system

Abstract

The system according to the embodiment aims to efficiently obtain inventory information of products that a user needs and to suggest appropriate stores. [Solution] A system according to an embodiment includes a search unit, an acquisition unit, a suggestion unit, and a provision unit. The search unit allows a user to search for products. The acquisition unit acquires inventory information based on the products searched by the search unit. The suggestion unit suggests appropriate stores based on the inventory information acquired by the acquisition unit. The provision unit provides detailed information about the stores suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when users search for a product they need, it is difficult to efficiently find out whether the product is in stock or which stores carry it.

[0005] The system according to the embodiment aims to efficiently obtain inventory information of products that a user needs and to suggest appropriate stores. [Means for solving the problem]

[0006] The system according to the embodiment includes a search unit, an acquisition unit, a suggestion unit, and a provision unit. The search unit allows a user to search for a product. The acquisition unit acquires inventory information based on the product searched by the search unit. The suggestion unit suggests an appropriate store based on the inventory information acquired by the acquisition unit. The provision unit provides detailed information about the store suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently obtain inventory information for products that a user needs and suggest appropriate stores. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A product search AI system according to an embodiment of the present invention solves the problem of unexpectedly out-of-stock items when purchasing daily necessities or medical supplies. This product search AI system works in conjunction with affiliated stores, allowing users to check inventory and availability information from an app. First, a user opens the app and searches for a desired product. For example, they search for medicine or daily necessities that suddenly become necessary while traveling. To do this, the user simply enters the product name and category. The app retrieves inventory information from nearby affiliated stores based on the user's current location. Next, the app analyzes the retrieved inventory information and suggests the most suitable store for the user. For example, if the product searched for by the user is available at multiple stores, the app prioritizes displaying the nearest store or the store with the largest inventory. This allows users to purchase the product efficiently. Furthermore, the app provides detailed information about the suggested store, such as store hours, address, and phone number. This allows users to check necessary information before going to the store. This mechanism allows users to efficiently purchase products they suddenly need. This is particularly useful when making impromptu shopping trips, such as when traveling, as it allows users to obtain products without wasting time. In addition, by linking with partner stores, cashless payments can be made smoothly. For example, if a user suddenly needs cold medicine while traveling, they open the app and search for "cold medicine." The app will obtain inventory information from nearby partner stores based on the user's current location and suggest the nearest store. The user can check the details of the suggested store and visit the store during business hours to purchase the product. In this way, the product search AI system solves the user's concerns when making sudden shopping trips and supports efficient product purchases. This allows the product search AI system to efficiently purchase products that users suddenly need.

[0029] A product search AI system according to an embodiment includes a search unit, an acquisition unit, a suggestion unit, and a providing unit. The search unit allows a user to search for a product. When searching for a product, the user simply inputs, for example, the product name or category. The search unit searches for related products based on the user's input. For example, if the user inputs "cold medicine," the search unit can search for products related to cold medicine. The acquisition unit acquires inventory information based on the product searched by the search unit. The acquisition unit, for example, acquires inventory information of nearby affiliated stores based on the user's current location. The acquisition unit can access a database of affiliated stores to acquire inventory information. For example, the acquisition unit can preferentially acquire inventory information of the nearest store based on the user's current location. The suggestion unit suggests the most appropriate store based on the inventory information acquired by the acquisition unit. For example, if the product searched for by the user is sold at multiple stores, the suggestion unit preferentially displays the nearest store or a store with a large inventory. The suggestion unit can suggest the most appropriate store taking user convenience into consideration. The providing unit provides detailed information about the store suggested by the suggestion unit. The providing unit displays information such as store opening hours, address, and telephone number. The providing unit allows the user to check the necessary information before going to the store. As a result, the product search AI system according to the embodiment allows the user to efficiently search for products, obtain inventory information, suggest the most suitable store, and provide detailed information.

[0030] The product search AI system includes an alternative suggestion unit that suggests alternative products. The alternative suggestion unit suggests alternative products when the product is out of stock. For example, if the product searched for by the user is out of stock, the alternative suggestion unit can suggest similar products. The alternative suggestion unit selects alternative products based on criteria such as price, quality, and brand. For example, the alternative suggestion unit can suggest alternative products with a similar price. The alternative suggestion unit can also suggest alternative products with the same or higher quality. Furthermore, the alternative suggestion unit can suggest alternative products of a brand preferred by the user. In this way, the alternative suggestion unit can meet the user's needs by suggesting alternative products when the product is out of stock.

[0031] The product search AI system includes a prediction unit that predicts the next purchase based on the user's purchase history. The prediction unit predicts the next purchase based on the user's purchase history. The prediction unit can, for example, analyze products that the user has purchased in the past and predict the next purchase. The prediction unit makes predictions based on data such as purchase date and time, purchased products, and purchase frequency. For example, the prediction unit can predict products that the user will purchase regularly. The prediction unit can also analyze the user's purchasing patterns and predict the next purchase. Furthermore, the prediction unit can predict the time of the next purchase based on the user's purchase history. In this way, the prediction unit improves user convenience by predicting the next purchase based on the user's purchase history.

[0032] The product search AI system includes a voice reception unit that receives voice input. The voice reception unit receives user voice input. The voice reception unit is used, for example, when a user searches for products by voice. The voice reception unit can convert the user's voice into text using voice recognition technology. For example, when a user says, "Looking for cold medicine," the voice reception unit can convert the voice into text and send it to the search unit. The voice reception unit has a function to learn the voice characteristics of specific speakers to improve the accuracy of voice input. For example, the voice reception unit accumulates user voice data and learns the voice characteristics of individual users to improve the accuracy of voice recognition. In this way, the voice reception unit enables users to easily search for products.

[0033] The product search AI system includes a map display unit that displays search results on a map. The map display unit displays the search results on the map. For example, the map display unit can display stores that sell the product searched for by the user on the map. The map display unit displays nearby stores on the map based on the user's current location. For example, the map display unit can display the store closest to the user's current location on the map. The map display unit can also display a store icon on the map based on the store's location information. Furthermore, the map display unit can display detailed information about the store on the map. For example, the map display unit displays information such as the store's business hours, address, and phone number on the map. This allows the map display unit to intuitively grasp the location of the store.

[0034] The product search AI system includes a Wi-Fi connection unit that automatically connects to the store's Wi-Fi. The Wi-Fi connection unit automatically connects to the store's Wi-Fi. For example, the Wi-Fi connection unit can automatically connect to the store's Wi-Fi when a user arrives at the store. The Wi-Fi connection unit can set the connection timing and priority. For example, the Wi-Fi connection unit can automatically connect to the Wi-Fi when a user approaches the store. The Wi-Fi connection unit can also select the optimal Wi-Fi network taking into account connection stability and speed. Furthermore, the Wi-Fi connection unit can use encryption technology to ensure connection security. This allows the Wi-Fi connection unit to smoothly use the Internet for users.

[0035] The search unit can analyze the user's past search history and automatically complete appropriate search keywords. For example, the search unit can automatically complete related keywords based on product names searched for by the user in the past. The search unit can use data mining technology to analyze the user's past search history. For example, the search unit can prioritize displaying frequently searched keywords from the user's past search history. The search unit can also analyze the user's search patterns and suggest optimal keywords. For example, the search unit can evaluate the relevance of keywords searched for by the user in the past and automatically complete the most relevant keywords. This allows the search unit to improve search efficiency by automatically completing optimal search keywords based on the user's past search history.

[0036] The search unit can filter search results based on the user's current situation when searching. For example, when searching at night, the search unit prioritizes displaying stores that are open 24 hours a day. The search unit can acquire information such as the time of day, weather, and the user's location to take the user's current situation into consideration. For example, the search unit can prioritize displaying stores that are open based on the user's current time of day. In addition, the search unit can prioritize displaying indoor stores when it is raining. Furthermore, when searching on a holiday, the search unit can prioritize displaying stores that are open. In this way, the search unit can provide more appropriate search results by filtering search results based on the user's current situation.

[0037] During a search, the search unit can prioritize displaying highly relevant products by taking into account the user's geographical location information. For example, the search unit can prioritize displaying products from stores closest to the user's current location. The search unit can use GPS data or location information services to acquire the user's geographical location information. For example, the search unit can prioritize displaying products that are only available in a specific region based on the user's current location. The search unit can also prioritize displaying products that are available in nearby stores based on the user's location information. This allows the search unit to prioritize displaying highly relevant products by taking into account the user's geographical location information, thereby improving user convenience.

