use AI to analyze the user's geographical location information and select the optimal confirmation method. For example, if the user is in a specific area, confirmation methods related to that area can be preferentially suggested. Also, if the user is traveling, confirmation methods related to the user's travel destination can be preferentially suggested. Furthermore, if the user is at home, confirmation methods that can be used at home can be preferentially suggested. In this way, the confirmation unit can select the optimal confirmation method by taking into consideration the user's geographical location information.

[0062] The verification unit can analyze the user's social media activity and suggest verification methods at the time of verification. The verification unit can analyze the user's social media activity and suggest verification methods at the time of verification. Social media activity includes, but is not limited to, for example, the content of posts and like history. The verification unit can use AI to analyze the user's social media activity and select an appropriate verification method. For example, the verification unit can prioritize and suggest verification methods related to products mentioned by the user on social media. The verification unit can also analyze the content of the user's social media posts and suggest related verification methods. Furthermore, the verification unit can suggest related verification methods based on the activity of the user's friends on social media. In this way, the verification unit can suggest related verification methods by analyzing the user's social media activity.

[0063] The confirmation unit can customize the confirmation method by reflecting the user's past feedback during confirmation. The confirmation unit can customize the confirmation method by reflecting the user's past feedback during confirmation. Past feedback includes, but is not limited to, survey results and reviews, for example. The confirmation unit can use AI to analyze the user's past feedback and select the optimal confirmation method. For example, the confirmation unit can suggest the optimal confirmation method based on feedback provided by the user in the past. It can also preferentially suggest specific confirmation methods based on the user's past feedback. Furthermore, the confirmation interface can be customized by reflecting the user's feedback. In this way, the confirmation unit can provide the optimal confirmation method by reflecting the user's past feedback.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The suggestion unit can suggest products taking into consideration not only the user's past purchase history but also the user's browsing history. For example, if the user frequently browses products in a specific category, products in that category can be preferentially suggested. Also, if the user likes a specific brand, products from that brand can be preferentially suggested. Furthermore, if the user likes products in a specific price range, products in that price range can be preferentially suggested. This allows the suggestion unit to make more appropriate product suggestions by taking the user's browsing history into consideration.

[0066] The confirmation unit can analyze the user's payment history and suggest the optimal payment method. For example, if the user has frequently used credit cards in the past, credit cards can be suggested with priority. Also, if the user has used bank transfers in the past, bank transfers can be suggested with priority. Furthermore, the confirmation unit can suggest the optimal payment method by taking into account the success rate of payment methods used by the user in the past. In this way, the confirmation unit can suggest the optimal payment method by analyzing the user's payment history.

[0067] The confirmation unit can analyze the user's delivery destination history and suggest the optimal delivery destination. For example, if the user has frequently used a particular address in the past, that address can be suggested preferentially. The confirmation unit can also suggest the optimal delivery destination taking into account the success rate of delivery destinations used by the user in the past. Furthermore, the confirmation unit can also suggest the optimal delivery destination taking into account the distance and delivery time of delivery destinations used by the user in the past. In this way, the confirmation unit can suggest the optimal delivery destination by analyzing the user's delivery destination history.

[0068] The suggestion unit can suggest products taking into consideration not only the user's past purchase history but also the user's social media activity. For example, it can prioritize suggesting products that the user has mentioned on social media. It can also analyze the content of the user's social media posts and suggest related products. It can also suggest related products by taking into consideration the activity of the user's friends on social media. This allows the suggestion unit to make more appropriate product suggestions by taking into consideration the user's social media activity.

[0069] The confirmation unit can improve the confirmation method by reflecting the user's past feedback. For example, the confirmation unit can suggest an optimal confirmation method based on feedback provided by the user in the past. Also, it can preferentially suggest a specific confirmation method based on the user's past feedback. Furthermore, it can also customize the confirmation interface by reflecting the user's past feedback. In this way, the confirmation unit can improve the confirmation method by reflecting the user's past feedback.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives a user request. The user request may include a product type or a service request. The reception unit can receive the user request using voice input or text input, and can also analyze the user request and convert it into an appropriate format. Step 2: The proposal unit proposes products based on the request received by the reception unit. The proposal unit proposes products based on product selection criteria and proposal algorithms, and uses AI to select the product that best suits the user's request. It can also propose products taking into account the user's past purchase history and preferences. Step 3: The selection unit accepts the user's selection from the products suggested by the suggestion unit. The selection unit accepts the user's selection based on the selection interface and the selection confirmation method, confirms the product selected by the user, and allows the user to proceed with the order procedure.

