system

Abstract

The system according to this embodiment aims to provide appropriate advice in real time based on the user's preferences and purchase history. [Solution] The system according to the embodiment comprises a collection unit, an advice unit, a real-time unit, a judgment unit, and a recommendation unit. The collection unit collects the user's preferences and purchase history. The advice unit provides advice based on the data collected by the collection unit. The real-time unit provides the advice provided by the advice unit in real time. The judgment unit determines that the advice provided by the advice unit is "unsuitable". The recommendation unit makes recommendations that "encourage" the user to proceed with the advice provided by the advice unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to provide appropriate advice in real time based on the user's preferences and purchase history, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate advice in real time based on the user's preferences and purchase history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an advice unit, a real-time unit, a judgment unit, and a recommendation unit. The data collection unit collects the user's preferences and purchase history. The advice unit provides advice based on the data collected by the data collection unit. The real-time unit provides the advice provided by the advice unit in real time. The judgment unit determines that the advice provided by the advice unit is "unsuitable". The recommendation unit makes recommendations that "encourage" the user to proceed with the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate advice in real time based on the user's preferences and purchase history. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smartphone assistant system according to an embodiment of the present invention is a tool for making the shopping experience more enjoyable and intelligent. This smartphone assistant system collects user preferences and past purchase history data and provides appropriate and frank advice in real time. For example, it provides advice such as "That doesn't suit you" or "Do you really need that?" to prevent wasteful spending. It also provides encouraging advice such as "I think this is a good choice" to those who are undecided. This smartphone assistant system primarily targets people who have risks such as "wasteful spending" or "indecisiveness" when shopping, and people who want to enjoy shopping efficiently and with high satisfaction. By providing appropriate and frank advice in real time based on user preferences and purchase history data, it improves the shopping experience. For example, when purchasing fashion items, the AI ​​judges whether the item selected by the user suits them and provides appropriate advice. Also, if the user is unsure which of several items to choose, the AI ​​suggests the optimal item. As a result, users can enjoy shopping efficiently and obtain a highly satisfying shopping experience. This mechanism allows users to prevent wasteful spending and enjoy highly satisfying shopping. Furthermore, because the AI ​​provides advice in real time, users can proceed with their shopping efficiently. For example, when shopping online, AI can provide appropriate advice, allowing users to quickly select the best items. Furthermore, this AI generates personalized advice based on the user's preferences and purchase history data, providing advice tailored to each individual user. This enables users to choose the items that are best suited to them, resulting in a more satisfying shopping experience. In this way, smartphone assistant systems can improve the user's shopping experience, prevent wasteful spending, and support more satisfying shopping.

[0029] The smartphone assistant system according to this embodiment comprises a data collection unit, an advice unit, a real-time unit, a judgment unit, and a recommendation unit. The data collection unit collects the user's preferences and purchase history. For example, the data collection unit collects data on items the user has purchased in the past. The data collection unit can also collect data on brands and styles that the user prefers. Furthermore, the data collection unit can analyze the user's purchase history and identify the user's preferences. For example, the data collection unit identifies categories of items that the user frequently purchases and collects data on those categories. The advice unit provides advice based on the data collected by the data collection unit. For example, the advice unit determines whether the item the user has chosen suits them and provides appropriate advice. The advice unit can also suggest the best item if the user is unsure which of several items to choose. Furthermore, the advice unit can provide personalized advice based on the user's preferences and purchase history. For example, the advice unit suggests items in a similar style to items the user has purchased in the past. The real-time unit provides the advice provided by the advice unit in real time. For example, the real-time unit provides advice in real time when the user is online shopping. Furthermore, the real-time unit can provide real-time advice while the user is shopping in a store. It can also provide real-time advice while the user is selecting items. For example, the real-time unit can determine in real-time whether the item the user has chosen suits them and provide advice. The judgment unit, based on the advice provided by the advice unit, can determine that the item "does not suit" the user. For example, the judgment unit will determine that the item the user has chosen does not suit them if it does not match their style. It can also determine that the item the user has chosen does not suit them if it does not match their preferences. Furthermore, the judgment unit can determine that the item the user has chosen does not suit them if it does not match their body type. For example, the judgment unit can determine whether the selected item suits the user's body type based on the user's body type data.The recommendation unit provides "encouraging" advice from the advice provided by the advice unit. For example, if the item selected by the user suits the user, the recommendation unit will provide encouraging advice such as "I think this is a good choice." The recommendation unit can also provide encouraging advice such as "I think this is a good choice" if the item selected by the user matches the user's preferences. Furthermore, the recommendation unit can also provide encouraging advice such as "I think this is a good choice" if the item selected by the user suits the user's body type. For example, the recommendation unit will determine whether the selected item suits the user's body type based on the user's body type data and provide encouraging advice such as "I think this is a good choice." As a result, the smartphone assistant system according to this embodiment can provide appropriate advice in real time based on the user's preferences and purchase history, prevent wasteful spending, and support highly satisfying shopping.

[0030] The data collection unit collects user preferences and purchase history. For example, it collects data on items that users have purchased in the past. Specifically, it obtains purchase history data from online shopping sites and applications and analyzes what kinds of products users frequently purchase. The data collection unit can also collect data on brands and styles that users prefer. For example, if a user frequently purchases products from a particular brand, it collects information about that brand to identify the user's preferences. Furthermore, the data collection unit can analyze a user's purchase history to identify their preferences. For example, it can identify the categories of items that a user frequently purchases and collect data on those categories. This allows the data collection unit to gain a detailed understanding of user preferences and purchasing trends, and to collect foundational data to provide optimal advice to individual users. In addition, the data collection unit can also collect other relevant data, such as users' social media activity and search history. This allows for a broader understanding of user interests and preferences, enabling the provision of more accurate advice. For example, if a user frequently visits a particular fashion blog, the content of that blog can be analyzed to identify the user's preferences. This allows the data collection unit to gather information from diverse user data sources and create a comprehensive user profile.

