System

Abstract

An object of a system according to an embodiment is to easily select, purchase, and deliver a gift reflecting the emotion and intention of a user.SOLUTION: A system includes an emotion input unit, an analysis unit, a selection unit, a purchase unit, and a delivery unit. The emotion input unit inputs a user's emotion or intention. The analysis unit analyzes the information input by the emotion input unit. The selection unit selects an optimal gift based on the information analyzed by the analysis unit. The purchase unit purchases the gift selected by the selection unit. The delivery unit delivers the gift purchased by the purchase unit to the other party.SELECTED DRAWING: Figure 1

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 technology has the drawback of making the process of selecting, purchasing, and delivering gifts that appropriately reflect the user's emotions and intentions complicated.

[0005] The system according to the embodiment aims to enable a user to easily select, purchase, and deliver gifts that reflect the user's emotions and intentions. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion input unit, an analysis unit, a selection unit, a purchase unit, and a delivery unit. The emotion input unit inputs a user's emotion or intention. The analysis unit analyzes the information input by the emotion input unit. The selection unit selects an optimal gift based on the information analyzed by the analysis unit. The purchase unit purchases the gift selected by the selection unit. The delivery unit delivers the gift purchased by the purchase unit to the recipient. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily select, purchase, and deliver gifts that reflect their emotions and intentions. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The gift selection system according to the embodiment of the present invention is a system that selects the most suitable gift based on the user's feelings and intentions, and automates the purchase and delivery of the gift, thereby reducing the burden on the user and enabling the user to convey their feelings and intentions to the recipient.

[0029] The gift selection system according to the embodiment includes an emotion input unit, an analysis unit, a selection unit, a purchasing unit, and a delivery unit. The emotion input unit inputs a user's emotions and intentions. For example, the user inputs emotions and intentions in text format. The emotion input unit can also analyze voice input and estimate emotions from the tone and speed of the voice. For example, the emotion is estimated by analyzing the tone and speed of the voice using voice recognition technology. The emotion input unit can also check the consistency of emotions and intentions by referring to the user's past message history. For example, the past message history is stored in a database and the consistency of emotions and intentions is checked. The analysis unit analyzes the information input by the emotion input unit. For example, the analysis unit analyzes prompts using natural language processing technology to understand the context. The analysis unit can also track changes in emotions and intentions by referring to the user's past input data. For example, the analysis unit tracks changes in emotions and intentions based on the past input data. The analysis unit can also monitor the user's emotions in real time during prompt analysis using an emotion estimation function and reflect the results of the analysis. For example, the analysis unit can analyze facial expressions and voice during input to monitor emotions in real time. The selection unit selects an optimal gift based on the information analyzed by the analysis unit. For example, the selection unit selects a gift based on the user's emotions and intentions. The selection unit can also refer to the recipient's past gift-giving history to reflect the recipient's preferences and tendencies. For example, the selection unit analyzes preferences and tendencies based on the recipient's past gift-giving history. The selection unit can also suggest optimal gifts according to seasons or events. For example, the selection unit can suggest gifts suited to events such as Christmas and Valentine's Day. The purchase unit purchases the gift selected by the selection unit. For example, the purchase unit purchases the gift from an online store. The purchase unit can also compare multiple online stores to select the optimal price and delivery terms. For example, the purchase unit selects the optimal store based on the price and delivery terms. The purchase unit can also refer to the user's past purchase history to select a reliable store. For example, the purchase unit selects a reliable store based on the user's past purchase history. The delivery unit delivers the gift purchased by the purchase unit to the recipient. For example, the delivery unit selects a delivery company and delivers the gift. The delivery unit can also compare multiple delivery companies to select the optimal delivery company.For example, the optimal delivery company is selected based on the reliability of the delivery company and delivery conditions. Furthermore, the delivery unit can also use an emotion estimation function to estimate the recipient's emotion and select the delivery method most suitable for that emotion. For example, the delivery method is selected based on the recipient's emotion. In this way, the gift selection system according to the embodiment reduces the burden on the user and enables the recipient to convey their emotion and intention. For example, the user simply inputs their emotion or intention, and the optimal gift is selected and delivered to the recipient. Furthermore, the recipient can sense the sender's emotion and intention through the gift.

[0030] The emotion input unit can check the consistency of emotions or intentions by referring to the user's past message history. The emotion input unit, for example, stores the user's past message history in a database and checks the consistency of emotions and intentions. For example, the emotion input unit analyzes the emotion scores of messages sent in the past and compares them with the current input. The emotion input unit can also use text messages or chat logs to refer to the message history. For example, the emotion input unit analyzes the text messages or chat logs to check the consistency of emotions and intentions. Furthermore, the emotion input unit can use natural language processing technology to refer to the message history. For example, the message history is analyzed using natural language processing technology to check the consistency of emotions and intentions. In this way, the consistency of emotions and intentions can be checked by referring to the past message history.

[0031] The emotion input unit can express emotions or intentions input by a user with visual elements such as emojis or stamps, allowing them to be visually confirmed. For example, the emotion input unit provides an interface that allows a user to select emojis or stamps when inputting emotions or intentions. For example, it provides emojis that express gratitude and stamps that indicate an apology. The emotion input unit can also design visual elements for using emojis and stamps. For example, it designs emojis that express emotions or stamps that correspond to specific situations. Furthermore, the emotion input unit can construct a user interface for using emojis and stamps. For example, it provides buttons or menus for selecting emojis or stamps. This allows emotions or intentions to be visually confirmed with visual elements.

[0032] The emotion input unit can add a function that allows a user to share the emotion or intention input by the user with other users and receive feedback. The emotion input unit, for example, provides a platform where a user can share the emotion or intention input by the user with other users. For example, a message of gratitude can be shared and feedback can be received from other users. The emotion input unit can also set up a comment function to receive feedback. For example, other users can post comments and provide feedback on the emotion or intention. The emotion input unit can also set up a rating function to receive feedback. For example, other users can rate the emotion or intention and provide feedback. This allows emotions or intentions to be shared with other users and feedback can be received.