[0072] (Example 2) An online shopping support system according to an embodiment of the present invention is a system designed to support people who are not good at online shopping, particularly the elderly. This system allows users to place orders by conversing with AI, much like conversing with an operator in a mail-order business. For example, when a user speaks to AI about ordering a product, the AI ​​understands the user's request and suggests appropriate products. Once the user selects a product, the AI ​​proceeds with the order process and also confirms the payment method and delivery address. This allows users to enjoy shopping with peace of mind and significantly reduces the hassle. This makes the online shopping support system easy to order, even for people who are not good at online shopping, particularly the elderly. Because the AI ​​understands the user's request and makes appropriate suggestions, users can enjoy shopping with peace of mind. Furthermore, because the AI ​​automates the ordering process, it significantly reduces the user's hassle.

[0073] The online shopping support system according to the embodiment includes a reception unit, a proposal unit, and a selection unit. The reception unit accepts a user request. The user request may include, but is not limited to, a request for a product type or a service. The reception unit may accept the user request using, for example, voice input or text input. The reception unit may also analyze the user request and convert it into an appropriate format. The proposal unit proposes products based on the request accepted by the reception unit. The proposal unit may propose products based on, for example, product selection criteria or a proposal algorithm. The proposal unit may use AI to select and propose products that best suit the user's request. The proposal unit may also propose products taking into account the user's past purchase history and preferences. The selection unit accepts the user's selection from the products proposed by the proposal unit. The selection unit may accept the user's selection based on, for example, a selection interface or a selection confirmation method. The selection unit may confirm the product selected by the user and proceed with the order process. This allows the online shopping support system according to the embodiment to efficiently accept user requests, propose products, and accept a selection.

[0074] The online shopping support system includes a learning unit that learns past purchase history or preferences. The learning unit learns past purchase history and preferences. Past purchase history includes, for example, but is not limited to, the types of products purchased and the purchase dates and times. The learning unit can use AI to analyze the user's past purchase history and learn the user's preferences. The learning unit can also use survey results and past selection history to obtain the user's preferences. In this way, the learning unit can learn the user's past purchase history and preferences, thereby enabling more appropriate product suggestions.

[0075] The online shopping support system includes a confirmation unit that confirms the payment method or delivery address. The confirmation unit confirms the payment method and delivery address. Payment methods include, but are not limited to, credit card and bank transfer. The confirmation unit can use AI to confirm the user's payment method and proceed with the appropriate procedure. The confirmation unit can also confirm the user's delivery address and obtain accurate delivery information. By confirming the payment method and delivery address, the confirmation unit can smoothly proceed with the order procedure.

[0076] The suggestion unit can suggest products based on the information learned by the learning unit. The suggestion unit suggests products based on the information learned by the learning unit. The learned information includes, for example, past purchase history and user preferences, but is not limited to such examples. The suggestion unit can use AI to analyze the information learned by the learning unit and suggest products that best suit the user's requirements. In this way, the suggestion unit can suggest products that match the user's preferences by suggesting products based on the learned information.

[0077] The confirmation unit can confirm the payment method and delivery address based on the product selected by the selection unit. The confirmation unit can confirm the payment method and delivery address based on the product selected by the selection unit. The confirmation unit can use AI to confirm the user's payment method and delivery address based on the product selected by the selection unit. The confirmation unit can also suggest an appropriate payment method and delivery address based on, for example, the price and delivery conditions of the selected product. In this way, the confirmation unit can smoothly proceed with the order procedure by confirming the payment method and delivery address based on the selected product.

[0078] The reception unit can estimate the user's emotion and adjust the request reception method based on the estimated user emotion. The reception unit can estimate the user's emotion and adjust the request reception method based on the estimated user emotion. User emotions include, but are not limited to, stress, relaxation, and hurry. The reception unit can use AI to estimate the user's emotion and select an appropriate reception method. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept requests. This allows the reception unit to adjust the request reception method according to the user's emotion, thereby enabling more appropriate responses. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The reception unit can analyze the user's past request history and select an appropriate reception method. The reception unit analyzes the user's past request history and selects an appropriate reception method. The past request history includes, for example, the type of request and the date and time of the request, but is not limited to these examples. The reception unit can use AI to analyze the user's past request history and select the optimal reception method. For example, products that the user has frequently requested in the past can be automatically displayed as candidates. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest products that will be used in a specific time period based on the user's past request history. In this way, the reception unit can select the optimal reception method by analyzing the past request history.

[0080] The reception unit can perform filtering based on the user's current situation and areas of interest at the time of reception. The reception unit can perform filtering based on the user's current situation and areas of interest at the time of reception. The current situation includes, for example, the user's current activity and areas of interest, but is not limited to such examples. The reception unit can use AI to analyze the user's current situation and areas of interest and perform appropriate filtering. For example, when the user inputs their current situation, products suitable for that situation can be suggested. Also, related products can be preferentially displayed based on the user's areas of interest. Furthermore, when the user is in a specific situation, product categories corresponding to that situation can be suggested. As a result, the reception unit can perform filtering based on the user's current situation and areas of interest, enabling more appropriate product suggestions.