[0031] The advice department provides advice based on data collected by the data collection department. For example, the advice department can determine whether an item selected by a user suits them and provide appropriate advice. Specifically, it uses AI to analyze the user's past purchase history and preference data to evaluate whether the selected item suits the user's style. The advice department can also suggest the best item if the user is unsure which of several items to choose. For example, the AI ​​considers the user's past selection patterns and current trends to recommend the most suitable item. Furthermore, the advice department can provide personalized advice based on the user's preferences and purchase history. For example, it can suggest items with a similar style to items the user has purchased in the past. This allows the advice department to provide specific advice tailored to the user's individual needs and increase user satisfaction. In addition, the advice department can collect user feedback and continuously improve the accuracy of its advice. For example, it can analyze how users reacted to the advice provided and reflect this in future advice. This allows the advice department to flexibly respond to changes in user preferences and needs and always provide the best advice.

[0032] The Real-Time Unit provides advice in real time, based on the advice provided by the Advice Unit. For example, the Real-Time Unit provides advice in real time when a user is online shopping. Specifically, when a user is browsing a product page, it displays advice in a pop-up window based on their past purchase history and preferences. The Real-Time Unit can also provide advice in real time when a user is shopping in a store. For example, it can obtain location information within the store via a smartphone app and notify the user of recommended products nearby. Furthermore, the Real-Time Unit can provide advice in real time while a user is selecting products. For example, when a user is trying on clothes in a fitting room, it can use a smart mirror to determine in real time whether the selected item suits them and provide advice. In this way, the Real-Time Unit can provide immediately useful information while the user is shopping, supporting them in making better choices. In addition, the Real-Time Unit can collect user behavior data in real time to improve the accuracy of advice. For example, it can analyze in real time what kind of products a user is interested in and provide advice based on that information. In this way, the Real-Time Unit can provide advice that is immediately tailored to the user's needs and improve the shopping experience.

[0033] The judgment unit determines that an item is "unsuitable" based on the advice provided by the advice unit. For example, the judgment unit determines that an item selected by the user is "unsuitable" if it does not suit the user's style. Specifically, it uses AI to analyze the user's past purchase history and preference data to determine if the selected item does not suit the user's style. The judgment unit can also determine that an item is "unsuitable" if it does not suit the user's preferences. For example, if the user selects an item in a style or color they have avoided in the past, the judgment unit will determine that the item does not suit their preferences. Furthermore, the judgment unit can also determine that an item is "unsuitable" if it does not suit the user's body type. For example, it uses the user's body type data to determine if the selected item suits their body type. This allows the judgment unit to prevent users from making inappropriate choices and support them in making better choices. In addition, the judgment unit can collect user feedback and continuously improve the accuracy of its judgments. For example, it can analyze how users reacted to the judgments provided and reflect this in future judgments. This allows the judgment unit to flexibly respond to changes in user preferences and needs and always provide the best possible judgment.

[0034] The recommendation section provides "encouraging" advice from the advice section. For example, if the item chosen by the user suits the user, the recommendation section will offer encouraging advice such as, "I think this is a good choice." Specifically, it uses AI to analyze the user's past purchase history and preference data to determine if the chosen item suits the user's style. The recommendation section can also offer encouraging advice such as, "I think this is a good choice," if the item chosen by the user matches the user's preferences. For example, if the user chooses an item in a style or color they have previously purchased, the recommendation section will determine if that item suits their preferences. Furthermore, the recommendation section can also offer encouraging advice such as, "I think this is a good choice," if the item chosen by the user suits the user's body type. For example, based on the user's body type data, it will determine if the chosen item suits their body type and offer encouraging advice such as, "I think this is a good choice." In this way, the recommendation section can support users in making choices with confidence and provide a highly satisfying shopping experience. In addition, the recommendation section can collect user feedback and continuously improve the accuracy of its advice. For example, it can analyze how users reacted to the advice provided and reflect this in future advice. This allows the recommendation system to flexibly adapt to changes in user preferences and needs, and always provide the best advice.

[0035] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, the data collection unit can identify product categories that a user frequently purchases and select a data collection method specific to those categories. The data collection unit can also analyze a user's preferences for specific brands or stores from their purchase history and collect data based on that analysis. Furthermore, the data collection unit can analyze a user's purchase history by time of day and day of the week to select the optimal data collection timing. For example, if a user tends to make purchases during a particular time of day, the data collection unit will collect data during that time. This allows the optimal data collection method to be selected by analyzing past purchase history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter purchase history based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit will prioritize collecting purchase history related to that hobby. Furthermore, if a user moves, the data collection unit can collect purchase history of product categories suitable for their new living environment. Additionally, if a user plans to attend a specific event, the data collection unit can collect purchase history related to that event. For example, the data collection unit filters the most relevant purchase history based on the user's lifestyle data. This allows for the collection of more relevant data by filtering the data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting purchase history. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of data on popular products in that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of purchase history at their travel destination. Additionally, if the user frequently visits a particular store, the data collection unit can prioritize the collection of purchase history at that store. For example, the data collection unit determines the optimal data priority based on the user's geographical location. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI determine the data priority.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data when collecting purchase history. For example, the data collection unit can collect data on products that the user has "liked" or commented on on social media. It can also collect data on products that have been featured by influencers that the user follows. Furthermore, the data collection unit can collect data on products that are trending in social media groups that the user participates in. For example, the data collection unit can select the optimal data collection method based on the user's social media activity data. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0039] The advice unit can adjust the level of detail in its advice based on the importance of the product when providing advice. For example, it can provide detailed advice for high-priced products. It can also provide concise advice for products used on a daily basis. Furthermore, it can provide detailed advice for products that the user is particularly interested in. For example, the advice unit can select the optimal level of detail in its advice based on product importance data. By adjusting the level of detail in the advice based on product importance, it can provide more appropriate advice. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input product importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the advice.