[0038] The search unit can analyze the user's social media activity during a search and display related products. For example, the search unit can prioritize displaying products that the user has mentioned on social media. The search unit can use data mining technology to analyze the user's social media activity. For example, the search unit can display products recommended by the user's social media followers. The search unit can also suggest products that the user might be interested in based on the user's social media activity. In this way, the search unit can display products that match the user's interests by analyzing the user's social media activity.

[0039] The acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method. The acquisition unit can acquire related inventory information based on, for example, products purchased by the user in the past. The acquisition unit can use data mining technology to analyze the user's past purchase history. For example, the acquisition unit can prioritize acquisition of inventory information for frequently purchased products based on the user's past purchase history. The acquisition unit can also analyze the user's purchasing patterns and acquire optimal inventory information. For example, the acquisition unit can evaluate the relevance of products purchased by the user in the past and acquire the most relevant inventory information. This allows the acquisition unit to efficiently acquire inventory information by selecting an optimal acquisition method based on the user's past purchase history.

[0040] When acquiring inventory information, the acquisition unit can filter the inventory information based on the user's current living situation and areas of interest. For example, if the user is interested in health, the acquisition unit can prioritize acquiring inventory information for health-related products. The acquisition unit can use the user's profile information to take the user's current living situation and areas of interest into consideration. For example, if the user is traveling, the acquisition unit can prioritize acquiring inventory information for products needed at the travel destination. The acquisition unit can also filter related inventory information based on the user's current living situation. For example, the acquisition unit can acquire highly relevant inventory information based on information such as the user's family composition, occupation, and hobbies. As a result, the acquisition unit can provide highly relevant information by filtering inventory information based on the user's current living situation and areas of interest.

[0041] When acquiring inventory information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring inventory information from a store closest to the user's current location. The acquisition unit can use GPS data or location information services to acquire the user's geographical location information. For example, the acquisition unit can prioritize acquiring area-specific inventory information based on the user's current location. The acquisition unit can also prioritize acquiring inventory information from nearby stores based on the user's location information. In this way, the acquisition unit can improve user convenience by prioritizing acquisition of highly relevant information by taking into account the user's geographical location information.

[0042] When acquiring inventory information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit can prioritize acquiring inventory information for products mentioned by the user on social media. The acquisition unit can use data mining technology to analyze the user's social media activity. For example, the acquisition unit can acquire inventory information for products recommended by the user's social media followers. The acquisition unit can also acquire inventory information for products that the user may be interested in from the user's social media activity. In this way, the acquisition unit can acquire inventory information that matches the user's interests by analyzing the user's social media activity.

[0043] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product. For example, for products with high importance, the suggestion unit makes a suggestion including detailed information. The suggestion unit can use criteria such as price, demand, and user ratings to evaluate the importance of the product. For example, the suggestion unit can provide detailed information for products with high prices or high demand. The suggestion unit can also provide detailed information for products with high user ratings. Furthermore, the suggestion unit can make a suggestion including concise information for products with low importance. For example, the suggestion unit can provide concise information for products with low prices or low demand. In this way, the suggestion unit can provide appropriate information to the user by adjusting the level of detail of the suggestion according to the importance of the product.

[0044] When making a suggestion, the suggestion unit can apply an appropriate suggestion algorithm depending on the product category. For example, for medical products, the suggestion unit makes a suggestion based on highly reliable information. The suggestion unit can use criteria such as product category or service category to classify product categories. For example, the suggestion unit can apply an appropriate suggestion algorithm based on categories such as medical products, daily necessities, and food. Furthermore, for daily necessities, the suggestion unit can also make a suggestion that emphasizes price and stock status. For example, the suggestion unit can evaluate the price and stock status of daily necessities and make an optimal suggestion. Furthermore, the suggestion unit can make an appropriate suggestion to the user by applying an optimal suggestion algorithm depending on the product category. As a result, the suggestion unit can make an appropriate suggestion to the user by applying an optimal suggestion algorithm depending on the product category.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the product. For example, the proposal unit prioritizes proposals for new products. The proposal unit can use criteria such as the product release date or the inventory update date to evaluate the time of submission of the product. For example, the proposal unit can prioritize proposals for products with a new release date. The proposal unit can also prioritize proposals for products on sale. For example, the proposal unit can provide the user with advantageous information by preferentially suggesting products on sale. Furthermore, the proposal unit can provide the user with appropriate information by determining the priority of proposals according to the time of submission of the product. As a result, the proposal unit can provide the user with appropriate information by determining the priority of proposals based on the time of submission of the product.

[0046] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit preferentially suggests products related to a product searched by the user. The suggestion unit can use criteria such as product category, user interests, and past purchase history to evaluate the relevance of the products. For example, the suggestion unit can preferentially suggest products related to a product searched by the user. The suggestion unit can also preferentially suggest highly relevant products based on the user's past purchase history. For example, the suggestion unit can evaluate the relevance of products purchased by the user in the past and suggest the most relevant product. Furthermore, the suggestion unit can provide appropriate information to the user by adjusting the order of suggestions based on the relevance of the products. This allows the suggestion unit to provide appropriate information to the user by adjusting the order of suggestions based on the relevance of the products.

[0047] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit selects the optimal display method based on display methods used by the user in the past. The providing unit can use data mining technology to analyze the user's past operation history. For example, the providing unit can prioritize and provide frequently used display methods based on the user's past operation history. The providing unit can also analyze the user's operation patterns and select the optimal display method. For example, the providing unit can evaluate the relevance of display methods used by the user in the past and select the most relevant display method. In this way, the providing unit can improve user convenience by selecting the optimal display method based on the user's past operation history.

[0048] The providing unit can filter information based on the user's current situation when providing the information. For example, when providing information at night, the providing unit prioritizes displaying information about stores that are open 24 hours a day. The providing unit can acquire information about the time of day, weather, the user's location information, and the like to take the user's current situation into consideration. For example, the providing unit can prioritize displaying information about stores that are open based on the user's current time of day. Furthermore, when it rains, the providing unit can prioritize displaying information about stores that are open. Furthermore, when providing information on holidays, the providing unit can prioritize displaying information about stores that are open. In this way, the providing unit can provide appropriate information to the user by filtering information based on the user's current situation.

[0049] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the providing unit can prioritize providing information about stores closest to the user's current location. The providing unit can use GPS data or location information services to acquire the user's geographical location information. For example, the providing unit can prioritize providing area-specific information based on the user's current location. The providing unit can also prioritize providing information about nearby stores based on the user's location information. In this way, the providing unit can improve user convenience by prioritized providing highly relevant information by taking into account the user's geographical location information.

[0050] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit can prioritize providing information about products mentioned by the user on social media. The providing unit can use data mining technology to analyze the user's social media activity. For example, the providing unit can provide information about products recommended by the user's social media followers. The providing unit can also provide information about products that the user may be interested in based on the user's social media activity. In this way, the providing unit can provide information that matches the user's interests by analyzing the user's social media activity.

[0051] When proposing a substitute, the alternative suggestion unit can select an optimal substitute by referring to the user's past purchase history. The alternative suggestion unit can suggest related alternatives based on, for example, products previously purchased by the user. The alternative suggestion unit can use data mining technology to analyze the user's past purchase history. For example, the alternative suggestion unit can prioritize suggesting alternatives to frequently purchased products based on the user's past purchase history. The alternative suggestion unit can also analyze the user's purchasing patterns and suggest optimal alternatives. For example, the alternative suggestion unit can evaluate the relevance of products previously purchased by the user and suggest the most relevant alternative. In this way, the alternative suggestion unit can improve user convenience by selecting optimal alternatives based on the user's past purchase history.

[0052] When proposing an alternative product, the alternative suggestion unit can suggest an appropriate alternative product by taking into account the user's geographical location information. For example, the alternative suggestion unit can preferentially suggest alternative products from stores closest to the user's current location. The alternative suggestion unit can use GPS data or location information services to acquire the user's geographical location information. For example, the alternative suggestion unit can preferentially suggest regional alternative products based on the user's current location. The alternative suggestion unit can also preferentially suggest alternative products from nearby stores based on the user's location information. In this way, the alternative suggestion unit can improve user convenience by proposing the optimal alternative product by taking into account the user's geographical location information.

[0053] When making a prediction, the prediction unit can apply an optimal prediction algorithm by referring to the user's past purchase history. The prediction unit, for example, predicts the user's next purchase based on products previously purchased by the user. The prediction unit can use data mining technology to analyze the user's past purchase history. For example, the prediction unit can predict the next purchase of frequently purchased products based on the user's past purchase history. The prediction unit can also analyze the user's purchasing patterns and apply an optimal prediction algorithm. For example, the prediction unit can evaluate the relevance of products previously purchased by the user and apply the most relevant prediction algorithm. This allows the prediction unit to apply an optimal prediction algorithm based on the user's past purchase history, thereby improving user convenience.