[0033] When analyzing a prompt, the analysis unit uses natural language processing technology to understand the context and perform more accurate sentiment analysis. For example, when the generative AI analyzes a prompt, the analysis unit uses natural language processing technology to understand the context and perform sentiment analysis. For example, it calculates a sentiment score based on the context. The analysis unit can also perform morphological analysis and grammatical analysis to understand the context. For example, it can use morphological analysis to analyze the meaning of words and grammatical analysis to analyze the structure of sentences. Furthermore, the analysis unit can also perform semantic analysis to understand the context. For example, it can use semantic analysis to analyze the meaning of sentences and infer emotions. In this way, the use of natural language processing technology enables understanding of the context and more accurate sentiment analysis.

[0034] When analyzing prompts, the analysis unit can accommodate input in different languages ​​and perform multilingual sentiment analysis. For example, the analysis unit builds a system that can accommodate input in different languages ​​when the generation AI analyzes prompts. For example, it can accommodate multiple languages ​​such as English, French, and Chinese. The analysis unit can also use multilingual natural language processing technology. For example, it performs multilingual morphological analysis and grammatical analysis. Furthermore, the analysis unit can use translation technology to analyze prompts in different languages. For example, it uses translation technology to translate the prompt and perform sentiment analysis. This makes it possible to accommodate input in different languages ​​and perform multilingual sentiment analysis.

[0035] When analyzing a prompt, the analysis unit can visualize the user's input content to make it easier to understand visually. For example, the analysis unit builds a system that visualizes the input content when the generation AI analyzes a prompt. For example, it displays emotion scores in a graph or chart. The analysis unit can also display graphs or charts for visualization. For example, it can display emotion scores in a bar graph or pie chart. Furthermore, the analysis unit can use data visualization technology for visualization. For example, it can visually display emotion scores using data visualization technology. In this way, visualizing the input content makes it easier to understand visually.

[0036] When selecting a gift, the selection unit can refer to the recipient's past receiving history and reflect their preferences or tendencies. For example, when the generation AI selects a gift, the selection unit stores the recipient's past receiving history in a database and analyzes their preferences and tendencies. For example, the selection unit may select a gift based on the type and frequency of gifts received in the past. The selection unit may also use past gift receiving records and the date and time of receipt to refer to the receiving history. For example, it may analyze preferences and tendencies based on past gift receiving records. Furthermore, the selection unit may use survey results and preference data to reflect preferences and tendencies. For example, it may analyze preferences and tendencies based on survey results. This makes it possible to select a gift that reflects the recipient's preferences and tendencies by referring to the past receiving history.

[0037] The selection unit can suggest optimal gifts according to seasons or events when selecting gifts. For example, the selection unit builds a system that suggests optimal gifts according to seasons or events when the generation AI selects gifts. For example, it suggests gifts that match events such as Christmas or Valentine's Day. The selection unit can also set definitions of seasons and types of events to take seasons and events into consideration. For example, it can set seasons such as spring, summer, autumn, and winter, and events such as birthdays and weddings. Furthermore, the selection unit can store the types and characteristics of gifts in a database to suggest gifts according to seasons and events. For example, it can create and suggest lists of gifts that are suitable for seasons and events. This makes it possible to suggest optimal gifts according to seasons and events.

[0038] When selecting a gift, the selection unit can list gift candidates selected by the user, allowing the user to make a final selection. For example, when the generation AI selects a gift, the selection unit builds a system in which multiple candidates are listed and the user can make a final selection. For example, multiple gift candidates are displayed and the user selects one. The selection unit can also set gift selection criteria and a listing method for listing the candidates. For example, the selection unit can list candidates based on the user's preferences, budget, and the recipient's profile. Furthermore, the selection unit can provide an interface to allow the user to make a final selection. For example, it can install a screen for displaying candidates and a button for selection. This makes it possible to list gift candidates selected by the user, allowing the user to make a final selection.

[0039] The selection unit can propose gifts in different price ranges when selecting a gift, providing options according to the user's budget. For example, the selection unit builds a system that proposes gifts in different price ranges when the generation AI selects a gift. For example, it lists low-priced, mid-priced, and high-priced gifts. The selection unit can also set the definition of a price range and the method of consideration in order to take price ranges into account. For example, it sets a low-priced range, a mid-priced range, and a high-priced range. Furthermore, the selection unit can also set the method of setting and considering a budget in order to provide options according to the user's budget. For example, it sets a user's budget range and proposes gifts within that range. This makes it possible to propose gifts in different price ranges and provide options according to the user's budget.

[0040] The purchasing department can compare multiple online stores when purchasing a gift and select the optimal price and delivery terms. For example, the purchasing department builds a system that allows the generation AI to compare multiple online stores when purchasing a gift. For example, it selects the optimal store based on price and delivery terms. The purchasing department can also set price comparison methods and criteria for comparing online stores. For example, it can compare based on the lowest price or discounted price. Furthermore, the purchasing department can take delivery time, shipping fees, and delivery companies into consideration when comparing delivery terms. For example, it can select the optimal delivery terms based on delivery time and shipping fees. This makes it possible to compare multiple online stores and select the optimal price and delivery terms.

[0041] When purchasing a gift, the purchasing department can refer to the user's past purchase history and select a highly reliable store. For example, the purchasing department builds a system in which the generation AI refers to the user's past purchase history when purchasing a gift. For example, it evaluates the reliability of stores that have been used in the past. The purchasing department can also use past purchase records and purchase dates and times to refer to the purchase history. For example, it selects a highly reliable store based on the past purchase records. Furthermore, the purchasing department can also take user reviews and store ratings into consideration to evaluate reliability. For example, it evaluates reliability based on user reviews and store ratings. In this way, it is possible to select a highly reliable store by referring to the user's past purchase history.

[0042] When purchasing a gift, the purchasing unit can list gift candidates selected by the user, allowing the user to make a final selection. For example, when the generation AI purchases a gift, the purchasing unit builds a system in which multiple candidates are listed and the user can make a final selection. For example, multiple gift candidates are displayed and the user selects one. The purchasing unit can also set gift selection criteria and a listing method for listing candidates. For example, the purchasing unit can list candidates based on the user's preferences, budget, and the recipient's profile. Furthermore, the purchasing unit can provide an interface to allow the user to make a final selection. For example, a screen for displaying candidates and a button for selection can be installed. This allows the system to list gift candidates selected by the user, allowing the user to make a final selection.