[0040] The advice unit can apply different advice algorithms depending on the product category when providing advice. For example, the advice unit can provide styling advice for fashion items. It can also provide advice on functions and performance for home appliances. Furthermore, it can provide advice on nutritional value and storage methods for food products. For example, the advice unit selects the optimal advice algorithm based on product category data. By applying different advice algorithms depending on the product category, it can provide more appropriate advice. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input product category data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0041] The real-time unit can select the optimal timing for providing advice in real time by referring to the user's past response data. For example, the real-time unit can select the timing of advice provision based on when the user has shown a positive response in the past. The real-time unit can also select the timing of advice provision by avoiding when the user has shown a negative response in the past. Furthermore, the real-time unit can analyze the user's past response data and provide advice at the most effective timing. For example, the real-time unit can select the optimal timing for providing advice based on the user's past response data. This allows advice to be provided at the optimal timing by referring to the user's past response data. Some or all of the above processing in the real-time unit may be performed using AI, for example, or without AI. For example, the real-time unit can input the user's past response data into a generating AI and have the generating AI perform the selection of the advice provision timing.

[0042] The real-time unit can customize the content of advice based on the user's current situation when providing advice in real time. For example, if the user is in a store, the real-time unit can provide advice on products in the store. It can also provide advice on products available for online purchase if the user is online shopping. Furthermore, if the user is participating in a specific event, the real-time unit can provide advice on products related to that event. For example, the real-time unit selects the most appropriate advice based on the user's current situation data. This allows for the provision of more appropriate advice by customizing the content of the advice based on the user's current situation. Some or all of the above processing in the real-time unit may be performed using AI, for example, or without AI. For example, the real-time unit can input the user's current situation data into a generating AI and have the generating AI customize the content of the advice.

[0043] The decision-making unit can improve the accuracy of its decisions by referring to the user's past fashion history. For example, the decision-making unit can analyze the styles of items the user has purchased in the past and adjust the criteria for determining what is unsuitable. It can also adjust the criteria for determining what is unsuitable based on the colors and designs the user has preferred to wear in the past. Furthermore, the decision-making unit can refer to the user's past fashion history and make decisions based on trends. For example, the decision-making unit can select the optimal decision criteria based on the user's past fashion history data. This improves the accuracy of decisions by referring to the user's past fashion history. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's past fashion history data into a generating AI and have the generating AI perform the adjustment of the decision criteria.

[0044] The decision-making unit can customize its judgment criteria based on the user's current fashion trends when making a decision. For example, the unit can adjust the criteria for determining what is unsuitable based on the style the user currently prefers. Furthermore, if the user is sensitive to current trends, the unit can apply trend-based judgment criteria. Additionally, if the user prefers a particular brand or designer, the unit can adjust its judgment criteria based on the style of that brand or designer. For example, the unit can select the optimal judgment criteria based on the user's current fashion trend data. This allows for more appropriate decisions by customizing the judgment criteria based on the user's current fashion trends. Some or all of the above processing in the decision-making unit may be performed using AI, or not. For example, the decision-making unit can input the user's current fashion trend data into a generating AI and have the generating AI perform the customization of the judgment criteria.

[0045] The recommendation unit can select the optimal recommendation method by referring to the user's past purchase history when making recommendations. For example, the recommendation unit can analyze the styles of items the user has purchased in the past and recommend items that suit them. The recommendation unit can also recommend optimal items based on brands and designs that the user has previously preferred to purchase. Furthermore, the recommendation unit can also recommend items based on trends by referring to the user's past purchase history. For example, the recommendation unit can select the optimal recommendation method based on the user's past purchase history data. This allows the recommendation unit to select the optimal recommendation method by referring to the user's past purchase history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past purchase history data into a generating AI and have the generating AI perform the selection of the recommendation method.

[0046] The recommendation unit can customize its recommendations based on the user's current areas of interest. For example, it can recommend the most suitable items based on the style the user is currently interested in. It can also recommend trend-based items if the user is sensitive to current trends. Furthermore, if the user prefers a particular brand or designer, it can recommend items from that brand or designer. For example, the recommendation unit selects the most suitable recommendations based on the user's current areas of interest data. This allows for the recommendation of more appropriate items by customizing the recommendations based on the user's current areas of interest. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input the user's current areas of interest data into a generating AI and have the generating AI customize the recommendations.

[0047] The recommendation unit can select the optimal recommendation method by considering the user's geographical location information when making recommendations. For example, if the user lives in a specific region, the recommendation unit can recommend items that are popular in that region. Furthermore, if the user is traveling, the recommendation unit can recommend items suitable for purchase at their travel destination. Additionally, if the user is in a specific store, the recommendation unit can recommend items suitable for purchase at that store. For example, the recommendation unit selects the optimal recommendation method based on the user's geographical location information. This allows the recommendation unit to select the optimal recommendation method by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the recommendation method.

[0048] The recommendation unit can analyze the user's social media activity and adjust the recommendation content when making recommendations. For example, the recommendation unit can make recommendations based on items that the user has "liked" or commented on on social media. It can also recommend items that have been featured by influencers that the user follows. Furthermore, it can recommend items that are trending in social media groups that the user participates in. For example, the recommendation unit can select the most appropriate recommendations based on the user's social media activity data. This allows for more appropriate item recommendations by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI adjust the recommendation content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can collect user health data and provide purchase advice based on their health status. For example, it can collect heart rate and exercise data from the user's fitness tracker or smartwatch to understand their health status. It can also collect nutritional intake data from the user's food logging app and provide advice to support a healthy diet. Furthermore, it can collect sleep data from the user's sleep tracker and provide advice to improve sleep quality. This allows for more appropriate purchase advice to be provided based on the user's health status.

[0051] The recommendation system can analyze a user's past purchase history and predict trends. For example, it can predict items that are likely to become popular next based on data from items a user has purchased in the past. It can also predict seasonal trends based on a user's purchase history and suggest items at the appropriate time. Furthermore, it can analyze a user's purchase history and predict items related to specific events or holidays. This allows the system to predict future trends based on a user's past purchase history and suggest more appropriate items.

[0052] The decision-making unit can make more accurate decisions by combining the user's past purchase history with current trends. For example, the unit can determine whether an item matches current trends based on data of items the user has purchased in the past. It can also analyze the user's preferences for specific brands or styles from their past purchase history and make decisions based on current trends. Furthermore, the unit can suggest the most suitable items by combining the user's past purchase history with current trends. This allows for more accurate decisions by combining the user's past purchase history with current trends.