[0081] The reception unit can select an appropriate reception means according to the user's input method at the time of reception. The reception unit selects an appropriate reception means according to the user's input method at the time of reception. Input methods include, but are not limited to, voice input, text input, and image input, for example. The reception unit can analyze the user's input method using AI and select the optimal reception means. For example, if the user inputs a request by voice, the reception unit can receive the request using voice recognition technology. Also, if the user inputs a request by text, the reception unit can also receive the request using text analysis technology. Furthermore, if the user inputs a request by image, the reception unit can also receive the request using image recognition technology. This allows the reception unit to select the optimal reception means according to the user's input method, enabling smoother request reception.

[0082] The reception unit can estimate the user's emotion and prioritize requests based on the estimated user emotion. The reception unit can estimate the user's emotion and prioritize requests based on the estimated user emotion. Examples of user emotions include, but are not limited to, urgent, relaxed, and stressed. The reception unit can estimate the user's emotion using AI and determine appropriate priorities. For example, if the user makes an urgent request, the reception unit can process the request with priority. Also, if the user is relaxed, the reception unit can process the request with normal priority. Furthermore, if the user is stressed, the reception unit can quickly process the request. This allows the reception unit to prioritize requests based on the user's emotion, enabling more appropriate responses. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information when receiving the request. The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information when receiving the request. Geographical location information includes, but is not limited to, GPS data and address information, for example. The reception unit can use AI to analyze the user's geographical location information and prioritize receiving highly relevant requests. For example, if the user is in a specific area, products related to that area can be prioritized. Also, if the user is traveling, products related to the travel destination can be prioritized. Furthermore, if the user is at home, products that can be used at home can be prioritized. In this way, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information.

[0084] The reception unit can analyze the user's social media activity and receive related requests at the time of reception. The reception unit can analyze the user's social media activity and receive related requests at the time of reception. Social media activity includes, for example, but is not limited to, posted content and like history. The reception unit can use AI to analyze the user's social media activity and receive related requests. For example, the reception unit can prioritize suggesting products that the user mentioned on social media. The reception unit can also analyze the content of the user's social media posts and suggest related products. Furthermore, the reception unit can suggest related products by referring to the activity of the user's friends on social media. In this way, the reception unit can accept related requests by analyzing the user's social media activity.

[0085] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a call. Past feedback includes, but is not limited to, survey results and reviews, for example. The reception unit can use AI to analyze the user's past feedback and select the optimal reception method. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. It can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception interface can be customized by reflecting the user's feedback. In this way, the reception unit can provide the optimal reception method by reflecting the user's past feedback.

[0086] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. User emotions include, but are not limited to, relaxed, rushed, and excited. The suggestion unit can estimate the user's emotion using AI and select an appropriate way to express the suggestion. For example, if the user is relaxed, the suggestion unit can provide a detailed suggestion. If the user is rushed, the suggestion unit can provide a concise suggestion. Furthermore, if the user is excited, the suggestion unit can provide a visually appealing suggestion. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion, thereby enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. Examples of the importance of the product include, but are not limited to, sales data and user ratings. The suggestion unit can use AI to analyze the importance of the product and select an appropriate level of detail of the suggestion. For example, the suggestion unit can provide a detailed description for important products. The suggestion unit can also provide a concise description for general products. Furthermore, the suggestion unit can make a detailed suggestion for products in which the user is particularly interested. As a result, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product, thereby enabling more appropriate suggestions.

[0088] The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. Product categories include, but are not limited to, electronic devices, clothing, and food. The suggestion unit can use AI to analyze the product category and select an appropriate suggestion algorithm. For example, in the case of electronic devices, the suggestion unit can make suggestions that emphasize technical details. In addition, in the case of clothing, the suggestion unit can make suggestions regarding design and materials. Furthermore, in the case of food, the suggestion unit can make suggestions regarding nutritional value and taste. This allows the suggestion unit to make more appropriate suggestions by applying different suggestion algorithms depending on the product category.

[0089] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. Past suggestion results include, but are not limited to, examples of success rates of suggestions and user feedback. The suggestion unit can use AI to analyze the user's past suggestion results and improve the accuracy of suggestions. For example, related products can be suggested based on products the user has previously purchased. The suggestion unit can also analyze the user's preference trends from the user's past suggestion results and suggest optimal products. Furthermore, it can also suggest products that the user has previously rejected. In this way, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results.