[0054] The prediction unit can make an appropriate prediction by taking into account the user's geographical location information. For example, the prediction unit prioritizes prediction of the store closest to the user's current location. The prediction unit can use GPS data or location information services to acquire the user's geographical location information. For example, the prediction unit can prioritize prediction of a specific area based on the user's current location. The prediction unit can also prioritize prediction of nearby stores based on the user's location information. This allows the prediction unit to make an optimal prediction by taking into account the user's geographical location information, thereby improving user convenience.

[0055] The voice accepting unit can apply an optimal recognition algorithm by referring to the user's past voice input history when inputting voice. For example, the voice accepting unit applies the optimal recognition algorithm based on the voice input used by the user in the past. The voice accepting unit can use data mining technology to analyze the user's past voice input history. For example, the voice accepting unit can prioritize recognition of frequently used voice inputs from the user's past voice input history. The voice accepting unit can also analyze the user's voice input pattern and apply the optimal recognition algorithm. For example, the voice accepting unit can evaluate the relevance of the voice input used by the user in the past and apply the most relevant recognition algorithm. As a result, the voice accepting unit can improve the accuracy of the voice input by applying the optimal recognition algorithm based on the user's past voice input history.

[0056] The voice reception unit can perform appropriate recognition by taking into account the user's geographical location information when receiving voice input. For example, the voice reception unit recognizes the voice input based on information about the store closest to the user's current location. The voice reception unit can use GPS data or location information services to obtain the user's geographical location information. For example, the voice reception unit can prioritize recognition of region-specific voice input based on the user's current location. The voice reception unit can also prioritize recognition of voice input from nearby stores based on the user's location information. This allows the voice reception unit to perform optimal recognition by taking into account the user's geographical location information, thereby improving the accuracy of the voice input.

[0057] When displaying a map, the map display unit can select the optimal display method by referring to the user's past map usage history. For example, the map display unit selects the optimal display method based on map display methods used by the user in the past. The map display unit can use data mining technology to analyze the user's past map usage history. For example, the map display unit can prioritize and provide frequently used display methods based on the user's past map usage history. The map display unit can also analyze the user's map usage patterns and select the optimal display method. For example, the map display unit can evaluate the relevance of map display methods used by the user in the past and select the most relevant display method. In this way, the map display unit can improve the accuracy of map display by selecting the optimal display method based on the user's past map usage history.

[0058] The map display unit can provide an appropriate display when displaying a map, taking into consideration the user's geographical location information. For example, the map display unit can prioritize displaying map information of a store closest to the user's current location. The map display unit can use GPS data or location information services to obtain the user's geographical location information. For example, the map display unit can prioritize displaying area-specific map information based on the user's current location. The map display unit can also prioritize displaying map information of nearby stores based on the user's location information. This allows the map display unit to provide an optimal display taking into consideration the user's geographical location information, thereby improving the accuracy of the map display.

[0059] The Wi-Fi connection unit may select an optimal connection method by referring to the user's past connection history when connecting to Wi-Fi. For example, the Wi-Fi connection unit may select an optimal connection method based on Wi-Fi networks to which the user has previously connected. The Wi-Fi connection unit may use data mining technology to analyze the user's past connection history. For example, the Wi-Fi connection unit may prioritize connecting to frequently used Wi-Fi networks based on the user's past connection history. The Wi-Fi connection unit may also analyze the user's connection patterns and select an optimal connection method. For example, the Wi-Fi connection unit may evaluate the relevance of Wi-Fi networks to which the user has previously connected and select the most relevant connection method. In this way, the Wi-Fi connection unit may select an optimal connection method based on the user's past connection history, thereby improving the accuracy of Wi-Fi connections.

[0060] The Wi-Fi connection unit can make an appropriate connection when connecting to Wi-Fi, taking into account the user's geographical location information. For example, the Wi-Fi connection unit can preferentially connect to a Wi-Fi network that is closest to the user's current location. The Wi-Fi connection unit can use GPS data or location information services to obtain the user's geographical location information. For example, the Wi-Fi connection unit can preferentially connect to a region-specific Wi-Fi network based on the user's current location. The Wi-Fi connection unit can also preferentially connect to a nearby Wi-Fi network based on the user's location information. In this way, the Wi-Fi connection unit can make an optimal connection taking into account the user's geographical location information, thereby improving the accuracy of the Wi-Fi connection.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The product search AI system can be equipped with a price prediction unit that predicts price fluctuations of specific products based on the user's purchasing history. The price prediction unit analyzes past price data and predicts future price fluctuations. For example, the price prediction unit can learn seasonal price fluctuation patterns and suggest the optimal purchase timing to the user. The price prediction unit can also predict price increases and decreases by taking into account the balance between supply and demand for specific products. Furthermore, the price prediction unit can notify the user when the price of a product in which the user is interested will drop. This allows the user to purchase products at the optimal time and reduce costs.

[0063] The product search AI system can be equipped with a health monitoring unit that monitors the user's health condition and suggests necessary products. The health monitoring unit collects and analyzes the user's health data. For example, the health monitoring unit can collect data such as the user's heart rate, blood pressure, and body temperature, and suggest appropriate medical products if abnormalities are detected. The health monitoring unit can also suggest supplements and foods necessary to maintain health based on the user's diet and exercise data. Furthermore, the health monitoring unit can suggest regular health checks and medical consultations depending on the user's health condition. This allows the user to understand their own health condition and purchase appropriate products.

[0064] The product search AI system can be equipped with a usage suggestion unit that suggests how to use and maintain a specific product based on the user's purchasing history. The usage suggestion unit provides how to use and maintain a product purchased by the user. For example, the usage suggestion unit can provide video or text on how to use and maintain a home appliance. The usage suggestion unit can also monitor the frequency and usage status of use of a product purchased by the user and notify the user of appropriate maintenance times. Furthermore, the usage suggestion unit can suggest related products and accessories for the product purchased by the user. This allows the user to use the purchased product appropriately and for a long period of time.

[0065] The product search AI system can be equipped with a recall notification unit that notifies users of recall information for specific products based on their purchasing history. The recall notification unit collects and notifies users of recall information for products they have purchased. For example, the recall notification unit can collect recall information from manufacturers and notify users of the information. The recall notification unit can also check whether a product purchased by a user is subject to a recall and suggest necessary measures. Furthermore, the recall notification unit can support the exchange and refund procedures for recalled products. This allows users to quickly understand recall information and take appropriate action.

[0066] The product search AI system can include an eco-suggestion unit that suggests eco-friendly alternatives to specific products based on the user's purchasing history. The eco-suggestion unit collects information on environmentally friendly products and suggests them to the user. For example, the eco-suggestion unit can suggest eco-friendly alternatives to products purchased by the user. The eco-suggestion unit can also evaluate the environmental impact of products in which the user is interested and provide eco-friendly options. Furthermore, the eco-suggestion unit can display the environmental contribution that the user can make by purchasing eco-friendly products. This allows the user to select environmentally friendly products and promote sustainable consumption.

[0067] The product search AI system may include a customization suggestion unit that suggests customization options for a specific product based on the user's purchasing history. The customization suggestion unit provides customization options for products purchased by the user. For example, the customization suggestion unit may suggest customization options for furniture purchased by the user. The customization suggestion unit may also suggest customization options for clothing purchased by the user. Furthermore, the customization suggestion unit may suggest accessories or parts required to customize the product purchased by the user. This allows the user to customize the product to suit their preferences and improve their satisfaction.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The search unit searches for products. The user simply inputs the product name or category, and the search unit searches for related products based on that input. For example, if the user inputs "cold medicine," the search unit searches for products related to cold medicine. Step 2: The acquisition unit acquires inventory information based on the products searched by the search unit. The acquisition unit acquires inventory information from nearby affiliated stores based on the user's current location. For example, the acquisition unit accesses a database of affiliated stores and prioritizes acquiring inventory information from the nearest store. Step 3: The suggestion unit suggests the most suitable store based on the inventory information acquired by the acquisition unit. If the product searched for by the user is available at multiple stores, the suggestion unit preferentially displays the nearest store or the store with the most inventory. The suggestion unit suggests the most suitable store, taking into consideration the user's convenience. Step 4: The providing unit provides detailed information about the store suggested by the suggestion unit. The providing unit displays information such as the store's business hours, address, and phone number, allowing the user to check the necessary information before going to the store.