[0053] The recommendation system can analyze regional trends based on the user's geographical location and suggest the most suitable items. For example, it can analyze trends in the user's area of ​​residence and suggest popular items in that area. If the user is traveling, it can also analyze trends in their destination and suggest popular items there. Furthermore, if the user is attending a specific event, it can suggest items related to that event. This allows the system to analyze regional trends based on the user's geographical location and suggest the most suitable items.

[0054] The decision-making unit can make more accurate decisions by combining the user's past purchase history with their current emotions. For example, the unit can determine items that match the user's current emotions based on data of items the user has purchased in the past. It can also analyze the user's preferences for specific brands or styles from their past purchase history and make decisions based on their current emotions. Furthermore, the unit can combine the user's past purchase history with their current emotions to determine the most suitable items. This allows for more accurate decisions by combining the user's past purchase history with their current emotions.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects user preferences and purchase history. For example, it collects data on items the user has purchased in the past, as well as data on preferred brands and styles. It also analyzes the user's purchase history to identify categories of items that are frequently purchased and collects data related to those categories. Step 2: The advice unit provides advice based on the data collected by the data collection unit. For example, it determines whether the item selected by the user suits them and provides appropriate advice. It also suggests the best item from among several items and provides personalized advice. Step 3: The real-time unit provides advice in real time based on the advice provided by the advice unit. For example, it provides advice in real time when a user is shopping online or in a store. Step 4: The judgment unit determines that an item is "unsuitable" based on the advice provided by the advice unit. For example, if an item chosen by the user does not match the user's style or preferences, the judgment unit will determine that it is "unsuitable." Step 5: The recommendation section provides "encouraging" advice from the advice section. For example, if the item chosen by the user suits the user, it provides encouraging advice such as, "I think this is a good choice."

[0057] (Example of form 2) The smartphone assistant system according to an embodiment of the present invention is a tool for making the shopping experience more enjoyable and intelligent. This smartphone assistant system collects user preferences and past purchase history data and provides appropriate and frank advice in real time. For example, it provides advice such as "That doesn't suit you" or "Do you really need that?" to prevent wasteful spending. It also provides encouraging advice such as "I think this is a good choice" to those who are undecided. This smartphone assistant system primarily targets people who have risks such as "wasteful spending" or "indecisiveness" when shopping, and people who want to enjoy shopping efficiently and with high satisfaction. By providing appropriate and frank advice in real time based on user preferences and purchase history data, it improves the shopping experience. For example, when purchasing fashion items, the AI ​​judges whether the item selected by the user suits them and provides appropriate advice. Also, if the user is unsure which of several items to choose, the AI ​​suggests the optimal item. As a result, users can enjoy shopping efficiently and obtain a highly satisfying shopping experience. This mechanism allows users to prevent wasteful spending and enjoy highly satisfying shopping. Furthermore, because the AI ​​provides advice in real time, users can proceed with their shopping efficiently. For example, when shopping online, AI can provide appropriate advice, allowing users to quickly select the best items. Furthermore, this AI generates personalized advice based on the user's preferences and purchase history data, providing advice tailored to each individual user. This enables users to choose the items that are best suited to them, resulting in a more satisfying shopping experience. In this way, smartphone assistant systems can improve the user's shopping experience, prevent wasteful spending, and support more satisfying shopping.

[0058] The smartphone assistant system according to this embodiment comprises a data collection unit, an advice unit, a real-time unit, a judgment unit, and a recommendation unit. The data collection unit collects the user's preferences and purchase history. For example, the data collection unit collects data on items the user has purchased in the past. The data collection unit can also collect data on brands and styles that the user prefers. Furthermore, the data collection unit can analyze the user's purchase history and identify the user's preferences. For example, the data collection unit identifies categories of items that the user frequently purchases and collects data on those categories. The advice unit provides advice based on the data collected by the data collection unit. For example, the advice unit determines whether the item the user has chosen suits them and provides appropriate advice. The advice unit can also suggest the best item if the user is unsure which of several items to choose. Furthermore, the advice unit can provide personalized advice based on the user's preferences and purchase history. For example, the advice unit suggests items in a similar style to items the user has purchased in the past. The real-time unit provides the advice provided by the advice unit in real time. For example, the real-time unit provides advice in real time when the user is online shopping. Furthermore, the real-time unit can provide real-time advice while the user is shopping in a store. It can also provide real-time advice while the user is selecting items. For example, the real-time unit can determine in real-time whether the item the user has chosen suits them and provide advice. The judgment unit, based on the advice provided by the advice unit, can determine that the item "does not suit" the user. For example, the judgment unit will determine that the item the user has chosen does not suit them if it does not match their style. It can also determine that the item the user has chosen does not suit them if it does not match their preferences. Furthermore, the judgment unit can determine that the item the user has chosen does not suit them if it does not match their body type. For example, the judgment unit can determine whether the selected item suits the user's body type based on the user's body type data.The recommendation unit provides "encouraging" advice from the advice provided by the advice unit. For example, if the item selected by the user suits the user, the recommendation unit will provide encouraging advice such as "I think this is a good choice." The recommendation unit can also provide encouraging advice such as "I think this is a good choice" if the item selected by the user matches the user's preferences. Furthermore, the recommendation unit can also provide encouraging advice such as "I think this is a good choice" if the item selected by the user suits the user's body type. For example, the recommendation unit will determine whether the selected item suits the user's body type based on the user's body type data and provide encouraging advice such as "I think this is a good choice." As a result, the smartphone assistant system according to this embodiment can provide appropriate advice in real time based on the user's preferences and purchase history, prevent wasteful spending, and support highly satisfying shopping.