[0043] The purchasing unit can propose different delivery options when purchasing a gift, providing choices according to the user's preferences. For example, the purchasing unit builds a system in which a generation AI proposes different delivery options when purchasing a gift. For example, it lists options such as standard delivery, expedited delivery, and gift wrapping. The purchasing unit can also set the type of delivery option and the proposal method in order to propose delivery options. For example, it sets options such as regular delivery, express delivery, and courier delivery. Furthermore, the purchasing unit can provide an interface for inputting the user's preferences in order to provide choices according to the user's preferences. For example, it installs a button or menu for selecting a delivery option. This makes it possible to propose different delivery options and provide choices according to the user's preferences.

[0044] The delivery department can compare multiple delivery companies when delivering a gift and select the most suitable delivery company. For example, the delivery department builds a system that compares multiple delivery companies when the generation AI delivers a gift. For example, it selects the most suitable company based on the reliability of the delivery company and delivery conditions. The delivery department can also set the type of delivery company and comparison method to compare delivery companies. For example, it can set delivery companies such as Yamato Transport, Sagawa Express, and Japan Post. Furthermore, the delivery department can take delivery time, shipping fee, and delivery company reputation into consideration when selecting a delivery company. For example, it selects the most suitable delivery company based on delivery time and shipping fee. This makes it possible to compare multiple delivery companies and select the most suitable delivery company.

[0045] The delivery unit can refer to the user's past delivery history when delivering a gift and select a highly reliable delivery company. For example, the delivery unit builds a system in which the generation AI refers to the user's past delivery history when delivering a gift. For example, it evaluates the reliability of delivery companies used in the past. The delivery unit can also use past delivery records and delivery dates and times to refer to the delivery history. For example, it selects a highly reliable delivery company based on the past delivery records. Furthermore, the delivery unit can also take user reviews and delivery company ratings into consideration when evaluating reliability. For example, it evaluates reliability based on user reviews and delivery company ratings. This makes it possible to refer to the user's past delivery history and select a highly reliable delivery company.

[0046] The delivery unit can list delivery options selected by the user when delivering a gift, allowing the user to make a final selection. For example, the delivery unit can build a system in which, when the generation AI delivers a gift, it lists multiple delivery options and allows the user to make a final selection. For example, it can display multiple delivery options and allow the user to select one. The delivery unit can also set the types of delivery options and the listing method for listing the delivery options. For example, it can set options such as standard delivery, express delivery, and courier delivery. Furthermore, the delivery unit can provide an interface to allow the user to make a final selection. For example, it can install a screen for displaying delivery options and buttons for selecting them. This can list the delivery options selected by the user, allowing the user to make a final selection.

[0047] The delivery unit can propose different delivery options when delivering a gift, providing options according to the user's preferences. For example, the delivery unit builds a system that proposes different delivery options when the generation AI delivers a gift. For example, it lists options such as standard delivery, expedited delivery, and gift wrapping. The delivery unit can also set the type of delivery option and the proposal method in order to propose delivery options. For example, it sets options such as regular delivery, express delivery, and courier delivery. Furthermore, the delivery unit can provide an interface for inputting the user's preferences in order to provide options according to the user's preferences. For example, it installs buttons or menus for selecting delivery options. This makes it possible to propose different delivery options and provide options according to the user's preferences.

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

[0049] The gift selection system may further include a health monitoring unit that monitors the user's health status and selects gifts based on that status. For example, the health monitoring unit may acquire data from the user's fitness tracker or smartwatch and analyze the user's health status. The health monitoring unit may also suggest gifts based on the user's health status. For example, if the user is experiencing high stress, the health monitoring unit may suggest relaxation items, and if the user is lacking exercise, the health monitoring unit may suggest fitness-related gifts. The health monitoring unit may also provide advice to improve the user's health status. For example, the health monitoring unit may provide advice on diet and exercise based on the user's health status.

[0050] The gift selection system may further include a hobby analysis unit that analyzes a user's hobbies and interests and selects gifts based on that information. For example, the system may analyze a user's social media posts and search history to identify the user's hobbies and interests. The hobby analysis unit may also suggest gifts based on the user's hobbies and interests. For example, the system may suggest musical instruments or concert tickets to a user who likes music, or kitchenware or cooking class tickets to a user who likes cooking. The hobby analysis unit may also suggest new hobbies based on the user's hobbies and interests. For example, the system may suggest new hobbies or activities that the user may be interested in.

[0051] The gift selection system may further include a lifestyle analysis unit that analyzes the user's lifestyle and selects gifts based on the information. For example, the system may analyze the user's lifestyle habits and daily activities to identify the user's lifestyle. The lifestyle analysis unit may also suggest gifts that match the user's lifestyle. For example, the system may suggest camping equipment and hiking gear for a user who likes the outdoors, and items related to reading and watching movies for an indoor user. The lifestyle analysis unit may also provide advice to improve the user's lifestyle. For example, the system may provide advice to promote a healthy lifestyle.

[0052] The gift selection system may further include a purchase history analysis unit that analyzes a user's past purchase history and selects a gift based on that information. For example, the system may analyze items the user has purchased in the past and the frequency of purchases to identify preferences and trends. The purchase history analysis unit may also suggest gifts based on the user's past purchase history. For example, if a user prefers products from a particular brand or category, the system may suggest products from that brand or category. The purchase history analysis unit may also suggest new products based on the user's purchase history. For example, the system may suggest new products or services that the user may be interested in.