[0090] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. User emotions include, but are not limited to, being in a hurry, being relaxed, being excited, etc. The suggestion unit can estimate the user's emotion using AI and select an appropriate length for the suggestion. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. Alternatively, if the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a visually stimulating suggestion. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion, thereby enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The suggestion unit can determine the priority of suggestions based on the time of product submission when making a suggestion. The suggestion unit can determine the priority of suggestions based on the time of product submission when making a suggestion. The time of product submission includes, but is not limited to, the product release date or the product update date, for example. The suggestion unit can use AI to analyze the time of product submission and select an appropriate priority of suggestions. For example, the suggestion unit can prioritize suggestions for new products. Furthermore, the suggestion unit can also suggest seasonal products according to the season. Furthermore, the suggestion unit can prioritize suggestions for products on sale. In this way, the suggestion unit can make more appropriate suggestions by determining the priority of suggestions based on the time of product submission.

[0092] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. Product relevance includes, for example, product category and user interest, but is not limited to these examples. The suggestion unit can use AI to analyze the relevance of products and select an appropriate order of suggestions. For example, the suggestion unit can prioritize suggesting products that are highly relevant to products previously purchased by the user. It can also prioritize suggesting products related to the user's current requirements. Furthermore, it can prioritize suggesting highly relevant products based on the user's areas of interest. This allows the suggestion unit to adjust the order of suggestions based on the relevance of products, enabling more appropriate suggestions.

[0093] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. The user's level of expertise can include, but is not limited to, survey results and past selection history, for example. The suggestion unit can use AI to analyze the user's level of expertise and select appropriate technical terms to use. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terms. Also, if the user is a beginner, the suggestion unit can make a proposal using simple language. Furthermore, the suggestion unit can select appropriate terms to make a proposal according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise, thereby enabling more appropriate suggestions.

[0094] The selection unit can estimate the user's emotion and adjust the selection method based on the estimated user's emotion. The selection unit can estimate the user's emotion and adjust the selection method based on the estimated user's emotion. User emotions include, but are not limited to, relaxed, rushed, and excited. The selection unit can estimate the user's emotion using AI and select an appropriate selection method. For example, if the user is relaxed, the selection unit can provide detailed selection options. If the user is rushed, the selection unit can provide concise selection options. Furthermore, if the user is excited, the selection unit can provide visually appealing selection options. This allows the selection unit to adjust the selection method according to the user's emotion, enabling a more appropriate selection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The selection unit can analyze the user's past selection history to select the optimal selection method when making a selection. The selection unit analyzes the user's past selection history to select the optimal selection method when making a selection. The past selection history includes, for example, but is not limited to, the type of product selected and the date and time of selection. The selection unit can use AI to analyze the user's past selection history and select the optimal selection method. For example, related products can be suggested based on products selected by the user in the past. The selection unit can also analyze the user's past selection history to suggest the optimal selection method. Furthermore, the selection unit can provide selection options by excluding products that the user has previously rejected. This allows the selection unit to select the optimal selection method by analyzing the past selection history.

[0096] The selection unit can customize the selection means based on the user's current situation at the time of selection. The selection unit customizes the selection means based on the user's current situation at the time of selection. The current situation includes, but is not limited to, the user's current activities and areas of interest, for example. The selection unit can use AI to analyze the user's current situation and select an appropriate selection means. For example, when the user inputs their current situation, selection options appropriate for that situation can be provided. Also, related products can be preferentially displayed based on the user's current situation. Furthermore, when the user is in a specific situation, selection options appropriate for that situation can be provided. In this way, the selection unit can customize the selection means based on the user's current situation, enabling a more appropriate selection.

[0097] The selection unit can improve the selection method by reflecting user feedback at the time of selection. The selection unit can improve the selection method by reflecting user feedback at the time of selection. User feedback includes, but is not limited to, survey results and reviews, for example. The selection unit can use AI to analyze the user feedback and select the optimal selection method. For example, the selection unit can suggest the optimal selection method based on feedback provided by the user in the past. It can also preferentially suggest a specific selection method based on the user's past feedback. Furthermore, the selection interface can be customized by reflecting user feedback. This allows the selection unit to improve the selection method by reflecting user feedback.