[0070] (Example 2) A product search AI system according to an embodiment of the present invention solves the problem of unexpectedly out-of-stock items when purchasing daily necessities or medical supplies. This product search AI system works in conjunction with affiliated stores, allowing users to check inventory and availability information from an app. First, a user opens the app and searches for a desired product. For example, they search for medicine or daily necessities that suddenly become necessary while traveling. To do this, the user simply enters the product name and category. The app retrieves inventory information from nearby affiliated stores based on the user's current location. Next, the app analyzes the retrieved inventory information and suggests the most suitable store for the user. For example, if the product searched for by the user is available at multiple stores, the app prioritizes displaying the nearest store or the store with the largest inventory. This allows users to purchase the product efficiently. Furthermore, the app provides detailed information about the suggested store, such as store hours, address, and phone number. This allows users to check necessary information before going to the store. This mechanism allows users to efficiently purchase products they suddenly need. This is particularly useful when making impromptu shopping trips, such as when traveling, as it allows users to obtain products without wasting time. In addition, by linking with partner stores, cashless payments can be made smoothly. For example, if a user suddenly needs cold medicine while traveling, they open the app and search for "cold medicine." The app will obtain inventory information from nearby partner stores based on the user's current location and suggest the nearest store. The user can check the details of the suggested store and visit the store during business hours to purchase the product. In this way, the product search AI system solves the user's concerns when making sudden shopping trips and supports efficient product purchases. This allows the product search AI system to efficiently purchase products that users suddenly need.

[0071] A product search AI system according to an embodiment includes a search unit, an acquisition unit, a suggestion unit, and a providing unit. The search unit allows a user to search for a product. When searching for a product, the user simply inputs, for example, the product name or category. The search unit searches for related products based on the user's input. For example, if the user inputs "cold medicine," the search unit can search for products related to cold medicine. The acquisition unit acquires inventory information based on the product searched by the search unit. The acquisition unit, for example, acquires inventory information of nearby affiliated stores based on the user's current location. The acquisition unit can access a database of affiliated stores to acquire inventory information. For example, the acquisition unit can preferentially acquire inventory information of the nearest store based on the user's current location. The suggestion unit suggests the most appropriate store based on the inventory information acquired by the acquisition unit. For example, if the product searched for by the user is sold at multiple stores, the suggestion unit preferentially displays the nearest store or a store with a large inventory. The suggestion unit can suggest the most appropriate store taking user convenience into consideration. The providing unit provides detailed information about the store suggested by the suggestion unit. The providing unit displays information such as store opening hours, address, and telephone number. The providing unit allows the user to check the necessary information before going to the store. As a result, the product search AI system according to the embodiment allows the user to efficiently search for products, obtain inventory information, suggest the most suitable store, and provide detailed information.

[0072] The product search AI system includes an alternative suggestion unit that suggests alternative products. The alternative suggestion unit suggests alternative products when the product is out of stock. For example, if the product searched for by the user is out of stock, the alternative suggestion unit can suggest similar products. The alternative suggestion unit selects alternative products based on criteria such as price, quality, and brand. For example, the alternative suggestion unit can suggest alternative products with a similar price. The alternative suggestion unit can also suggest alternative products with the same or higher quality. Furthermore, the alternative suggestion unit can suggest alternative products of a brand preferred by the user. In this way, the alternative suggestion unit can meet the user's needs by suggesting alternative products when the product is out of stock.

[0073] The product search AI system includes a prediction unit that predicts the next purchase based on the user's purchase history. The prediction unit predicts the next purchase based on the user's purchase history. The prediction unit can, for example, analyze products that the user has purchased in the past and predict the next purchase. The prediction unit makes predictions based on data such as purchase date and time, purchased products, and purchase frequency. For example, the prediction unit can predict products that the user will purchase regularly. The prediction unit can also analyze the user's purchasing patterns and predict the next purchase. Furthermore, the prediction unit can predict the time of the next purchase based on the user's purchase history. In this way, the prediction unit improves user convenience by predicting the next purchase based on the user's purchase history.

[0074] The product search AI system includes a voice reception unit that receives voice input. The voice reception unit receives user voice input. The voice reception unit is used, for example, when a user searches for products by voice. The voice reception unit can convert the user's voice into text using voice recognition technology. For example, when a user says, "Looking for cold medicine," the voice reception unit can convert the voice into text and send it to the search unit. The voice reception unit has a function to learn the voice characteristics of specific speakers to improve the accuracy of voice input. For example, the voice reception unit accumulates user voice data and learns the voice characteristics of individual users to improve the accuracy of voice recognition. In this way, the voice reception unit enables users to easily search for products.

[0075] The product search AI system includes a map display unit that displays search results on a map. The map display unit displays the search results on the map. For example, the map display unit can display stores that sell the product searched for by the user on the map. The map display unit displays nearby stores on the map based on the user's current location. For example, the map display unit can display the store closest to the user's current location on the map. The map display unit can also display a store icon on the map based on the store's location information. Furthermore, the map display unit can display detailed information about the store on the map. For example, the map display unit displays information such as the store's business hours, address, and phone number on the map. This allows the map display unit to intuitively grasp the location of the store.

[0076] The product search AI system includes a Wi-Fi connection unit that automatically connects to the store's Wi-Fi. The Wi-Fi connection unit automatically connects to the store's Wi-Fi. For example, the Wi-Fi connection unit can automatically connect to the store's Wi-Fi when a user arrives at the store. The Wi-Fi connection unit can set the connection timing and priority. For example, the Wi-Fi connection unit can automatically connect to the Wi-Fi when a user approaches the store. The Wi-Fi connection unit can also select the optimal Wi-Fi network taking into account connection stability and speed. Furthermore, the Wi-Fi connection unit can use encryption technology to ensure connection security. This allows the Wi-Fi connection unit to smoothly use the Internet for users.

[0077] The search unit can estimate a user's emotions and adjust the display method of search results based on the estimated user's emotions. For example, if the user is feeling stressed, the search unit displays simple, highly visible search results. The search unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the search unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The search unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is relaxed, the search unit can display search results containing more information. For example, if the user is relaxed, the search unit can display detailed product information and reviews. Furthermore, if the user is in a hurry, the search unit can prioritize displaying the most relevant search results. For example, if the user is in a hurry, the search unit can prioritize displaying product inventory information and information about the nearest store. In this way, the search unit can improve user satisfaction by adjusting the display method of search results according to the user's emotions.

[0078] The search unit can analyze the user's past search history and automatically complete appropriate search keywords. For example, the search unit can automatically complete related keywords based on product names searched for by the user in the past. The search unit can use data mining technology to analyze the user's past search history. For example, the search unit can prioritize displaying frequently searched keywords from the user's past search history. The search unit can also analyze the user's search patterns and suggest optimal keywords. For example, the search unit can evaluate the relevance of keywords searched for by the user in the past and automatically complete the most relevant keywords. This allows the search unit to improve search efficiency by automatically completing optimal search keywords based on the user's past search history.

[0079] The search unit can filter search results based on the user's current situation when searching. For example, when searching at night, the search unit prioritizes displaying stores that are open 24 hours a day. The search unit can acquire information such as the time of day, weather, and the user's location to take the user's current situation into consideration. For example, the search unit can prioritize displaying stores that are open based on the user's current time of day. In addition, the search unit can prioritize displaying indoor stores when it is raining. Furthermore, when searching on a holiday, the search unit can prioritize displaying stores that are open. In this way, the search unit can provide more appropriate search results by filtering search results based on the user's current situation.

[0080] The search unit can estimate a user's emotions and prioritize search results based on the estimated user emotions. For example, if the user is feeling stressed, the search unit can prioritize displaying the most relevant search results. The search unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the search unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The search unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is relaxed, the search unit can prioritize displaying search results containing more detailed information. For example, if the user is relaxed, the search unit can display detailed product information and reviews. Furthermore, if the user is in a hurry, the search unit can prioritize displaying search results that can be accessed quickly. For example, if the user is in a hurry, the search unit can prioritize displaying product inventory information and information about the nearest store. In this way, the search unit can prioritize search results according to the user's emotions, thereby providing search results that meet the user's needs.

[0081] During a search, the search unit can prioritize displaying highly relevant products by taking into account the user's geographical location information. For example, the search unit can prioritize displaying products from stores closest to the user's current location. The search unit can use GPS data or location information services to acquire the user's geographical location information. For example, the search unit can prioritize displaying products that are only available in a specific region based on the user's current location. The search unit can also prioritize displaying products that are available in nearby stores based on the user's location information. This allows the search unit to prioritize displaying highly relevant products by taking into account the user's geographical location information, thereby improving user convenience.