[0053] The gift selection system may further include a location information analysis unit that analyzes the user's geographical location information and selects gifts based on that information. For example, the system may analyze the user's current location and past visited locations to identify geographical trends. The location information analysis unit may also suggest gifts based on the user's location information. For example, it may suggest travel-related items or local specialties to a user who likes to travel, or items related to that area to a user who lives in a specific area. The location information analysis unit may also suggest events or activities based on the user's location information. For example, it may suggest events or activities held near the user.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The emotion input unit inputs the user's emotions and intentions. For example, the user inputs emotions and intentions in text format. The emotion input unit can also analyze voice input and estimate emotions from the tone and speed of the voice. Furthermore, the emotion input unit can refer to the user's past message history to check the consistency of emotions and intentions. Step 2: The analysis unit analyzes the information input by the emotion input unit. For example, it uses natural language processing technology to analyze the prompt and understand the context. The analysis unit can also refer to the user's past input data to track changes in emotion and intention. Furthermore, the analysis unit can use an emotion estimation function to monitor the user's emotion in real time when analyzing the prompt and reflect it in the analysis results. Step 3: The selection unit selects the optimal gift based on the information analyzed by the analysis unit. For example, it selects a gift based on the user's emotions and intentions. The selection unit can also refer to the recipient's past gift history to reflect their preferences and tendencies. Furthermore, the selection unit can suggest the optimal gift according to the season or event. Step 4: The purchasing unit purchases the gift selected by the selection unit. For example, the gift is purchased from an online store. The purchasing unit can also compare multiple online stores and select the best price and delivery terms. Furthermore, the purchasing unit can also refer to the user's past purchase history to select a reliable store. Step 5: The delivery unit delivers the gift purchased by the purchasing unit to the recipient. For example, the delivery unit selects a delivery company and delivers the gift. The delivery unit can also compare multiple delivery companies and select the most suitable one. Furthermore, the delivery unit can use an emotion estimation function to estimate the recipient's emotion and select the delivery method that best suits that emotion.

[0056] (Example 2) The gift selection system according to the embodiment of the present invention is a system that selects the most suitable gift based on the user's feelings and intentions, and automates the purchase and delivery of the gift, thereby reducing the burden on the user and enabling the user to convey their feelings and intentions to the recipient.

[0057] The gift selection system according to the embodiment includes an emotion input unit, an analysis unit, a selection unit, a purchasing unit, and a delivery unit. The emotion input unit inputs a user's emotions and intentions. For example, the user inputs emotions and intentions in text format. The emotion input unit can also analyze voice input and estimate emotions from the tone and speed of the voice. For example, the emotion is estimated by analyzing the tone and speed of the voice using voice recognition technology. The emotion input unit can also check the consistency of emotions and intentions by referring to the user's past message history. For example, the past message history is stored in a database and the consistency of emotions and intentions is checked. The analysis unit analyzes the information input by the emotion input unit. For example, the analysis unit analyzes prompts using natural language processing technology to understand the context. The analysis unit can also track changes in emotions and intentions by referring to the user's past input data. For example, the analysis unit tracks changes in emotions and intentions based on the past input data. The analysis unit can also monitor the user's emotions in real time during prompt analysis using an emotion estimation function and reflect the results of the analysis. For example, the analysis unit can analyze facial expressions and voice during input to monitor emotions in real time. The selection unit selects an optimal gift based on the information analyzed by the analysis unit. For example, the selection unit selects a gift based on the user's emotions and intentions. The selection unit can also refer to the recipient's past gift-giving history to reflect the recipient's preferences and tendencies. For example, the selection unit analyzes preferences and tendencies based on the recipient's past gift-giving history. The selection unit can also suggest optimal gifts according to seasons or events. For example, the selection unit can suggest gifts suited to events such as Christmas and Valentine's Day. The purchase unit purchases the gift selected by the selection unit. For example, the purchase unit purchases the gift from an online store. The purchase unit can also compare multiple online stores to select the optimal price and delivery terms. For example, the purchase unit selects the optimal store based on the price and delivery terms. The purchase unit can also refer to the user's past purchase history to select a reliable store. For example, the purchase unit selects a reliable store based on the user's past purchase history. The delivery unit delivers the gift purchased by the purchase unit to the recipient. For example, the delivery unit selects a delivery company and delivers the gift. The delivery unit can also compare multiple delivery companies to select the optimal delivery company.For example, the optimal delivery company is selected based on the reliability of the delivery company and delivery conditions. Furthermore, the delivery unit can also use an emotion estimation function to estimate the recipient's emotion and select the delivery method most suitable for that emotion. For example, the delivery method is selected based on the recipient's emotion. In this way, the gift selection system according to the embodiment reduces the burden on the user and enables the recipient to convey their emotion and intention. For example, the user simply inputs their emotion or intention, and the optimal gift is selected and delivered to the recipient. Furthermore, the recipient can sense the sender's emotion and intention through the gift.

[0058] The emotion input unit can analyze the user's voice input and infer emotions from the tone or speed of the voice. For example, when the user inputs emotions or intentions by voice, the emotion input unit analyzes the tone and speed of the voice using voice recognition technology. For example, a high and fast voice indicates excitement or joy, while a low and slow voice indicates sadness or calmness. The emotion input unit can also use a microphone to analyze the voice input. For example, the microphone can be used to record and analyze the voice. Furthermore, the emotion input unit can also use a machine learning algorithm to analyze the voice input. For example, the machine learning algorithm can be used to analyze the voice data and infer emotions. This enables more accurate emotion analysis by analyzing the voice input.

[0059] The emotion input unit can check the consistency of emotions or intentions by referring to the user's past message history. The emotion input unit, for example, stores the user's past message history in a database and checks the consistency of emotions and intentions. For example, the emotion input unit analyzes the emotion scores of messages sent in the past and compares them with the current input. The emotion input unit can also use text messages or chat logs to refer to the message history. For example, the emotion input unit analyzes the text messages or chat logs to check the consistency of emotions and intentions. Furthermore, the emotion input unit can use natural language processing technology to refer to the message history. For example, the message history is analyzed using natural language processing technology to check the consistency of emotions and intentions. In this way, the consistency of emotions and intentions can be checked by referring to the past message history.