[0098] The selection unit can estimate the user's emotions and determine the priority of selection based on the estimated user emotions. The selection unit can estimate the user's emotions and determine the priority of selection based on the estimated user emotions. User emotions include, but are not limited to, urgent, relaxed, and stressed. The selection unit can estimate the user's emotions using AI and determine appropriate priorities. For example, if the user selects urgent, the selection unit processes that selection with priority. Also, if the user is relaxed, the selection unit can process the selection with normal priority. Furthermore, if the user is stressed, the selection unit can quickly process the selection. This allows the selection unit to determine the priority of selection according to the user's emotions, thereby enabling a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The selection unit can select the optimal selection method by taking into consideration the user's geographical location information when making a selection. The selection unit can select the optimal selection method by taking into consideration the user's geographical location information when making a selection. Geographical location information includes, but is not limited to, GPS data and address information, for example. The selection unit can use AI to analyze the user's geographical location information and select the optimal selection method. For example, if the user is in a specific area, products related to that area can be preferentially suggested. Also, if the user is traveling, products related to the travel destination can be preferentially suggested. Furthermore, if the user is at home, products that can be used at home can be preferentially suggested. In this way, the selection unit can select the optimal selection method by taking into consideration the user's geographical location information.

[0100] The selection unit can analyze the user's social media activity at the time of selection to suggest a means of selection. The selection unit can analyze the user's social media activity at the time of selection to suggest a means of selection. Social media activity includes, for example, but is not limited to, posted content and like history. The selection unit can use AI to analyze the user's social media activity and select an appropriate means of selection. For example, the selection unit can prioritize and suggest products that the user mentioned on social media. The selection unit can also analyze the content of the user's social media posts to suggest related products. Furthermore, the selection unit can suggest related products based on the activity of the user's friends on social media. In this way, the selection unit can suggest related means of selection by analyzing the user's social media activity.

[0101] The selection unit can customize the selection method by reflecting the user's past feedback when making a selection. The selection unit can customize the selection method by reflecting the user's past feedback when making a selection. Past feedback includes, but is not limited to, survey results and reviews, for example. The selection unit can use AI to analyze the user's past feedback and select the optimal selection method. For example, the selection unit can suggest the optimal selection method based on feedback provided by the user in the past. It can also preferentially suggest a specific selection method based on the user's past feedback. Furthermore, the selection interface can be customized by reflecting the user's feedback. In this way, the selection unit can provide the optimal selection method by reflecting the user's past feedback.

[0102] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. User emotions include, but are not limited to, relaxed, rushed, and excited. The learning unit can estimate the user's emotions using AI and select appropriate training data. For example, if the user is relaxed, the learning unit can select detailed training data. Alternatively, if the user is rushed, the learning unit can select concise training data. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. This allows the learning unit to select training data according to the user's emotions, enabling more appropriate learning. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit can optimize the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, examples of learning success rates and user feedback. The learning unit can analyze past learning data using AI to optimize the learning algorithm. For example, the optimal algorithm is selected based on the past learning data. The parameters of the algorithm can also be adjusted from the past learning data. Furthermore, the accuracy of the algorithm can be improved by analyzing the past learning data. In this way, the learning unit can optimize the learning algorithm by referring to the past learning data.

[0104] During learning, the learning unit can analyze fluctuations in the user's purchasing history and adjust the update frequency of the learning data. During learning, the learning unit can analyze fluctuations in the user's purchasing history and adjust the update frequency of the learning data. Fluctuations in purchasing history include, but are not limited to, purchase frequency and types of purchased products, for example. The learning unit can use AI to analyze fluctuations in the user's purchasing history and select an appropriate update frequency. For example, if the user's purchasing history fluctuates frequently, the learning unit can increase the update frequency of the learning data. Also, if the user's purchasing history is stable, the learning unit can decrease the update frequency of the learning data. Furthermore, the learning unit can analyze fluctuation patterns in the user's purchasing history and set an optimal update frequency. As a result, the learning unit can adjust the update frequency of the learning data by analyzing fluctuations in the user's purchasing history.

[0105] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit can adjust the learning algorithm by reflecting user feedback during learning. User feedback includes, but is not limited to, survey results and reviews, for example. The learning unit can analyze user feedback using AI and adjust the learning algorithm. For example, the learning algorithm can be adjusted based on feedback provided by the user. The parameters of the algorithm can also be optimized based on user feedback. Furthermore, the accuracy of the algorithm can be improved by analyzing user feedback. In this way, the learning unit can adjust the learning algorithm by reflecting user feedback.

[0106] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. User emotions include, but are not limited to, relaxed, rushed, and excited. The learning unit can estimate the user's emotions using AI and select an appropriate learning frequency. For example, if the user is relaxed, the learning unit can increase the learning frequency. Also, if the user is rushed, the learning unit can decrease the learning frequency. Furthermore, if the user is excited, the learning unit can adjust the learning frequency. In this way, the learning unit can adjust the learning frequency according to the user's emotions, enabling more appropriate learning. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] During learning, the learning unit can weight the learning data based on the time when the purchase history was submitted. During learning, the learning unit weights the learning data based on the time when the purchase history was submitted. The time when the purchase history was submitted includes, for example, but is not limited to, the date and time of purchase and the type of purchased product. The learning unit can use AI to analyze the time when the purchase history was submitted and perform appropriate weighting. For example, the learning unit weights the learning data based on recent purchase history. The learning unit can also weight the learning data based on past purchase history. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time when the purchase history was submitted. This allows the learning unit to weight the learning data based on the time when the purchase history was submitted, enabling more appropriate learning.