[0059] The data collection unit collects user preferences and purchase history. For example, it collects data on items that users have purchased in the past. Specifically, it obtains purchase history data from online shopping sites and applications and analyzes what kinds of products users frequently purchase. The data collection unit can also collect data on brands and styles that users prefer. For example, if a user frequently purchases products from a particular brand, it collects information about that brand to identify the user's preferences. Furthermore, the data collection unit can analyze a user's purchase history to identify their preferences. For example, it can identify the categories of items that a user frequently purchases and collect data on those categories. This allows the data collection unit to gain a detailed understanding of user preferences and purchasing trends, and to collect foundational data to provide optimal advice to individual users. In addition, the data collection unit can also collect other relevant data, such as users' social media activity and search history. This allows for a broader understanding of user interests and preferences, enabling the provision of more accurate advice. For example, if a user frequently visits a particular fashion blog, the content of that blog can be analyzed to identify the user's preferences. This allows the data collection unit to gather information from diverse user data sources and create a comprehensive user profile.

[0060] The advice department provides advice based on data collected by the data collection department. For example, the advice department can determine whether an item selected by a user suits them and provide appropriate advice. Specifically, it uses AI to analyze the user's past purchase history and preference data to evaluate whether the selected item suits the user's style. The advice department can also suggest the best item if the user is unsure which of several items to choose. For example, the AI ​​considers the user's past selection patterns and current trends to recommend the most suitable item. Furthermore, the advice department can provide personalized advice based on the user's preferences and purchase history. For example, it can suggest items with a similar style to items the user has purchased in the past. This allows the advice department to provide specific advice tailored to the user's individual needs and increase user satisfaction. In addition, the advice department can collect user feedback and continuously improve the accuracy of its advice. For example, it can analyze how users reacted to the advice provided and reflect this in future advice. This allows the advice department to flexibly respond to changes in user preferences and needs and always provide the best advice.

[0061] The Real-Time Unit provides advice in real time, based on the advice provided by the Advice Unit. For example, the Real-Time Unit provides advice in real time when a user is online shopping. Specifically, when a user is browsing a product page, it displays advice in a pop-up window based on their past purchase history and preferences. The Real-Time Unit can also provide advice in real time when a user is shopping in a store. For example, it can obtain location information within the store via a smartphone app and notify the user of recommended products nearby. Furthermore, the Real-Time Unit can provide advice in real time while a user is selecting products. For example, when a user is trying on clothes in a fitting room, it can use a smart mirror to determine in real time whether the selected item suits them and provide advice. In this way, the Real-Time Unit can provide immediately useful information while the user is shopping, supporting them in making better choices. In addition, the Real-Time Unit can collect user behavior data in real time to improve the accuracy of advice. For example, it can analyze in real time what kind of products a user is interested in and provide advice based on that information. In this way, the Real-Time Unit can provide advice that is immediately tailored to the user's needs and improve the shopping experience.

[0062] The judgment unit determines that an item is "unsuitable" based on the advice provided by the advice unit. For example, the judgment unit determines that an item selected by the user is "unsuitable" if it does not suit the user's style. Specifically, it uses AI to analyze the user's past purchase history and preference data to determine if the selected item does not suit the user's style. The judgment unit can also determine that an item is "unsuitable" if it does not suit the user's preferences. For example, if the user selects an item in a style or color they have avoided in the past, the judgment unit will determine that the item does not suit their preferences. Furthermore, the judgment unit can also determine that an item is "unsuitable" if it does not suit the user's body type. For example, it uses the user's body type data to determine if the selected item suits their body type. This allows the judgment unit to prevent users from making inappropriate choices and support them in making better choices. In addition, the judgment unit can collect user feedback and continuously improve the accuracy of its judgments. For example, it can analyze how users reacted to the judgments provided and reflect this in future judgments. This allows the judgment unit to flexibly respond to changes in user preferences and needs and always provide the best possible judgment.

[0063] The recommendation section provides "encouraging" advice from the advice section. For example, if the item chosen by the user suits the user, the recommendation section will offer encouraging advice such as, "I think this is a good choice." Specifically, it uses AI to analyze the user's past purchase history and preference data to determine if the chosen item suits the user's style. The recommendation section can also offer encouraging advice such as, "I think this is a good choice," if the item chosen by the user matches the user's preferences. For example, if the user chooses an item in a style or color they have previously purchased, the recommendation section will determine if that item suits their preferences. Furthermore, the recommendation section can also offer encouraging advice such as, "I think this is a good choice," if the item chosen by the user suits the user's body type. For example, based on the user's body type data, it will determine if the chosen item suits their body type and offer encouraging advice such as, "I think this is a good choice." In this way, the recommendation section can support users in making choices with confidence and provide a highly satisfying shopping experience. In addition, the recommendation section can collect user feedback and continuously improve the accuracy of its advice. For example, it can analyze how users reacted to the advice provided and reflect this in future advice. This allows the recommendation system to flexibly adapt to changes in user preferences and needs, and always provide the best advice.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of collecting preferences and purchase history based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. Alternatively, if the user is excited, the data collection unit can collect data immediately and incorporate it into real-time advice. Furthermore, if the user is tired, the data collection unit can adjust the collection timing to collect data after the user has rested. For example, the data collection unit selects the optimal collection timing based on the user's emotional data. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotional data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, the data collection unit can identify product categories that a user frequently purchases and select a data collection method specific to those categories. The data collection unit can also analyze a user's preferences for specific brands or stores from their purchase history and collect data based on that analysis. Furthermore, the data collection unit can analyze a user's purchase history by time of day and day of the week to select the optimal data collection timing. For example, if a user tends to make purchases during a particular time of day, the data collection unit will collect data during that time. This allows the optimal data collection method to be selected by analyzing past purchase history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0066] The data collection unit can filter purchase history based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit will prioritize collecting purchase history related to that hobby. Furthermore, if a user moves, the data collection unit can collect purchase history of product categories suitable for their new living environment. Additionally, if a user plans to attend a specific event, the data collection unit can collect purchase history related to that event. For example, the data collection unit filters the most relevant purchase history based on the user's lifestyle data. This allows for the collection of more relevant data by filtering the data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit may prioritize collecting data on the latest trending products. Similarly, if the user is relaxed, the data collection unit may prioritize collecting data on long-term purchase plans. Furthermore, if the user is stressed, the data collection unit may prioritize collecting data on products with relaxing effects. For example, the data collection unit determines the optimal data priority based on the user's emotional data. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotional data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting purchase history. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of data on popular products in that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of purchase history at their travel destination. Additionally, if the user frequently visits a particular store, the data collection unit can prioritize the collection of purchase history at that store. For example, the data collection unit determines the optimal data priority based on the user's geographical location. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI determine the data priority.