[0060] The emotion input unit can capture facial expressions when a user makes an input using a camera and complement the emotion. For example, when a user inputs an emotion or intention, the emotion input unit captures the facial expression using a camera and analyzes it using an emotion estimation function. For example, facial expressions such as smiling or furrowed brows are detected and an emotion score is calculated. The emotion input unit can also use facial recognition technology to capture facial expressions. For example, facial expressions are detected using facial recognition technology and emotions are analyzed. Furthermore, the emotion input unit can also use a webcam or a smartphone camera to capture facial expressions. For example, facial expressions are captured and analyzed using a webcam or a smartphone camera. This makes it possible to complement emotions by capturing facial expressions.

[0061] The emotion input unit can express emotions or intentions input by a user with visual elements such as emojis or stamps, allowing them to be visually confirmed. For example, the emotion input unit provides an interface that allows a user to select emojis or stamps when inputting emotions or intentions. For example, it provides emojis that express gratitude and stamps that indicate an apology. The emotion input unit can also design visual elements for using emojis and stamps. For example, it designs emojis that express emotions or stamps that correspond to specific situations. Furthermore, the emotion input unit can construct a user interface for using emojis and stamps. For example, it provides buttons or menus for selecting emojis or stamps. This allows emotions or intentions to be visually confirmed with visual elements.

[0062] The emotion input unit can add a function that allows a user to share the emotion or intention input by the user with other users and receive feedback. The emotion input unit, for example, provides a platform where a user can share the emotion or intention input by the user with other users. For example, a message of gratitude can be shared and feedback can be received from other users. The emotion input unit can also set up a comment function to receive feedback. For example, other users can post comments and provide feedback on the emotion or intention. The emotion input unit can also set up a rating function to receive feedback. For example, other users can rate the emotion or intention and provide feedback. This allows emotions or intentions to be shared with other users and feedback can be received.

[0063] The emotion input unit uses the emotion estimation function to display the emotion of the user as they input information in real time and make suggestions to adjust the input content. The emotion input unit, for example, builds a system that uses the emotion estimation function to analyze and display emotions in real time when a user inputs their emotions or intentions. For example, an emotion score is displayed while the user is inputting. The emotion input unit can also install a suggestion function to adjust the input content. For example, it can suggest corrections to the input content or present alternatives. Furthermore, the emotion input unit can also monitor the user's emotions in real time using the emotion estimation function. For example, it can analyze facial expressions and voices while the user is inputting information and monitor emotions in real time. This makes it possible to display emotions in real time and make suggestions to adjust the input content.

[0064] When analyzing a prompt, the analysis unit uses natural language processing technology to understand the context and perform more accurate sentiment analysis. For example, when the generative AI analyzes a prompt, the analysis unit uses natural language processing technology to understand the context and perform sentiment analysis. For example, it calculates a sentiment score based on the context. The analysis unit can also perform morphological analysis and grammatical analysis to understand the context. For example, it can use morphological analysis to analyze the meaning of words and grammatical analysis to analyze the structure of sentences. Furthermore, the analysis unit can also perform semantic analysis to understand the context. For example, it can use semantic analysis to analyze the meaning of sentences and infer emotions. In this way, the use of natural language processing technology enables understanding of the context and more accurate sentiment analysis.

[0065] The analysis unit can use the emotion estimation function to monitor the user's emotions in real time when analyzing prompts and reflect them in the analysis results. For example, the analysis unit uses the emotion estimation function to build a system that monitors the user's emotions in real time when the generation AI analyzes prompts. For example, it analyzes facial expressions and voice while inputting. The analysis unit can also use the emotion estimation function to display the user's emotions in real time. For example, it can display an emotion score while inputting on the screen. Furthermore, the analysis unit can use the emotion estimation function to feed back the analysis results to the user. For example, it can notify the user of the analysis results and provide feedback to the user. In this way, by using the emotion estimation function, the user's emotions can be monitored in real time when analyzing prompts and reflected in the analysis results.

[0066] When analyzing prompts, the analysis unit can accommodate input in different languages ​​and perform multilingual sentiment analysis. For example, the analysis unit builds a system that can accommodate input in different languages ​​when the generation AI analyzes prompts. For example, it can accommodate multiple languages ​​such as English, French, and Chinese. The analysis unit can also use multilingual natural language processing technology. For example, it performs multilingual morphological analysis and grammatical analysis. Furthermore, the analysis unit can use translation technology to analyze prompts in different languages. For example, it uses translation technology to translate the prompt and perform sentiment analysis. This makes it possible to accommodate input in different languages ​​and perform multilingual sentiment analysis.

[0067] When analyzing a prompt, the analysis unit can visualize the user's input content to make it easier to understand visually. For example, the analysis unit builds a system that visualizes the input content when the generation AI analyzes a prompt. For example, it displays emotion scores in a graph or chart. The analysis unit can also display graphs or charts for visualization. For example, it can display emotion scores in a bar graph or pie chart. Furthermore, the analysis unit can use data visualization technology for visualization. For example, it can visually display emotion scores using data visualization technology. In this way, visualizing the input content makes it easier to understand visually.

[0068] The analysis unit can use the emotion estimation function to display the user's emotions in real time when analyzing prompts and feed back the analysis results to the user. For example, the analysis unit can build a system that uses the emotion estimation function to display the user's emotions in real time when the generation AI analyzes prompts. For example, it can display the emotion score on the screen as the input is taking place. The analysis unit can also set up a notification function to feed back the analysis results to the user. For example, it can notify the user of the analysis results and provide feedback to the user. Furthermore, the analysis unit can set up a comment function or rating function for feedback. For example, the user can comment on or rate the analysis results. This makes it possible to display emotions in real time and feed back the analysis results to the user.

[0069] When selecting a gift, the selection unit can refer to the recipient's past receiving history and reflect their preferences or tendencies. For example, when the generation AI selects a gift, the selection unit stores the recipient's past receiving history in a database and analyzes their preferences and tendencies. For example, the selection unit may select a gift based on the type and frequency of gifts received in the past. The selection unit may also use past gift receiving records and the date and time of receipt to refer to the receiving history. For example, it may analyze preferences and tendencies based on past gift receiving records. Furthermore, the selection unit may use survey results and preference data to reflect preferences and tendencies. For example, it may analyze preferences and tendencies based on survey results. This makes it possible to select a gift that reflects the recipient's preferences and tendencies by referring to the past receiving history.