[0108] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit can adjust the learning algorithm by reflecting user feedback during learning. User feedback includes, but is not limited to, survey results and reviews, for example. The learning unit can analyze user feedback using AI and adjust the learning algorithm. For example, the learning algorithm can be adjusted based on feedback provided by the user. The parameters of the algorithm can also be optimized based on user feedback. Furthermore, the accuracy of the algorithm can be improved by analyzing user feedback. In this way, the learning unit can adjust the learning algorithm by reflecting user feedback.

[0109] During learning, the learning unit can predict future purchasing trends based on the user's purchasing history. During learning, the learning unit predicts future purchasing trends based on the user's purchasing history. The purchasing history includes, for example, but is not limited to, the type of product purchased and the date and time of purchase. The learning unit can use AI to analyze the user's purchasing history and predict future purchasing trends. For example, the learning unit can analyze the user's past purchasing history and predict future purchasing trends. It can also predict future purchasing trends based on the user's purchasing patterns. Furthermore, it can predict purchasing trends for specific product categories based on the user's purchasing history. This allows the learning unit to predict future purchasing trends based on the user's purchasing history, enabling more appropriate product suggestions.

[0110] The confirmation unit can estimate the user's emotion and adjust the confirmation method based on the estimated user's emotion. The confirmation unit can estimate the user's emotion and adjust the confirmation method based on the estimated user's emotion. User emotions include, but are not limited to, relaxed, rushed, and excited. The confirmation unit can estimate the user's emotion using AI and select an appropriate confirmation method. For example, if the user is relaxed, the confirmation unit can perform detailed confirmation. Alternatively, if the user is rushed, the confirmation unit can perform brief confirmation. Furthermore, if the user is excited, the confirmation unit can perform visually appealing confirmation. This allows the confirmation unit to adjust the confirmation method according to the user's emotion, thereby enabling more appropriate confirmation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The confirmation unit can select the optimal confirmation method by analyzing the user's past confirmation history at the time of confirmation. The confirmation unit can select the optimal confirmation method by analyzing the user's past confirmation history at the time of confirmation. The past confirmation history includes, for example, the type of confirmation and the date and time of confirmation, but is not limited to these examples. The confirmation unit can use AI to analyze the user's past confirmation history and select the optimal confirmation method. For example, the confirmation unit can suggest the optimal confirmation method based on the confirmation methods used by the user in the past. The confirmation unit can also analyze the user's past confirmation history to analyze preferences and suggest the optimal confirmation method. Furthermore, the confirmation unit can suggest a method by excluding confirmation methods that the user has rejected in the past. In this way, the confirmation unit can select the optimal confirmation method by analyzing the past confirmation history.

[0112] The confirmation unit can customize the confirmation method based on the user's current situation at the time of confirmation. The confirmation unit customizes the confirmation method based on the user's current situation at the time of confirmation. The current situation includes, for example, the user's current activities and areas of interest, but is not limited to such examples. The confirmation unit can use AI to analyze the user's current situation and select an appropriate confirmation method. For example, when the user inputs their current situation, a confirmation method appropriate for that situation is provided. Also, based on the user's current situation, related confirmation methods can be preferentially displayed. Furthermore, when the user is in a specific situation, a confirmation method appropriate for that situation can be provided. As a result, the confirmation unit can customize the confirmation method based on the user's current situation, enabling more appropriate confirmation.

[0113] The confirmation unit can improve the confirmation method by reflecting user feedback during confirmation. The confirmation unit can improve the confirmation method by reflecting user feedback during confirmation. User feedback includes, but is not limited to, survey results and reviews, for example. The confirmation unit can use AI to analyze the user feedback and select the optimal confirmation method. For example, the confirmation unit can suggest the optimal confirmation method based on feedback provided by the user in the past. It can also preferentially suggest specific confirmation methods based on the user's past feedback. Furthermore, the confirmation interface can be customized by reflecting user feedback. This allows the confirmation unit to improve the confirmation method by reflecting user feedback.