[0069] The data collection unit can analyze the user's social media activity and collect relevant data when collecting purchase history. For example, the data collection unit can collect data on products that the user has "liked" or commented on on social media. It can also collect data on products that have been featured by influencers that the user follows. Furthermore, the data collection unit can collect data on products that are trending in social media groups that the user participates in. For example, the data collection unit can select the optimal data collection method based on the user's social media activity data. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0070] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is relaxed, the advice unit will provide advice in a gentle tone. If the user is excited, the advice unit can provide advice in an energetic tone. Furthermore, if the user is stressed, the advice unit can provide advice in a calm and composed tone. For example, the advice unit can select the optimal way to express advice based on the user's emotion data. This allows for more appropriate advice to be provided by adjusting the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, or not. For example, the advice unit can input the user's emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0071] The advice unit can adjust the level of detail in its advice based on the importance of the product when providing advice. For example, it can provide detailed advice for high-priced products. It can also provide concise advice for products used on a daily basis. Furthermore, it can provide detailed advice for products that the user is particularly interested in. For example, the advice unit can select the optimal level of detail in its advice based on product importance data. By adjusting the level of detail in the advice based on product importance, it can provide more appropriate advice. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input product importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the advice.

[0072] The advice unit can apply different advice algorithms depending on the product category when providing advice. For example, the advice unit can provide styling advice for fashion items. It can also provide advice on functions and performance for home appliances. Furthermore, it can provide advice on nutritional value and storage methods for food products. For example, the advice unit selects the optimal advice algorithm based on product category data. By applying different advice algorithms depending on the product category, it can provide more appropriate advice. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input product category data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0073] The real-time unit can estimate the user's emotions and adjust the timing of advice delivery in real time based on the estimated emotions. For example, if the user is excited, the real-time unit can provide advice immediately. It can also provide advice at an appropriate time if the user is relaxed. Furthermore, if the user is stressed, the real-time unit can provide advice at a suitable time. For example, the real-time unit selects the optimal advice delivery timing based on the user's emotion data. By adjusting the advice delivery timing according to the user's emotions, advice can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the real-time unit may be performed using AI, or not. For example, the real-time unit can input user emotion data into the generative AI and have the generative AI adjust the advice delivery timing.

[0074] The real-time unit can select the optimal timing for providing advice in real time by referring to the user's past response data. For example, the real-time unit can select the timing of advice provision based on when the user has shown a positive response in the past. The real-time unit can also select the timing of advice provision by avoiding when the user has shown a negative response in the past. Furthermore, the real-time unit can analyze the user's past response data and provide advice at the most effective timing. For example, the real-time unit can select the optimal timing for providing advice based on the user's past response data. This allows advice to be provided at the optimal timing by referring to the user's past response data. Some or all of the above processing in the real-time unit may be performed using AI, for example, or without AI. For example, the real-time unit can input the user's past response data into a generating AI and have the generating AI perform the selection of the advice provision timing.

[0075] The real-time unit can customize the content of advice based on the user's current situation when providing advice in real time. For example, if the user is in a store, the real-time unit can provide advice on products in the store. It can also provide advice on products available for online purchase if the user is online shopping. Furthermore, if the user is participating in a specific event, the real-time unit can provide advice on products related to that event. For example, the real-time unit selects the most appropriate advice based on the user's current situation data. This allows for the provision of more appropriate advice by customizing the content of the advice based on the user's current situation. Some or all of the above processing in the real-time unit may be performed using AI, for example, or without AI. For example, the real-time unit can input the user's current situation data into a generating AI and have the generating AI customize the content of the advice.

[0076] The decision unit can estimate the user's emotions and adjust the criteria for determining whether something "doesn't suit" based on the estimated emotions. For example, if the user is relaxed, the decision unit may use stricter criteria to determine that something "doesn't suit." Conversely, if the user is excited, the decision unit may use more lenient criteria to determine that something "doesn't suit." Furthermore, if the user is stressed, the decision unit may use emotionally sensitive criteria to determine that something "doesn't suit." For example, the decision unit selects the optimal criteria based on the user's emotional data. This allows for more appropriate decisions by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision unit may be performed using AI, or not. For example, the decision unit can input user emotional data into the generative AI and have the generative AI adjust the criteria.

[0077] The decision-making unit can improve the accuracy of its decisions by referring to the user's past fashion history. For example, the decision-making unit can analyze the styles of items the user has purchased in the past and adjust the criteria for determining what is unsuitable. It can also adjust the criteria for determining what is unsuitable based on the colors and designs the user has preferred to wear in the past. Furthermore, the decision-making unit can refer to the user's past fashion history and make decisions based on trends. For example, the decision-making unit can select the optimal decision criteria based on the user's past fashion history data. This improves the accuracy of decisions by referring to the user's past fashion history. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's past fashion history data into a generating AI and have the generating AI perform the adjustment of the decision criteria.

[0078] The decision-making unit can customize its judgment criteria based on the user's current fashion trends when making a decision. For example, the unit can adjust the criteria for determining what is unsuitable based on the style the user currently prefers. Furthermore, if the user is sensitive to current trends, the unit can apply trend-based judgment criteria. Additionally, if the user prefers a particular brand or designer, the unit can adjust its judgment criteria based on the style of that brand or designer. For example, the unit can select the optimal judgment criteria based on the user's current fashion trend data. This allows for more appropriate decisions by customizing the judgment criteria based on the user's current fashion trends. Some or all of the above processing in the decision-making unit may be performed using AI, or not. For example, the decision-making unit can input the user's current fashion trend data into a generating AI and have the generating AI perform the customization of the judgment criteria.