[0082] The search unit can analyze the user's social media activity during a search and display related products. For example, the search unit can prioritize displaying products that the user has mentioned on social media. The search unit can use data mining technology to analyze the user's social media activity. For example, the search unit can display products recommended by the user's social media followers. The search unit can also suggest products that the user might be interested in based on the user's social media activity. In this way, the search unit can display products that match the user's interests by analyzing the user's social media activity.

[0083] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring inventory information based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit quickly acquires inventory information. The acquisition unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the acquisition unit can acquire detailed inventory information when the user is relaxed. For example, the acquisition unit can acquire detailed inventory information for products when the user is relaxed. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the most relevant inventory information. For example, when the user is in a hurry, the acquisition unit can quickly acquire inventory information for products. In this way, the acquisition unit can adjust the timing of acquiring inventory information according to the user's emotions, thereby providing information that meets the user's needs.

[0084] The acquisition unit can analyze the user's past purchase history and select an appropriate acquisition method. The acquisition unit can acquire related inventory information based on, for example, products purchased by the user in the past. The acquisition unit can use data mining technology to analyze the user's past purchase history. For example, the acquisition unit can prioritize acquisition of inventory information for frequently purchased products based on the user's past purchase history. The acquisition unit can also analyze the user's purchasing patterns and acquire optimal inventory information. For example, the acquisition unit can evaluate the relevance of products purchased by the user in the past and acquire the most relevant inventory information. This allows the acquisition unit to efficiently acquire inventory information by selecting an optimal acquisition method based on the user's past purchase history.

[0085] When acquiring inventory information, the acquisition unit can filter the inventory information based on the user's current living situation and areas of interest. For example, if the user is interested in health, the acquisition unit can prioritize acquiring inventory information for health-related products. The acquisition unit can use the user's profile information to take the user's current living situation and areas of interest into consideration. For example, if the user is traveling, the acquisition unit can prioritize acquiring inventory information for products needed at the travel destination. The acquisition unit can also filter related inventory information based on the user's current living situation. For example, the acquisition unit can acquire highly relevant inventory information based on information such as the user's family composition, occupation, and hobbies. As a result, the acquisition unit can provide highly relevant information by filtering inventory information based on the user's current living situation and areas of interest.

[0086] The acquisition unit can estimate the user's emotions and determine the priority of inventory information to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring the most relevant inventory information. The acquisition unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the acquisition unit can prioritize acquiring detailed inventory information when the user is relaxed. For example, the acquisition unit can prioritize acquiring detailed inventory information of products when the user is relaxed. Furthermore, the acquisition unit can prioritize acquiring inventory information that can be acquired quickly when the user is in a hurry. For example, the acquisition unit can quickly acquire inventory information of products when the user is in a hurry. In this way, the acquisition unit can prioritize inventory information according to the user's emotions, thereby providing information that meets the user's needs.

[0087] When acquiring inventory information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring inventory information from a store closest to the user's current location. The acquisition unit can use GPS data or location information services to acquire the user's geographical location information. For example, the acquisition unit can prioritize acquiring area-specific inventory information based on the user's current location. The acquisition unit can also prioritize acquiring inventory information from nearby stores based on the user's location information. In this way, the acquisition unit can improve user convenience by prioritizing acquisition of highly relevant information by taking into account the user's geographical location information.

[0088] When acquiring inventory information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit can prioritize acquiring inventory information for products mentioned by the user on social media. The acquisition unit can use data mining technology to analyze the user's social media activity. For example, the acquisition unit can acquire inventory information for products recommended by the user's social media followers. The acquisition unit can also acquire inventory information for products that the user may be interested in from the user's social media activity. In this way, the acquisition unit can acquire inventory information that matches the user's interests by analyzing the user's social media activity.

[0089] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. The suggestion unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions including detailed information. For example, if the user is relaxed, the suggestion unit can display detailed product information and reviews. Furthermore, if the user is in a hurry, the suggestion unit can prioritize the most relevant suggestions. For example, if the user is in a hurry, the suggestion unit can prioritize displaying product inventory information and information about the nearest store. In this way, the suggestion unit can improve user satisfaction by adjusting the way suggestions are presented based on the user's emotions.

[0090] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product. For example, for products with high importance, the suggestion unit makes a suggestion including detailed information. The suggestion unit can use criteria such as price, demand, and user ratings to evaluate the importance of the product. For example, the suggestion unit can provide detailed information for products with high prices or high demand. The suggestion unit can also provide detailed information for products with high user ratings. Furthermore, the suggestion unit can make a suggestion including concise information for products with low importance. For example, the suggestion unit can provide concise information for products with low prices or low demand. In this way, the suggestion unit can provide appropriate information to the user by adjusting the level of detail of the suggestion according to the importance of the product.

[0091] When making a suggestion, the suggestion unit can apply an appropriate suggestion algorithm depending on the product category. For example, for medical products, the suggestion unit makes a suggestion based on highly reliable information. The suggestion unit can use criteria such as product category or service category to classify product categories. For example, the suggestion unit can apply an appropriate suggestion algorithm based on categories such as medical products, daily necessities, and food. Furthermore, for daily necessities, the suggestion unit can also make a suggestion that emphasizes price and stock status. For example, the suggestion unit can evaluate the price and stock status of daily necessities and make an optimal suggestion. Furthermore, the suggestion unit can make an appropriate suggestion to the user by applying an optimal suggestion algorithm depending on the product category. As a result, the suggestion unit can make an appropriate suggestion to the user by applying an optimal suggestion algorithm depending on the product category.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide short, concise suggestions. The suggestion unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. For example, if the user is relaxed, the suggestion unit can display detailed product information and reviews. Furthermore, if the user is in a hurry, the suggestion unit can provide short suggestions that can be quickly understood. For example, if the user is in a hurry, the suggestion unit can prioritize displaying product inventory information and information about the nearest store. In this way, the suggestion unit can improve user satisfaction by adjusting the length of the suggestions according to the user's emotions.

[0093] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the product. For example, the proposal unit prioritizes proposals for new products. The proposal unit can use criteria such as the product release date or the inventory update date to evaluate the time of submission of the product. For example, the proposal unit can prioritize proposals for products with a new release date. The proposal unit can also prioritize proposals for products on sale. For example, the proposal unit can provide the user with advantageous information by preferentially suggesting products on sale. Furthermore, the proposal unit can provide the user with appropriate information by determining the priority of proposals according to the time of submission of the product. As a result, the proposal unit can provide the user with appropriate information by determining the priority of proposals based on the time of submission of the product.

[0094] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit preferentially suggests products related to a product searched by the user. The suggestion unit can use criteria such as product category, user interests, and past purchase history to evaluate the relevance of the products. For example, the suggestion unit can preferentially suggest products related to a product searched by the user. The suggestion unit can also preferentially suggest highly relevant products based on the user's past purchase history. For example, the suggestion unit can evaluate the relevance of products purchased by the user in the past and suggest the most relevant product. Furthermore, the suggestion unit can provide appropriate information to the user by adjusting the order of suggestions based on the relevance of the products. This allows the suggestion unit to provide appropriate information to the user by adjusting the order of suggestions based on the relevance of the products.

[0095] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit provides a simple, highly visible display method. The providing unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the providing unit can provide a display method including detailed information. For example, when the user is relaxed, the providing unit can display detailed information and reviews of a product. Furthermore, when the user is in a hurry, the providing unit can prioritize displaying the most relevant information. For example, when the user is in a hurry, the providing unit can prioritize displaying product inventory information and information about the nearest store. In this way, the providing unit can improve user satisfaction by adjusting the display method of information according to the user's emotions.

[0096] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit selects the optimal display method based on display methods used by the user in the past. The providing unit can use data mining technology to analyze the user's past operation history. For example, the providing unit can prioritize and provide frequently used display methods based on the user's past operation history. The providing unit can also analyze the user's operation patterns and select the optimal display method. For example, the providing unit can evaluate the relevance of display methods used by the user in the past and select the most relevant display method. In this way, the providing unit can improve user convenience by selecting the optimal display method based on the user's past operation history.