[0114] The confirmation unit can estimate the user's emotions and determine the priority of confirmations based on the estimated user emotions. The confirmation unit can estimate the user's emotions and determine the priority of confirmations based on the estimated user emotions. User emotions include, but are not limited to, urgent, relaxed, and stressed. The confirmation unit can estimate the user's emotions using AI and determine appropriate priorities. For example, if the user is making an urgent confirmation, the confirmation unit can process that confirmation with priority. Also, if the user is relaxed, the confirmation unit can process the confirmation with normal priority. Furthermore, if the user is feeling stressed, the confirmation unit can quickly process the confirmation. This allows the confirmation unit to determine the priority of confirmations according to the user's emotions, thereby enabling more appropriate responses. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The confirmation unit can select the optimal confirmation method by taking into consideration the user's geographical location information when confirming. The confirmation unit can select the optimal confirmation method by taking into consideration the user's geographical location information when confirming. Geographical location information includes, but is not limited to, GPS data and address information, for example. The confirmation unit can use AI to analyze the user's geographical location information and select the optimal confirmation method. For example, if the user is in a specific area, confirmation methods related to that area can be preferentially suggested. Also, if the user is traveling, confirmation methods related to the user's travel destination can be preferentially suggested. Furthermore, if the user is at home, confirmation methods that can be used at home can be preferentially suggested. In this way, the confirmation unit can select the optimal confirmation method by taking into consideration the user's geographical location information.

[0116] The verification unit can analyze the user's social media activity and suggest verification methods at the time of verification. The verification unit can analyze the user's social media activity and suggest verification methods at the time of verification. Social media activity includes, but is not limited to, for example, the content of posts and like history. The verification unit can use AI to analyze the user's social media activity and select an appropriate verification method. For example, the verification unit can prioritize and suggest verification methods related to products mentioned by the user on social media. The verification unit can also analyze the content of the user's social media posts and suggest related verification methods. Furthermore, the verification unit can suggest related verification methods based on the activity of the user's friends on social media. In this way, the verification unit can suggest related verification methods by analyzing the user's social media activity.

[0117] The confirmation unit can customize the confirmation method by reflecting the user's past feedback during confirmation. The confirmation unit can customize the confirmation method by reflecting the user's past feedback during confirmation. Past feedback includes, but is not limited to, survey results and reviews, for example. The confirmation unit can use AI to analyze the user's past feedback and select the optimal confirmation method. For example, the confirmation unit can suggest the optimal confirmation method based on feedback provided by the user in the past. It can also preferentially suggest specific confirmation methods based on the user's past feedback. Furthermore, the confirmation interface can be customized by reflecting the user's feedback. In this way, the confirmation unit can provide the optimal confirmation method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, selection unit, learning unit, and confirmation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's request using the microphone 38B or touch panel 38A of the smart device 14, and the request is analyzed by the control unit 46A. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests products based on the user's request. The selection unit receives the user's selection using the display 40A or touch panel 38A of the smart device 14. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns past purchase history and preferences. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms the payment method and delivery address. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, selection unit, learning unit, and confirmation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's request using the microphone 238 of the smart glasses 214, and the request is analyzed by the control unit 46A. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests products based on the user's request. The selection unit receives the user's selection using the display or touch panel of the smart glasses 214. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns past purchase history and preferences. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms the payment method and delivery address. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, selection unit, learning unit, and confirmation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's request using the microphone 238 of the headset type terminal 314, and the request is analyzed by the control unit 46A. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests products based on the user's request. The selection unit receives the user's selection using the display 343 or touch panel of the headset type terminal 314. The learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns past purchase history and preferences. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and confirms the payment method and delivery address. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, selection unit, learning unit, and confirmation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's request using the microphone 238 of the robot 414, and the request is analyzed by the control unit 46A. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests products based on the user's request. The selection unit receives the user's selection using the display or touch panel of the robot 414. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns past purchase history and preferences. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12 and confirms the payment method and delivery address.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The reception unit can analyze the user's tone of voice and speaking style to estimate the user's level of urgency. For example, if the user is in a hurry, the reception unit can provide a simple interface to respond quickly. If the user is relaxed, the reception unit can provide detailed options to allow the user to make a selection slowly. Furthermore, if the user is excited, the reception unit can provide a visually appealing interface to calm the user's excitement. This allows the reception unit to respond more appropriately based on the user's tone of voice and speaking style.

[0120] The suggestion unit can suggest products taking into consideration not only the user's past purchase history but also the user's browsing history. For example, if the user frequently browses products in a specific category, products in that category can be preferentially suggested. Also, if the user likes a specific brand, products from that brand can be preferentially suggested. Furthermore, if the user likes products in a specific price range, products in that price range can be preferentially suggested. This allows the suggestion unit to make more appropriate product suggestions by taking the user's browsing history into consideration.