[0079] The recommendation unit can estimate the user's emotions and adjust the way it expresses "encouraging" advice based on those emotions. For example, if the user is relaxed, the recommendation unit can provide "encouraging" advice in gentle words. If the user is excited, it can provide "encouraging" advice in energetic words. Furthermore, if the user is stressed, it can provide "encouraging" advice in calm and composed words. For example, the recommendation unit selects the optimal way to express the advice based on the user's emotion data. This allows for more appropriate advice to be provided by adjusting the way the advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the way the advice is expressed.

[0080] The recommendation unit can select the optimal recommendation method by referring to the user's past purchase history when making recommendations. For example, the recommendation unit can analyze the styles of items the user has purchased in the past and recommend items that suit them. The recommendation unit can also recommend optimal items based on brands and designs that the user has previously preferred to purchase. Furthermore, the recommendation unit can also recommend items based on trends by referring to the user's past purchase history. For example, the recommendation unit can select the optimal recommendation method based on the user's past purchase history data. This allows the recommendation unit to select the optimal recommendation method by referring to the user's past purchase history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past purchase history data into a generating AI and have the generating AI perform the selection of the recommendation method.

[0081] The recommendation unit can customize its recommendations based on the user's current areas of interest. For example, it can recommend the most suitable items based on the style the user is currently interested in. It can also recommend trend-based items if the user is sensitive to current trends. Furthermore, if the user prefers a particular brand or designer, it can recommend items from that brand or designer. For example, the recommendation unit selects the most suitable recommendations based on the user's current areas of interest data. This allows for the recommendation of more appropriate items by customizing the recommendations based on the user's current areas of interest. Some or all of the above processing in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input the user's current areas of interest data into a generating AI and have the generating AI customize the recommendations.

[0082] The recommendation unit can estimate the user's emotions and prioritize "encouraging" advice based on those emotions. For example, if the user is excited, the recommendation unit will prioritize important advice. It can also prioritize detailed advice if the user is relaxed. Furthermore, if the user is stressed, it can prioritize relaxing advice. For instance, the recommendation unit selects the optimal advice priority based on the user's emotional data. This allows for more appropriate advice to be provided by prioritizing advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotional data into a generative AI and have the generative AI determine the advice priority.

[0083] The recommendation unit can select the optimal recommendation method by considering the user's geographical location information when making recommendations. For example, if the user lives in a specific region, the recommendation unit can recommend items that are popular in that region. Furthermore, if the user is traveling, the recommendation unit can recommend items suitable for purchase at their travel destination. Additionally, if the user is in a specific store, the recommendation unit can recommend items suitable for purchase at that store. For example, the recommendation unit selects the optimal recommendation method based on the user's geographical location information. This allows the recommendation unit to select the optimal recommendation method by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the recommendation method.

[0084] The recommendation unit can analyze the user's social media activity and adjust the recommendation content when making recommendations. For example, the recommendation unit can make recommendations based on items that the user has "liked" or commented on on social media. It can also recommend items that have been featured by influencers that the user follows. Furthermore, it can recommend items that are trending in social media groups that the user participates in. For example, the recommendation unit can select the most appropriate recommendations based on the user's social media activity data. This allows for more appropriate item recommendations by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI adjust the recommendation content.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The data collection unit can collect user health data and provide purchase advice based on their health status. For example, it can collect heart rate and exercise data from the user's fitness tracker or smartwatch to understand their health status. It can also collect nutritional intake data from the user's food logging app and provide advice to support a healthy diet. Furthermore, it can collect sleep data from the user's sleep tracker and provide advice to improve sleep quality. This allows for more appropriate purchase advice to be provided based on the user's health status.

[0087] The advice section can estimate the user's emotions and customize the advice based on those estimates. For example, if the user is stressed, it can suggest products that promote relaxation. If the user is excited, it can suggest products suitable for an active lifestyle. Furthermore, if the user is sad, it can suggest items to lift their spirits. This allows for more personalized advice tailored to the user's emotions.

[0088] The recommendation system can analyze a user's past purchase history and predict trends. For example, it can predict items that are likely to become popular next based on data from items a user has purchased in the past. It can also predict seasonal trends based on a user's purchase history and suggest items at the appropriate time. Furthermore, it can analyze a user's purchase history and predict items related to specific events or holidays. This allows the system to predict future trends based on a user's past purchase history and suggest more appropriate items.

[0089] The real-time unit can estimate the user's emotions and adjust the frequency of real-time advice based on those emotions. For example, if the user is stressed, the frequency of advice can be reduced to help the user relax. Conversely, if the user is excited, the frequency of advice can be increased to support their excitement. Furthermore, if the user is relaxed, advice can be provided at an appropriate frequency. In this way, by adjusting the frequency of real-time advice according to the user's emotions, more appropriate support can be provided.

[0090] The decision-making unit can make more accurate decisions by combining the user's past purchase history with current trends. For example, the unit can determine whether an item matches current trends based on data of items the user has purchased in the past. It can also analyze the user's preferences for specific brands or styles from their past purchase history and make decisions based on current trends. Furthermore, the unit can suggest the most suitable items by combining the user's past purchase history with current trends. This allows for more accurate decisions by combining the user's past purchase history with current trends.

[0091] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data to reduce stress. If the user is excited, the data collection unit can also prioritize collecting data to support the excitement. Furthermore, if the user is relaxed, the data collection unit can also prioritize collecting data to maintain relaxation. In this way, by adjusting the data collection method according to the user's emotions, more appropriate data can be collected.

[0092] The advice section can provide more personalized advice by combining a user's past purchase history with their current emotions. For example, it can suggest items that match a user's current emotions based on data from items they have purchased in the past. The advice section can also analyze a user's preferences for specific brands or styles from their past purchase history and provide advice based on their current emotions. Furthermore, the advice section can combine a user's past purchase history with their current emotions to suggest the most suitable items. This allows for more personalized advice by combining a user's past purchase history with their current emotions.