[0097] The providing unit can filter information based on the user's current situation when providing the information. For example, when providing information at night, the providing unit prioritizes displaying information about stores that are open 24 hours a day. The providing unit can acquire information about the time of day, weather, the user's location information, and the like to take the user's current situation into consideration. For example, the providing unit can prioritize displaying information about stores that are open based on the user's current time of day. Furthermore, when it rains, the providing unit can prioritize displaying information about stores that are open. Furthermore, when providing information on holidays, the providing unit can prioritize displaying information about stores that are open. In this way, the providing unit can provide appropriate information to the user by filtering information based on the user's current situation.

[0098] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing the most relevant information. The providing unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can prioritize providing detailed information if the user is relaxed. For example, the providing unit can display detailed information and reviews of a product if the user is relaxed. Furthermore, the providing unit can prioritize providing information that can be accessed quickly if the user is in a hurry. For example, the providing unit can prioritize displaying product inventory information and information about the nearest store if the user is in a hurry. In this way, the providing unit can prioritize information according to the user's emotions and provide appropriate information to the user.

[0099] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, the providing unit can prioritize providing information about stores closest to the user's current location. The providing unit can use GPS data or location information services to acquire the user's geographical location information. For example, the providing unit can prioritize providing area-specific information based on the user's current location. The providing unit can also prioritize providing information about nearby stores based on the user's location information. In this way, the providing unit can improve user convenience by prioritized providing highly relevant information by taking into account the user's geographical location information.

[0100] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit can prioritize providing information about products mentioned by the user on social media. The providing unit can use data mining technology to analyze the user's social media activity. For example, the providing unit can provide information about products recommended by the user's social media followers. The providing unit can also provide information about products that the user may be interested in based on the user's social media activity. In this way, the providing unit can provide information that matches the user's interests by analyzing the user's social media activity.

[0101] The alternative suggestion unit can estimate a user's emotions and adjust the method of suggesting alternatives based on the estimated user emotions. For example, if the user is feeling stressed, the alternative suggestion unit can suggest simple and highly visible alternatives. The alternative suggestion unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the alternative suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The alternative suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, if the user is relaxed, the alternative suggestion unit can suggest alternatives with detailed information. For example, if the user is relaxed, the alternative suggestion unit can display detailed information and reviews about the product. Furthermore, if the user is in a hurry, the alternative suggestion unit can prioritize suggesting the most relevant alternatives. For example, if the user is in a hurry, the alternative suggestion unit can prioritize displaying product inventory information and information about the nearest store. In this way, the alternative suggestion unit can improve user satisfaction by adjusting the method of suggesting alternatives according to the user's emotions.

[0102] When proposing a substitute, the alternative suggestion unit can select an optimal substitute by referring to the user's past purchase history. The alternative suggestion unit can suggest related alternatives based on, for example, products previously purchased by the user. The alternative suggestion unit can use data mining technology to analyze the user's past purchase history. For example, the alternative suggestion unit can prioritize suggesting alternatives to frequently purchased products based on the user's past purchase history. The alternative suggestion unit can also analyze the user's purchasing patterns and suggest optimal alternatives. For example, the alternative suggestion unit can evaluate the relevance of products previously purchased by the user and suggest the most relevant alternative. In this way, the alternative suggestion unit can improve user convenience by selecting optimal alternatives based on the user's past purchase history.

[0103] The alternative suggestion unit can estimate a user's emotions and prioritize alternatives based on the estimated user emotions. For example, when a user is feeling stressed, the alternative suggestion unit prioritizes suggesting the most relevant alternatives. The alternative suggestion unit can use technologies such as facial expression recognition and voice analysis to estimate a user's emotions. For example, the alternative suggestion unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The alternative suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, when a user is relaxed, the alternative suggestion unit can prioritize suggesting alternatives that include detailed information. For example, when a user is relaxed, the alternative suggestion unit can display detailed information and reviews about a product. Furthermore, when a user is in a hurry, the alternative suggestion unit can prioritize suggesting alternatives that can be obtained quickly. For example, when a user is in a hurry, the alternative suggestion unit can prioritize displaying product inventory information and information about the nearest store. In this way, the alternative suggestion unit can improve user satisfaction by prioritizing alternatives according to the user's emotions.

[0104] When proposing an alternative product, the alternative suggestion unit can suggest an appropriate alternative product by taking into account the user's geographical location information. For example, the alternative suggestion unit can preferentially suggest alternative products from stores closest to the user's current location. The alternative suggestion unit can use GPS data or location information services to acquire the user's geographical location information. For example, the alternative suggestion unit can preferentially suggest regional alternative products based on the user's current location. The alternative suggestion unit can also preferentially suggest alternative products from nearby stores based on the user's location information. In this way, the alternative suggestion unit can improve user convenience by proposing the optimal alternative product by taking into account the user's geographical location information.

[0105] The prediction unit can estimate the user's emotions and adjust the next purchase prediction based on the estimated user emotions. For example, if the user is feeling stressed, the prediction unit can quickly make a next purchase prediction. The prediction unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the prediction unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The prediction unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is relaxed, the prediction unit can make a next purchase prediction with detailed information. For example, if the user is relaxed, the prediction unit can display detailed product information and reviews. Furthermore, if the user is in a hurry, the prediction unit can prioritize the most relevant next purchase prediction. For example, if the user is in a hurry, the prediction unit can prioritize displaying product inventory information and information about the nearest store. In this way, the prediction unit can adjust the next purchase prediction according to the user's emotions, thereby improving user satisfaction.

[0106] When making a prediction, the prediction unit can apply an optimal prediction algorithm by referring to the user's past purchase history. The prediction unit, for example, predicts the user's next purchase based on products previously purchased by the user. The prediction unit can use data mining technology to analyze the user's past purchase history. For example, the prediction unit can predict the next purchase of frequently purchased products based on the user's past purchase history. The prediction unit can also analyze the user's purchasing patterns and apply an optimal prediction algorithm. For example, the prediction unit can evaluate the relevance of products previously purchased by the user and apply the most relevant prediction algorithm. This allows the prediction unit to apply an optimal prediction algorithm based on the user's past purchase history, thereby improving user convenience.

[0107] The prediction unit can estimate the user's emotions and prioritize predictions based on the estimated user emotions. For example, if the user is feeling stressed, the prediction unit prioritizes the most relevant prediction. The prediction unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the prediction unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The prediction unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is relaxed, the prediction unit can prioritize predictions that include detailed information. For example, if the user is relaxed, the prediction unit can display detailed product information and reviews. Furthermore, if the user is in a hurry, the prediction unit can prioritize predictions that can be obtained quickly. For example, if the user is in a hurry, the prediction unit can prioritize displaying product inventory information and information about the nearest store. In this way, the prediction unit can improve user satisfaction by prioritizing predictions according to the user's emotions.

[0108] The prediction unit can make an appropriate prediction by taking into account the user's geographical location information. For example, the prediction unit prioritizes prediction of the store closest to the user's current location. The prediction unit can use GPS data or location information services to acquire the user's geographical location information. For example, the prediction unit can prioritize prediction of a specific area based on the user's current location. The prediction unit can also prioritize prediction of nearby stores based on the user's location information. This allows the prediction unit to make an optimal prediction by taking into account the user's geographical location information, thereby improving user convenience.

[0109] The voice receiving unit can estimate the user's emotions and adjust the recognition accuracy of the voice input based on the estimated user emotions. For example, when the user is feeling stressed, the voice receiving unit can accept the voice input with increased recognition accuracy. The voice receiving unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the voice receiving unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The voice receiving unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the voice receiving unit can accept detailed voice input when the user is relaxed. For example, the voice receiving unit can display detailed product information and reviews when the user is relaxed. Furthermore, the voice receiving unit can prioritize accepting voice input that can be recognized quickly when the user is in a hurry. For example, when the user is in a hurry, the voice receiving unit can prioritize displaying product inventory information and information about the nearest store. In this way, the voice receiving unit can improve user satisfaction by adjusting the recognition accuracy of the voice input according to the user's emotions.

[0110] The voice accepting unit can apply an optimal recognition algorithm by referring to the user's past voice input history when inputting voice. For example, the voice accepting unit applies the optimal recognition algorithm based on the voice input used by the user in the past. The voice accepting unit can use data mining technology to analyze the user's past voice input history. For example, the voice accepting unit can prioritize recognition of frequently used voice inputs from the user's past voice input history. The voice accepting unit can also analyze the user's voice input pattern and apply the optimal recognition algorithm. For example, the voice accepting unit can evaluate the relevance of the voice input used by the user in the past and apply the most relevant recognition algorithm. As a result, the voice accepting unit can improve the accuracy of the voice input by applying the optimal recognition algorithm based on the user's past voice input history.