[0121] The confirmation unit can analyze the user's payment history and suggest the optimal payment method. For example, if the user has frequently used credit cards in the past, credit cards can be suggested with priority. Also, if the user has used bank transfers in the past, bank transfers can be suggested with priority. Furthermore, the confirmation unit can suggest the optimal payment method by taking into account the success rate of payment methods used by the user in the past. In this way, the confirmation unit can suggest the optimal payment method by analyzing the user's payment history.

[0122] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make detailed suggestions. If the user is in a hurry, the suggestion unit can also make concise suggestions. Furthermore, if the user is excited, the suggestion unit can also make visually appealing suggestions. In this way, the suggestion unit can adjust the timing of suggestions according to the user's emotions, thereby enabling more appropriate suggestions.

[0123] The confirmation unit can analyze the user's delivery destination history and suggest the optimal delivery destination. For example, if the user has frequently used a particular address in the past, that address can be suggested preferentially. The confirmation unit can also suggest the optimal delivery destination taking into account the success rate of delivery destinations used by the user in the past. Furthermore, the confirmation unit can also suggest the optimal delivery destination taking into account the distance and delivery time of delivery destinations used by the user in the past. In this way, the confirmation unit can suggest the optimal delivery destination by analyzing the user's delivery destination history.

[0124] The reception unit can estimate the user's emotions and customize the reception interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept requests. This allows the reception unit to customize the reception interface according to the user's emotions, enabling more appropriate responses.

[0125] The suggestion unit can suggest products taking into consideration not only the user's past purchase history but also the user's social media activity. For example, it can prioritize suggesting products that the user has mentioned on social media. It can also analyze the content of the user's social media posts and suggest related products. It can also suggest related products by taking into consideration the activity of the user's friends on social media. This allows the suggestion unit to make more appropriate product suggestions by taking into consideration the user's social media activity.

[0126] The selection unit can estimate the user's emotion and customize the selection interface based on the estimated user's emotion. For example, if the user is relaxed, the selection unit can provide detailed selection options. If the user is in a hurry, the selection unit can provide concise selection options. Furthermore, if the user is excited, the selection unit can provide visually appealing selection options. In this way, the selection unit can customize the selection interface according to the user's emotion, thereby enabling a more appropriate selection.

[0127] The confirmation unit can improve the confirmation method by reflecting the user's past feedback. For example, the confirmation unit can suggest an optimal confirmation method based on feedback provided by the user in the past. Also, it can preferentially suggest a specific confirmation method based on the user's past feedback. Furthermore, it can also customize the confirmation interface by reflecting the user's past feedback. In this way, the confirmation unit can improve the confirmation method by reflecting the user's past feedback.

[0128] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, the learning unit selects detailed learning data. If the user is in a hurry, the learning unit can select concise learning data. Furthermore, if the user is excited, the learning unit can select visually stimulating learning data. This allows the learning unit to select learning data according to the user's emotions, enabling more appropriate learning.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit receives a user request. The user request may include a product type or a service request. The reception unit can receive the user request using voice input or text input, and can also analyze the user request and convert it into an appropriate format. Step 2: The proposal unit proposes products based on the request received by the reception unit. The proposal unit proposes products based on product selection criteria and proposal algorithms, and uses AI to select the product that best suits the user's request. It can also propose products taking into account the user's past purchase history and preferences. Step 3: The selection unit accepts the user's selection from the products suggested by the suggestion unit. The selection unit accepts the user's selection based on the selection interface and the selection confirmation method, confirms the product selected by the user, and allows the user to proceed with the order procedure.

[0131] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

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

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a 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.

[0169] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0186] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0187] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0192] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0193] 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.

[0194] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0195] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0196] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0197] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0199] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0200] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0201] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a user request; a suggestion unit that suggests products based on the request received by the reception unit; a selection unit that receives a user's selection from the products suggested by the suggestion unit; Equipped with A system characterized by:

2. Equipped with a learning unit that learns past purchase history or preferences 2. The system of claim 1.

3. Equipped with a confirmation section for confirming payment method or delivery address 2. The system of claim 1.

4. The proposal unit Proposing products based on the information learned by the learning unit 3. The system of claim 2.

5. The confirmation unit Confirming the payment method and delivery address based on the product selected by the selection unit 4. The system of claim 3.

6. The reception unit Estimate the user's emotions and adjust the method of accepting requests based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past request history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit At the time of check-in, filtering is performed based on the user's current situation and interests.

2. The system of claim 1.

9. The reception unit At the time of reception, the appropriate reception method is selected according to the user's input method.

2. The system of claim 1.

10. The reception unit Inferring user emotions and prioritizing requests based on the estimated user emotions 2. The system of claim 1.

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