[0093] The recommendation system can analyze regional trends based on the user's geographical location and suggest the most suitable items. For example, it can analyze trends in the user's area of ​​residence and suggest popular items in that area. If the user is traveling, it can also analyze trends in their destination and suggest popular items there. Furthermore, if the user is attending a specific event, it can suggest items related to that event. This allows the system to analyze regional trends based on the user's geographical location and suggest the most suitable items.

[0094] The real-time unit can estimate the user's emotions and adjust the content of the advice provided in real time based on those emotions. For example, if the user is feeling stressed, it can provide advice that promotes relaxation. If the user is excited, it can provide advice suitable for an active lifestyle. Furthermore, if the user is relaxed, it can provide advice to help maintain that relaxation. In this way, by adjusting the content of the advice provided in real time according to the user's emotions, more appropriate support can be provided.

[0095] The decision-making unit can make more accurate decisions by combining the user's past purchase history with their current emotions. For example, the unit can determine items that match the user's current emotions based on data of items the user has purchased in the past. It can also analyze the user's preferences for specific brands or styles from their past purchase history and make decisions based on their current emotions. Furthermore, the unit can combine the user's past purchase history with their current emotions to determine the most suitable items. This allows for more accurate decisions by combining the user's past purchase history with their current emotions.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects user preferences and purchase history. For example, it collects data on items the user has purchased in the past, as well as data on preferred brands and styles. It also analyzes the user's purchase history to identify categories of items that are frequently purchased and collects data related to those categories. Step 2: The advice unit provides advice based on the data collected by the data collection unit. For example, it determines whether the item selected by the user suits them and provides appropriate advice. It also suggests the best item from among several items and provides personalized advice. Step 3: The real-time unit provides advice in real time based on the advice provided by the advice unit. For example, it provides advice in real time when a user is shopping online or in a store. Step 4: The judgment unit determines that an item is "unsuitable" based on the advice provided by the advice unit. For example, if an item chosen by the user does not match the user's style or preferences, the judgment unit will determine that it is "unsuitable." Step 5: The recommendation section provides "encouraging" advice from the advice section. For example, if the item chosen by the user suits the user, it provides encouraging advice such as, "I think this is a good choice."

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements described above, including the data collection unit, advice unit, real-time unit, decision unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user preferences and purchase history using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The advice unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates appropriate advice based on the collected data. The real-time unit is implemented, for example, by the control unit 46A of the smart device 14 and provides advice in real time. The decision unit and recommendation unit are implemented, for example, by the identification processing unit 290 of the data processing unit 12 and make appropriate decisions and recommendations based on the user's selection. The data collection unit is also implemented, for example, by the identification processing unit 290 of the data processing unit 12, which estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the data collection unit, advice unit, real-time unit, judgment unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect user preferences and purchase history, and transmits them to the data processing unit 12 via the control unit 46A. The advice unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates appropriate advice based on the collected data. The real-time unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides advice in real time. The judgment unit and recommendation unit are implemented, for example, by the identification processing unit 290 of the data processing unit 12, and make appropriate judgments and recommendations based on the user's selection. The data collection unit is also implemented, for example, by the identification processing unit 290 of the data processing unit 12, which estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the data collection unit, advice unit, real-time unit, judgment unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect user preferences and purchase history, and transmits them to the data processing unit 12 via the control unit 46A. The advice unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates appropriate advice based on the collected data. The real-time unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides advice in real time. The judgment unit and recommendation unit are implemented, for example, by the identification processing unit 290 of the data processing unit 12, and make appropriate judgments and recommendations based on user selections. The data collection unit is also implemented, for example, by the identification processing unit 290 of the data processing unit 12, which estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the data collection unit, advice unit, real-time unit, decision unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect user preferences and purchase history, and transmits them to the data processing unit 12 via the control unit 46A. The advice unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates appropriate advice based on the collected data. The real-time unit is implemented, for example, by the control unit 46A of the robot 414, and provides advice in real time. The decision unit and recommendation unit are implemented, for example, by the identification processing unit 290 of the data processing unit 12, and make appropriate decisions and recommendations based on user selections. The data collection unit is also implemented, for example, by the identification processing unit 290 of the data processing unit 12, which estimates the user's emotions using an emotion engine or generative AI and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A data collection unit that collects user preferences and purchase history, An advice unit provides advice based on the data collected by the aforementioned collection unit, A real-time unit that provides the advice provided by the aforementioned advice unit in real time, Among the advice provided by the aforementioned advice unit, there is a judgment unit that determines that something "does not suit" the person, The advice provided by the aforementioned advice section includes a "encouraging" recommendation section. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting preferences and purchase history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting purchase history, we analyze the user's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The real-time unit is, It estimates the user's emotions and adjusts the timing of real-time advice delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The real-time unit is, When providing real-time advice, the system selects the optimal timing by referring to the user's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The real-time unit is, When providing real-time advice, the advice is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The unit that makes the determination said, It estimates the user's emotions and adjusts the criteria for determining what "doesn't suit" based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The unit that makes the determination said, When making a decision, the system improves the accuracy of the decision by referencing the user's past fashion history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The unit that makes the determination said, When making a decision, the criteria are customized based on the user's current fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation section is, It estimates the user's emotions and adjusts the way it expresses "encouraging" advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation section is, When making recommendations, the system selects the most suitable recommendation method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation section is, When making recommendations, customize the recommendations based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation section is, It estimates the user's emotions and prioritizes "encouraging" advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation section is, When making recommendations, the system selects the most suitable recommendation method by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation section is, When making recommendations, we analyze the user's social media activity and adjust the recommendations accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user preferences and purchase history, An advice unit provides advice based on the data collected by the aforementioned collection unit, A real-time unit that provides the advice provided by the aforementioned advice unit in real time, A judgment unit determines that the advice provided by the aforementioned advice unit is unsuitable, The advice provided by the aforementioned advice section includes a recommendation section that encourages the user to take action, and A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting preferences and purchase history based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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

6. The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting purchase history, we analyze the user's social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the product. The system according to feature 1.

10. The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the product category. The system according to feature 1.

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