[0111] The voice receiving unit can estimate the user's emotions and prioritize voice inputs based on the estimated user emotions. For example, when the user is feeling stressed, the voice receiving unit prioritizes recognition of the most relevant voice input. The voice receiving unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the voice receiving unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The voice receiving unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the voice receiving unit can prioritize recognition of detailed voice inputs. For example, when the user is relaxed, the voice receiving unit can display detailed product information and reviews. Furthermore, when the user is in a hurry, the voice receiving unit can prioritize recognition of voice inputs that can be recognized quickly. For example, when the user is in a hurry, the voice receiving unit can prioritize displaying product inventory information and information about the nearest store. In this way, the voice receiving unit can prioritize recognition of voice inputs according to the user's emotions, thereby improving user satisfaction.

[0112] The voice reception unit can perform appropriate recognition by taking into account the user's geographical location information when receiving voice input. For example, the voice reception unit recognizes the voice input based on information about the store closest to the user's current location. The voice reception unit can use GPS data or location information services to obtain the user's geographical location information. For example, the voice reception unit can prioritize recognition of region-specific voice input based on the user's current location. The voice reception unit can also prioritize recognition of voice input from nearby stores based on the user's location information. This allows the voice reception unit to perform optimal recognition by taking into account the user's geographical location information, thereby improving the accuracy of the voice input.

[0113] The map display unit can estimate the user's emotions and adjust the map display method based on the estimated user's emotions. For example, when the user is feeling stressed, the map display unit provides a simple, highly visible map display. The map display unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the map display unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The map display unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the map display unit can provide a map display including detailed information. For example, when the user is relaxed, the map display unit can display detailed product information and reviews. Furthermore, when the user is in a hurry, the map display unit can prioritize displaying the most relevant information. For example, when the user is in a hurry, the map display unit can prioritize displaying product inventory information and information about the nearest store. In this way, the map display unit can improve user satisfaction by adjusting the map display method according to the user's emotions.

[0114] When displaying a map, the map display unit can select the optimal display method by referring to the user's past map usage history. For example, the map display unit selects the optimal display method based on map display methods used by the user in the past. The map display unit can use data mining technology to analyze the user's past map usage history. For example, the map display unit can prioritize and provide frequently used display methods based on the user's past map usage history. The map display unit can also analyze the user's map usage patterns and select the optimal display method. For example, the map display unit can evaluate the relevance of map display methods used by the user in the past and select the most relevant display method. In this way, the map display unit can improve the accuracy of map display by selecting the optimal display method based on the user's past map usage history.

[0115] The map display unit can estimate the user's emotions and determine a map display priority based on the estimated user's emotions. For example, when the user is feeling stressed, the map display unit prioritizes displaying the most relevant map information. The map display unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the map display unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The map display unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, when the user is relaxed, the map display unit can prioritize displaying detailed map information. For example, when the user is relaxed, the map display unit can display detailed product information and reviews. Furthermore, when the user is in a hurry, the map display unit can prioritize displaying map information that can be accessed quickly. For example, when the user is in a hurry, the map display unit can prioritize displaying product inventory information and information about the nearest store. In this way, the map display unit can improve user satisfaction by determining the map display priority based on the user's emotions.

[0116] The map display unit can provide an appropriate display when displaying a map, taking into consideration the user's geographical location information. For example, the map display unit can prioritize displaying map information of a store closest to the user's current location. The map display unit can use GPS data or location information services to obtain the user's geographical location information. For example, the map display unit can prioritize displaying area-specific map information based on the user's current location. The map display unit can also prioritize displaying map information of nearby stores based on the user's location information. This allows the map display unit to provide an optimal display taking into consideration the user's geographical location information, thereby improving the accuracy of the map display.

[0117] The Wi-Fi connection unit can estimate a user's emotions and adjust the timing of Wi-Fi connection based on the estimated user's emotions. For example, the Wi-Fi connection unit can quickly connect to Wi-Fi when the user is feeling stressed. The Wi-Fi connection unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the Wi-Fi connection unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The Wi-Fi connection unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the Wi-Fi connection unit can provide detailed connection information when the user is relaxed. For example, the Wi-Fi connection unit can provide information about connection stability and speed when the user is relaxed. Furthermore, the Wi-Fi connection unit can prioritize the most relevant Wi-Fi connection when the user is in a hurry. For example, the Wi-Fi connection unit can prioritize a Wi-Fi network that can be connected quickly when the user is in a hurry. This allows the Wi-Fi connection unit to adjust the timing of the Wi-Fi connection according to the user's emotions, thereby improving the user's satisfaction.

[0118] The Wi-Fi connection unit may select an optimal connection method by referring to the user's past connection history when connecting to Wi-Fi. For example, the Wi-Fi connection unit may select an optimal connection method based on Wi-Fi networks to which the user has previously connected. The Wi-Fi connection unit may use data mining technology to analyze the user's past connection history. For example, the Wi-Fi connection unit may prioritize connecting to frequently used Wi-Fi networks based on the user's past connection history. The Wi-Fi connection unit may also analyze the user's connection patterns and select an optimal connection method. For example, the Wi-Fi connection unit may evaluate the relevance of Wi-Fi networks to which the user has previously connected and select the most relevant connection method. In this way, the Wi-Fi connection unit may select an optimal connection method based on the user's past connection history, thereby improving the accuracy of Wi-Fi connections.

[0119] The Wi-Fi connection unit may estimate a user's emotions and prioritize Wi-Fi connections based on the estimated user emotions. For example, if the user is feeling stressed, the Wi-Fi connection unit may prioritize the most relevant Wi-Fi connection. The Wi-Fi connection unit may use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, the Wi-Fi connection unit may capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The Wi-Fi connection unit may also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the Wi-Fi connection unit may provide detailed connection information when the user is relaxed. For example, the Wi-Fi connection unit may provide information about connection stability and speed when the user is relaxed. Furthermore, the Wi-Fi connection unit may prioritize a Wi-Fi network that can be connected quickly when the user is in a hurry. For example, the Wi-Fi connection unit may prioritize a Wi-Fi network that can be connected quickly when the user is in a hurry. This allows the Wi-Fi connection unit to determine the priority of Wi-Fi connections according to the user's emotions, thereby improving user satisfaction.