[0070] The selection unit can suggest optimal gifts according to seasons or events when selecting gifts. For example, the selection unit builds a system that suggests optimal gifts according to seasons or events when the generation AI selects gifts. For example, it suggests gifts that match events such as Christmas or Valentine's Day. The selection unit can also set definitions of seasons and types of events to take seasons and events into consideration. For example, it can set seasons such as spring, summer, autumn, and winter, and events such as birthdays and weddings. Furthermore, the selection unit can store the types and characteristics of gifts in a database to suggest gifts according to seasons and events. For example, it can create and suggest lists of gifts that are suitable for seasons and events. This makes it possible to suggest optimal gifts according to seasons and events.

[0071] The selection unit can use the emotion estimation function to estimate the recipient's emotions and select a gift that best suits those emotions. For example, the selection unit builds a system that uses the emotion estimation function to estimate the recipient's emotions when the generation AI selects a gift. For example, the selection unit analyzes the recipient's social media posts and messages. The selection unit can also use the emotion estimation function to monitor the recipient's emotions in real time. For example, the selection unit analyzes the recipient's facial expressions and voice to monitor emotions in real time. Furthermore, the selection unit can use the emotion estimation function to calculate an emotion score in order to select a gift that best suits the recipient's emotions. For example, the selection unit selects a gift based on the emotion score. In this way, the emotion estimation function can be used to select a gift that best suits the recipient's emotions.

[0072] When selecting a gift, the selection unit can list gift candidates selected by the user, allowing the user to make a final selection. For example, when the generation AI selects a gift, the selection unit builds a system in which multiple candidates are listed and the user can make a final selection. For example, multiple gift candidates are displayed and the user selects one. The selection unit can also set gift selection criteria and a listing method for listing the candidates. For example, the selection unit can list candidates based on the user's preferences, budget, and the recipient's profile. Furthermore, the selection unit can provide an interface to allow the user to make a final selection. For example, it can install a screen for displaying candidates and a button for selection. This makes it possible to list gift candidates selected by the user, allowing the user to make a final selection.

[0073] The selection unit can propose gifts in different price ranges when selecting a gift, providing options according to the user's budget. For example, the selection unit builds a system that proposes gifts in different price ranges when the generation AI selects a gift. For example, it lists low-priced, mid-priced, and high-priced gifts. The selection unit can also set the definition of a price range and the method of consideration in order to take price ranges into account. For example, it sets a low-priced range, a mid-priced range, and a high-priced range. Furthermore, the selection unit can also set the method of setting and considering a budget in order to provide options according to the user's budget. For example, it sets a user's budget range and proposes gifts within that range. This makes it possible to propose gifts in different price ranges and provide options according to the user's budget.

[0074] The selection unit can use the emotion estimation function to monitor the recipient's emotions in real time and improve the accuracy of gift selection. For example, the selection unit uses the emotion estimation function to build a system that monitors the recipient's emotions in real time when the generation AI selects a gift. For example, it analyzes the recipient's social media posts and messages. The selection unit can also use the emotion estimation function to display the recipient's emotions in real time. For example, it can display the recipient's emotion score on a screen. Furthermore, the selection unit can use the emotion estimation function to select a gift based on the recipient's emotions. For example, it selects a gift based on the emotion score. In this way, by using the emotion estimation function, the recipient's emotions can be monitored in real time and the accuracy of gift selection can be improved.

[0075] The purchasing department can compare multiple online stores when purchasing a gift and select the optimal price and delivery terms. For example, the purchasing department builds a system that allows the generation AI to compare multiple online stores when purchasing a gift. For example, it selects the optimal store based on price and delivery terms. The purchasing department can also set price comparison methods and criteria for comparing online stores. For example, it can compare based on the lowest price or discounted price. Furthermore, the purchasing department can take delivery time, shipping fees, and delivery companies into consideration when comparing delivery terms. For example, it can select the optimal delivery terms based on delivery time and shipping fees. This makes it possible to compare multiple online stores and select the optimal price and delivery terms.

[0076] When purchasing a gift, the purchasing department can refer to the user's past purchase history and select a highly reliable store. For example, the purchasing department builds a system in which the generation AI refers to the user's past purchase history when purchasing a gift. For example, it evaluates the reliability of stores that have been used in the past. The purchasing department can also use past purchase records and purchase dates and times to refer to the purchase history. For example, it selects a highly reliable store based on the past purchase records. Furthermore, the purchasing department can also take user reviews and store ratings into consideration to evaluate reliability. For example, it evaluates reliability based on user reviews and store ratings. In this way, it is possible to select a highly reliable store by referring to the user's past purchase history.

[0077] When purchasing a gift, the purchasing unit can list gift candidates selected by the user, allowing the user to make a final selection. For example, when the generation AI purchases a gift, the purchasing unit builds a system in which multiple candidates are listed and the user can make a final selection. For example, multiple gift candidates are displayed and the user selects one. The purchasing unit can also set gift selection criteria and a listing method for listing candidates. For example, the purchasing unit can list candidates based on the user's preferences, budget, and the recipient's profile. Furthermore, the purchasing unit can provide an interface to allow the user to make a final selection. For example, a screen for displaying candidates and a button for selection can be installed. This allows the system to list gift candidates selected by the user, allowing the user to make a final selection.

[0078] The purchasing unit can propose different delivery options when purchasing a gift, providing choices according to the user's preferences. For example, the purchasing unit builds a system in which a generation AI proposes different delivery options when purchasing a gift. For example, it lists options such as standard delivery, expedited delivery, and gift wrapping. The purchasing unit can also set the type of delivery option and the proposal method in order to propose delivery options. For example, it sets options such as regular delivery, express delivery, and courier delivery. Furthermore, the purchasing unit can provide an interface for inputting the user's preferences in order to provide choices according to the user's preferences. For example, it installs a button or menu for selecting a delivery option. This makes it possible to propose different delivery options and provide choices according to the user's preferences.