[0120] The Wi-Fi connection unit can make an appropriate connection when connecting to Wi-Fi, taking into account the user's geographical location information. For example, the Wi-Fi connection unit can preferentially connect to a Wi-Fi network that is closest to the user's current location. The Wi-Fi connection unit can use GPS data or location information services to obtain the user's geographical location information. For example, the Wi-Fi connection unit can preferentially connect to a region-specific Wi-Fi network based on the user's current location. The Wi-Fi connection unit can also preferentially connect to a nearby Wi-Fi network based on the user's location information. In this way, the Wi-Fi connection unit can make an optimal connection taking into account the user's geographical location information, thereby improving the accuracy of the Wi-Fi connection. === Hard Collateral 1-1 === Each of the multiple elements, including the search unit, acquisition unit, suggestion unit, provision unit, alternative suggestion unit, prediction unit, voice reception unit, map display unit, and Wi-Fi connection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the smart device 14, and the user searches for a product by inputting the product name or category. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires inventory information of nearby affiliated stores based on the user's current location. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes the most suitable store based on the acquired inventory information. The provision unit is realized by the control unit 46A of the smart device 14, and provides the user with detailed information on the proposed store. The alternative suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an alternative product if the product is out of stock. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the user's next purchase based on the user's purchase history. The voice receiving unit is realized by the control unit 46A of the smart device 14 and receives voice input from the user. The map display unit is realized by the control unit 46A of the smart device 14 and displays search results on a map. The Wi-Fi connection unit is realized by the control unit 46A of the smart device 14 and automatically connects to the store's Wi-Fi. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned search unit, acquisition unit, suggestion unit, provision unit, alternative suggestion unit, prediction unit, voice reception unit, map display unit, and Wi-Fi connection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the smart glasses 214, and the user searches for a product by inputting the product name or category. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires inventory information of nearby affiliated stores based on the user's current location. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests the most suitable store based on the acquired inventory information. The provision unit is realized by the control unit 46A of the smart glasses 214, and provides the user with detailed information on the suggested store. The alternative suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests an alternative product if the product is out of stock. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the user's next purchase based on the user's purchase history. The voice receiving unit is realized by the control unit 46A of the smart glasses 214 and receives voice input from the user. The map display unit is realized by the control unit 46A of the smart glasses 214 and displays search results on a map. The Wi-Fi connection unit is realized by the control unit 46A of the smart glasses 214 and automatically connects to the store's Wi-Fi. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned search unit, acquisition unit, suggestion unit, provision unit, alternative suggestion unit, prediction unit, voice reception unit, map display unit, and Wi-Fi connection unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the headset-type terminal 314, and the user searches for a product by inputting the product name or category. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires inventory information of nearby affiliated stores based on the user's current location. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes the most suitable store based on the acquired inventory information. The provision unit is realized by the control unit 46A of the headset-type terminal 314, and provides the user with detailed information on the proposed store. The alternative suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an alternative product if the product is out of stock. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the user's next purchase based on the user's purchase history. The voice receiving unit is realized by the control unit 46A of the headset terminal 314 and receives voice input from the user. The map display unit is realized by the control unit 46A of the headset terminal 314 and displays search results on a map. The Wi-Fi connection unit is realized by the control unit 46A of the headset terminal 314 and automatically connects to the store's Wi-Fi. === Hard Collateral 1-4 === Each of the multiple elements, including the search unit, acquisition unit, suggestion unit, provision unit, alternative suggestion unit, prediction unit, voice reception unit, map display unit, and Wi-Fi connection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the search unit is realized by the control unit 46A of the robot 414, and the user searches for a product by inputting the product name or category. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and acquires inventory information of nearby affiliated stores based on the user's current location. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes the most suitable store based on the acquired inventory information. The provision unit is realized by the control unit 46A of the robot 414, and provides the user with detailed information on the proposed store. The alternative suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and proposes an alternative product if the product is out of stock. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the user's next purchase based on the user's purchase history. The voice receiving unit is realized by the control unit 46A of the robot 414 and receives voice input from the user. The map display unit is realized by the control unit 46A of the robot 414 and displays search results on a map. The Wi-Fi connection unit is realized by the control unit 46A of the robot 414 and automatically connects to the store's Wi-Fi.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The product search AI system can be equipped with a price prediction unit that predicts price fluctuations of specific products based on the user's purchasing history. The price prediction unit analyzes past price data and predicts future price fluctuations. For example, the price prediction unit can learn seasonal price fluctuation patterns and suggest the optimal purchase timing to the user. The price prediction unit can also predict price increases and decreases by taking into account the balance between supply and demand for specific products. Furthermore, the price prediction unit can notify the user when the price of a product in which the user is interested will drop. This allows the user to purchase products at the optimal time and reduce costs.

[0123] The product search AI system can be equipped with a health monitoring unit that monitors the user's health condition and suggests necessary products. The health monitoring unit collects and analyzes the user's health data. For example, the health monitoring unit can collect data such as the user's heart rate, blood pressure, and body temperature, and suggest appropriate medical products if abnormalities are detected. The health monitoring unit can also suggest supplements and foods necessary to maintain health based on the user's diet and exercise data. Furthermore, the health monitoring unit can suggest regular health checks and medical consultations depending on the user's health condition. This allows the user to understand their own health condition and purchase appropriate products.

[0124] The product search AI system can estimate a user's emotions and adjust the way product reviews are displayed based on the estimated emotions. For example, if a user is feeling stressed, simple, highly visible reviews can be displayed. If a user is relaxed, detailed reviews and ratings can be displayed. Also, if a user is in a hurry, the most relevant reviews can be displayed preferentially. This allows users to view appropriate reviews according to their emotions, making it easier for them to make purchasing decisions.

[0125] The product search AI system can be equipped with a usage suggestion unit that suggests how to use and maintain a specific product based on the user's purchasing history. The usage suggestion unit provides how to use and maintain a product purchased by the user. For example, the usage suggestion unit can provide video or text on how to use and maintain a home appliance. The usage suggestion unit can also monitor the frequency and usage status of use of a product purchased by the user and notify the user of appropriate maintenance times. Furthermore, the usage suggestion unit can suggest related products and accessories for the product purchased by the user. This allows the user to use the purchased product appropriately and for a long period of time.

[0126] The product search AI system can estimate a user's emotions and make personalized product suggestions based on the estimated emotions. For example, if a user is feeling stressed, it can suggest products that have a relaxing effect. If a user is relaxed, it can suggest new or trendy products that pique the user's interest. Also, if a user is in a hurry, it can prioritize suggestions of products that can be purchased quickly. This allows users to receive suggestions of the best products according to their emotions, improving their satisfaction.

[0127] The product search AI system can be equipped with a recall notification unit that notifies users of recall information for specific products based on their purchasing history. The recall notification unit collects and notifies users of recall information for products they have purchased. For example, the recall notification unit can collect recall information from manufacturers and notify users of the information. The recall notification unit can also check whether a product purchased by a user is subject to a recall and suggest necessary measures. Furthermore, the recall notification unit can support the exchange and refund procedures for recalled products. This allows users to quickly understand recall information and take appropriate action.

[0128] The product search AI system can estimate a user's emotions and display personalized advertisements for products based on the estimated emotions. For example, if a user is feeling stressed, advertisements for products with a relaxing effect can be displayed. If a user is feeling relaxed, advertisements for new or trending products that pique the user's interest can be displayed. Furthermore, if a user is in a hurry, advertisements for products that can be purchased quickly can be displayed preferentially. This allows users to see advertisements that are optimal for their emotions, increasing their desire to purchase.

[0129] The product search AI system can include an eco-suggestion unit that suggests eco-friendly alternatives to specific products based on the user's purchasing history. The eco-suggestion unit collects information on environmentally friendly products and suggests them to the user. For example, the eco-suggestion unit can suggest eco-friendly alternatives to products purchased by the user. The eco-suggestion unit can also evaluate the environmental impact of products in which the user is interested and provide eco-friendly options. Furthermore, the eco-suggestion unit can display the environmental contribution that the user can make by purchasing eco-friendly products. This allows the user to select environmentally friendly products and promote sustainable consumption.

[0130] The product search AI system can infer a user's emotions and support the product return process based on the inferred emotions. For example, if a user is feeling stressed, it can guide them through a concise and easy-to-understand return process. If a user is relaxed, it can provide detailed instructions on the return process. If a user is in a hurry, it can suggest a way to complete the return process quickly. This allows users to carry out the appropriate return process according to their emotions, reducing stress.

[0131] The product search AI system may include a customization suggestion unit that suggests customization options for a specific product based on the user's purchasing history. The customization suggestion unit provides customization options for products purchased by the user. For example, the customization suggestion unit may suggest customization options for furniture purchased by the user. The customization suggestion unit may also suggest customization options for clothing purchased by the user. Furthermore, the customization suggestion unit may suggest accessories or parts required to customize the product purchased by the user. This allows the user to customize the product to suit their preferences and improve their satisfaction.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The search unit searches for products. The user simply inputs the product name or category, and the search unit searches for related products based on that input. For example, if the user inputs "cold medicine," the search unit searches for products related to cold medicine. Step 2: The acquisition unit acquires inventory information based on the products searched by the search unit. The acquisition unit acquires inventory information from nearby affiliated stores based on the user's current location. For example, the acquisition unit accesses a database of affiliated stores and prioritizes acquiring inventory information from the nearest store. Step 3: The suggestion unit suggests the most suitable store based on the inventory information acquired by the acquisition unit. If the product searched for by the user is available at multiple stores, the suggestion unit preferentially displays the nearest store or the store with the most inventory. The suggestion unit suggests the most suitable store, taking into consideration the user's convenience. Step 4: The providing unit provides detailed information about the store suggested by the suggestion unit. The providing unit displays information such as the store's business hours, address, and phone number, allowing the user to check the necessary information before going to the store.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a search unit for allowing a user to search for a product; an acquisition unit that acquires inventory information based on the products searched by the search unit; a suggestion unit that suggests an appropriate store based on the inventory information acquired by the acquisition unit; a providing unit that provides detailed information about the store suggested by the suggestion unit; Equipped with A system characterized by:

2. Equipped with an alternative suggestion section that suggests alternative products 2. The system of claim 1.

3. Equipped with a prediction unit that predicts next purchases based on purchase history 2. The system of claim 1.

4. A voice input receiving unit is provided.

2. The system of claim 1.

5. Equipped with a map display section that displays search results on a map 2. The system of claim 1.

6. Equipped with a Wi-Fi connection that automatically connects to the store's Wi-Fi 2. The system of claim 1.

7. The search unit Inferring user sentiment and adjusting how search results are displayed based on the inferred sentiment 2. The system of claim 1.

8. The search unit Analyzes the user's past search history and auto-completes appropriate search keywords 2. The system of claim 1.

Images