[0079] The purchasing department can use the emotion estimation function to monitor the recipient's emotions in real time and optimize the timing of delivery. For example, when the generation AI purchases a gift, the purchasing department builds a system that uses the emotion estimation function to monitor the recipient's emotions in real time. For example, by analyzing the recipient's social media posts and messages. The purchasing department can also use the emotion estimation function to display the recipient's emotions in real time. For example, by displaying the recipient's emotion score on a screen. Furthermore, the purchasing department can use the emotion estimation function to optimize the timing of delivery based on the recipient's emotions. For example, by adjusting the timing of delivery based on the emotion score. In this way, the emotion estimation function can monitor the recipient's emotions in real time and optimize the timing of delivery.

[0080] The delivery department can compare multiple delivery companies when delivering a gift and select the most suitable delivery company. For example, the delivery department builds a system that compares multiple delivery companies when the generation AI delivers a gift. For example, it selects the most suitable company based on the reliability of the delivery company and delivery conditions. The delivery department can also set the type of delivery company and comparison method to compare delivery companies. For example, it can set delivery companies such as Yamato Transport, Sagawa Express, and Japan Post. Furthermore, the delivery department can take delivery time, shipping fee, and delivery company reputation into consideration when selecting a delivery company. For example, it selects the most suitable delivery company based on delivery time and shipping fee. This makes it possible to compare multiple delivery companies and select the most suitable delivery company.

[0081] The delivery unit can refer to the user's past delivery history when delivering a gift and select a highly reliable delivery company. For example, the delivery unit builds a system in which the generation AI refers to the user's past delivery history when delivering a gift. For example, it evaluates the reliability of delivery companies used in the past. The delivery unit can also use past delivery records and delivery dates and times to refer to the delivery history. For example, it selects a highly reliable delivery company based on the past delivery records. Furthermore, the delivery unit can also take user reviews and delivery company ratings into consideration when evaluating reliability. For example, it evaluates reliability based on user reviews and delivery company ratings. This makes it possible to refer to the user's past delivery history and select a highly reliable delivery company.

[0082] The delivery unit can use the emotion estimation function to estimate the recipient's emotions and select the delivery method most suitable for those emotions. For example, the delivery unit builds a system that uses the emotion estimation function to estimate the recipient's emotions when the generation AI delivers a gift. For example, it analyzes the recipient's social media posts and messages. The delivery unit can also use the emotion estimation function to monitor the recipient's emotions in real time. For example, it analyzes the recipient's facial expressions and voice to monitor emotions in real time. Furthermore, the delivery unit can use the emotion estimation function to calculate an emotion score in order to select the delivery method most suitable for the recipient's emotions. For example, it selects a delivery method based on the emotion score. In this way, the emotion estimation function can be used to select the delivery method most suitable for the recipient's emotions.

[0083] The delivery unit can list delivery options selected by the user when delivering a gift, allowing the user to make a final selection. For example, the delivery unit can build a system in which, when the generation AI delivers a gift, it lists multiple delivery options and allows the user to make a final selection. For example, it can display multiple delivery options and allow the user to select one. The delivery unit can also set the types of delivery options and the listing method for listing the delivery options. For example, it can set options such as standard delivery, express delivery, and courier delivery. Furthermore, the delivery unit can provide an interface to allow the user to make a final selection. For example, it can install a screen for displaying delivery options and buttons for selecting them. This can list the delivery options selected by the user, allowing the user to make a final selection.

[0084] The delivery unit can propose different delivery options when delivering a gift, providing options according to the user's preferences. For example, the delivery unit builds a system that proposes different delivery options when the generation AI delivers a gift. For example, it lists options such as standard delivery, expedited delivery, and gift wrapping. The delivery unit can also set the type of delivery option and the proposal method in order to propose delivery options. For example, it sets options such as regular delivery, express delivery, and courier delivery. Furthermore, the delivery unit can provide an interface for inputting the user's preferences in order to provide options according to the user's preferences. For example, it installs buttons or menus for selecting delivery options. This makes it possible to propose different delivery options and provide options according to the user's preferences.

[0085] The delivery unit can use the emotion estimation function to monitor the recipient's emotions in real time and optimize the timing of delivery. For example, when the generation AI delivers a gift, the delivery unit uses the emotion estimation function to build a system that monitors the recipient's emotions in real time. For example, it analyzes the recipient's social media posts and messages. The delivery unit can also use the emotion estimation function to display the recipient's emotions in real time. For example, it can display the recipient's emotion score on a screen. Furthermore, the delivery unit can use the emotion estimation function to optimize the timing of delivery based on the recipient's emotions. For example, it can adjust the timing of delivery based on the emotion score. In this way, the emotion estimation function can be used to monitor the recipient's emotions in real time and optimize the timing of delivery.

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

[0087] The gift selection system may further include a health monitoring unit that monitors the user's health status and selects gifts based on that status. For example, the health monitoring unit may acquire data from the user's fitness tracker or smartwatch and analyze the user's health status. The health monitoring unit may also suggest gifts based on the user's health status. For example, if the user is experiencing high stress, the health monitoring unit may suggest relaxation items, and if the user is lacking exercise, the health monitoring unit may suggest fitness-related gifts. The health monitoring unit may also provide advice to improve the user's health status. For example, the health monitoring unit may provide advice on diet and exercise based on the user's health status.

[0088] The gift selection system may further include a hobby analysis unit that analyzes a user's hobbies and interests and selects gifts based on that information. For example, the system may analyze a user's social media posts and search history to identify the user's hobbies and interests. The hobby analysis unit may also suggest gifts based on the user's hobbies and interests. For example, the system may suggest musical instruments or concert tickets to a user who likes music, or kitchenware or cooking class tickets to a user who likes cooking. The hobby analysis unit may also suggest new hobbies based on the user's hobbies and interests. For example, the system may suggest new hobbies or activities that the user may be interested in.

[0089] The gift selection system may further include a lifestyle analysis unit that analyzes the user's lifestyle and selects gifts based on the information. For example, the system may analyze the user's lifestyle habits and daily activities to identify the user's lifestyle. The lifestyle analysis unit may also suggest gifts that match the user's lifestyle. For example, the system may suggest camping equipment and hiking gear for a user who likes the outdoors, and items related to reading and watching movies for an indoor user. The lifestyle analysis unit may also provide advice to improve the user's lifestyle. For example, the system may provide advice to promote a healthy lifestyle.

[0090] The gift selection system may further include a purchase history analysis unit that analyzes a user's past purchase history and selects a gift based on that information. For example, the system may analyze items the user has purchased in the past and the frequency of purchases to identify preferences and trends. The purchase history analysis unit may also suggest gifts based on the user's past purchase history. For example, if a user prefers products from a particular brand or category, the system may suggest products from that brand or category. The purchase history analysis unit may also suggest new products based on the user's purchase history. For example, the system may suggest new products or services that the user may be interested in.

[0091] The gift selection system may further include a location information analysis unit that analyzes the user's geographical location information and selects gifts based on that information. For example, the system may analyze the user's current location and past visited locations to identify geographical trends. The location information analysis unit may also suggest gifts based on the user's location information. For example, it may suggest travel-related items or local specialties to a user who likes to travel, or items related to that area to a user who lives in a specific area. The location information analysis unit may also suggest events or activities based on the user's location information. For example, it may suggest events or activities held near the user.

[0092] The gift selection system can further estimate the user's emotions and customize the gift wrapping and message based on the user's emotions. For example, if the user is feeling happy, the system can use brightly colored wrapping and include a positive message. The emotion estimation function can also be used to select a message or design that expresses gratitude if the user is feeling grateful. The emotion estimation function can also be used to select a comforting message or a calm design if the user is feeling sad. This allows the gift wrapping and message to be customized according to the user's emotions.

[0093] The gift selection system can further estimate the user's emotions and adjust the timing of gift delivery based on the emotions. For example, if the user is in a hurry, a quick delivery option can be selected. Alternatively, the emotion estimation function can be used to select a standard delivery option if the user is relaxed. Furthermore, if the user is preparing for a specific event, the emotion estimation function can be used to select a delivery timing that is appropriate for the event. This allows the system to provide optimal delivery timing based on the user's emotions.

[0094] The gift selection system can further estimate the user's emotions and adjust the gift selection criteria based on the emotions. For example, if the user is excited, a unique and surprising gift can be suggested. The emotion estimation function can also be used to suggest practical and simple gifts if the user is calm. Furthermore, the emotion estimation function can also be used to suggest gifts that express gratitude if the user is grateful. This allows the system to select the optimal gift according to the user's emotions.

[0095] The gift selection system can further estimate the user's emotions and adjust the price range of gifts based on those emotions. For example, if the user is feeling happy, a gift in a higher price range is suggested. Also, using the emotion estimation function, if the user is conscious of saving money, a gift in a lower price range can be suggested. Furthermore, if the user is preparing for a special event, the emotion estimation function can be used to suggest a gift in a price range that matches the event. This makes it possible to provide gifts in the optimal price range according to the user's emotions.

[0096] The gift selection system can further estimate the user's emotions and customize the gift selection process based on the emotions. For example, if the user is feeling stressed, a simple and quick selection process can be provided. Alternatively, the emotion estimation function can be used to provide a detailed selection process if the user is having fun. Furthermore, if the user is preparing for a specific event, the emotion estimation function can be used to provide a selection process tailored to the event. This makes it possible to provide an optimal selection process according to the user's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The emotion input unit inputs the user's emotions and intentions. For example, the user inputs emotions and intentions in text format. The emotion input unit can also analyze voice input and estimate emotions from the tone and speed of the voice. Furthermore, the emotion input unit can refer to the user's past message history to check the consistency of emotions and intentions. Step 2: The analysis unit analyzes the information input by the emotion input unit. For example, it uses natural language processing technology to analyze the prompt and understand the context. The analysis unit can also refer to the user's past input data to track changes in emotion and intention. Furthermore, the analysis unit can use an emotion estimation function to monitor the user's emotion in real time when analyzing the prompt and reflect it in the analysis results. Step 3: The selection unit selects the optimal gift based on the information analyzed by the analysis unit. For example, it selects a gift based on the user's emotions and intentions. The selection unit can also refer to the recipient's past gift history to reflect their preferences and tendencies. Furthermore, the selection unit can suggest the optimal gift according to the season or event. Step 4: The purchasing unit purchases the gift selected by the selection unit. For example, the gift is purchased from an online store. The purchasing unit can also compare multiple online stores and select the best price and delivery terms. Furthermore, the purchasing unit can also refer to the user's past purchase history to select a reliable store. Step 5: The delivery unit delivers the gift purchased by the purchasing unit to the recipient. For example, the delivery unit selects a delivery company and delivers the gift. The delivery unit can also compare multiple delivery companies and select the most suitable one. Furthermore, the delivery unit can use an emotion estimation function to estimate the recipient's emotion and select the delivery method that best suits that emotion.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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. [Explanation of symbols]

[0166] 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. an emotion input unit for inputting a user's emotion or intention; an analysis unit that analyzes the information input by the emotion input unit; a selection unit that selects an optimal gift based on the information analyzed by the analysis unit; a purchasing unit for purchasing the gift selected by the selection unit; a delivery unit that delivers the gift purchased by the purchase unit to the recipient. A system characterized by:

2. The emotion input unit Analyzing the user's voice input and inferring the emotion from the tone or rate of voice.

2. The system of claim 1.

3. The analysis unit When parsing prompts, natural language processing technology is used to understand the context and perform more accurate sentiment analysis.

2. The system of claim 1.

4. The selection unit When selecting the gift, the recipient's past receiving history is referenced and the recipient's preferences or tendencies are reflected.

2. The system of claim 1.

5. The purchasing department When purchasing the gift, compare prices and delivery terms from multiple online stores to find the best deal.

2. The system of claim 1.

6. The delivery unit When delivering the gift, a plurality of delivery companies are compared and the most suitable delivery company is selected.

2. The system of claim 1.

7. The emotion input unit The facial expression of the user is captured by a camera when the user makes an input, and the emotion is complemented.

2. The system of claim 1.

8. The analysis unit Using an emotion estimation function, the user's emotions are monitored in real time during prompt analysis and reflected in the analysis results.

2. The system of claim 1.

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