system

Abstract

The system according to the embodiment aims to identify the store where the required product can be obtained in the shortest time possible, thereby realizing efficient purchasing. [Solution] A system according to an embodiment includes a reception unit, an acquisition unit, an identification unit, and a provision unit. The reception unit inputs product information. The acquisition unit acquires inventory information based on the product information input by the reception unit. The identification unit identifies an appropriate store based on the inventory information acquired by the acquisition unit. The provision unit provides the information identified by the identification 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, it was difficult to identify the store where the required products could be obtained in the shortest time, and there was a problem that efficient purchasing was not realized.

[0005] The system according to the embodiment aims to identify the store where the required product can be obtained in the shortest time possible, thereby realizing efficient purchasing. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, an identification unit, and a provision unit. The reception unit inputs product information. The acquisition unit acquires inventory information based on the product information input by the reception unit. The identification unit identifies an appropriate store based on the inventory information acquired by the acquisition unit. The provision unit provides the information identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the store where the required product can be obtained in the shortest time, thereby realizing efficient purchasing. [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 system according to an embodiment of the present invention uses a generation AI to instantly identify the store where a desired product can be purchased in the shortest time. When a user inputs the desired product into an app or website, the generation AI connects with each store to check its availability and stock status. The generation AI identifies the closest store to the user's current location and the store with the lowest price, and immediately provides this information to the user. This allows the user to quickly obtain the desired product. For example, a user inputs a specific product name, such as "product," into an app or website. This information is input into the generation AI. The generation AI then analyzes the input product information and connects with each store to check its availability and stock status. The generation AI accesses each store's database to obtain inventory information in real time. For example, it checks whether a specific store has the "product" in stock. Based on the obtained inventory information, the generation AI identifies the closest store to the user's current location and the store with the lowest price. For example, if the closest store to the user's current location is "Store A" and the cheapest store is "Store B," the system provides that information to the user. Based on the information provided by the generative AI, users can select the optimal store and quickly obtain products. For example, by going to "Store A," users can obtain products in the shortest time. Alternatively, by going to "Store B," users can obtain products at the lowest price. This system allows users to obtain the products they need quickly and efficiently. For example, it is extremely convenient in business and daily life because it allows users to obtain urgently needed products on the same day. Furthermore, identifying the cheapest store can reduce costs. In this way, systems using generative AI allow users to quickly obtain the products they need.

[0029] A product identification system according to an embodiment includes a reception unit, an acquisition unit, an identification unit, and a provision unit. The reception unit is a component through which a user inputs necessary product information. For example, the user can input a product name into an app or a website. The reception unit can also analyze the product information input by the user using a generation AI. The acquisition unit accesses each store's database based on the product information input by the reception unit and acquires inventory information in real time. For example, the acquisition unit checks whether a specific store has the "product" in stock. The acquisition unit can also acquire inventory information from each store's database using a generation AI. The identification unit identifies the optimal store based on the inventory information acquired by the acquisition unit. For example, the identification unit identifies the store closest to the user's current location or the store with the lowest price. The identification unit can also identify the optimal store using a generation AI. The provision unit immediately provides the information identified by the identification unit to the user. For example, the provision unit displays information about the store closest to the user or the store with the lowest price. The provision unit can also provide the identified information to the user using a generation AI. As a result, the product identification system according to the embodiment allows users to quickly obtain the products they need. For example, when a user enters the name of a product into an app or website, the generation AI connects with each store to check the product availability and stock status. The generation AI identifies the store closest to the user's current location where the product can be obtained, or the store with the lowest price, and immediately provides this information to the user. This allows users to quickly obtain the products they need. For example, products that are urgently needed can be obtained on the same day, which is extremely convenient in business and daily life. Furthermore, identifying the cheapest store can reduce costs.

[0030] The acquisition unit can access each store's database and obtain inventory information in real time. To obtain inventory information in real time, it is necessary to access each store's database. For example, the acquisition unit checks whether a specific store has a product in stock. The acquisition unit can also use generative AI to obtain inventory information from each store's database. For example, the acquisition unit accesses each store's database and obtains inventory information in real time. To obtain inventory information in real time, it is necessary to consider the database update interval and delay time. For example, the acquisition unit prioritizes obtaining inventory information from stores with short database update intervals. The acquisition unit can also prioritize obtaining inventory information from stores with short delay times. This allows inventory information to be obtained in real time. For example, when a user enters a product name into an app or website, the acquisition unit accesses each store's database and obtains inventory information in real time. This allows users to quickly obtain the products they need. For example, it allows users to obtain urgently needed products on the same day, which is very convenient in business and everyday life.

[0031] The identification unit can identify stores where products can be purchased near the user's current location. The identification unit needs to acquire current location information to identify the store closest to the user's current location. For example, the identification unit acquires the user's current location using GPS information or an IP address. The identification unit can also use a generation AI to identify the store closest to the user's current location. For example, the identification unit acquires location information of each store to identify the store closest to the user's current location. The identification unit can also use a generation AI to analyze the location information of each store and identify the store closest to the user's current location. This allows the user to identify the nearest store. For example, when a user enters a product name into an app or website, the identification unit identifies the store closest to the user's current location. This allows the user to quickly obtain the product they need. For example, this is very convenient in business and everyday life because it allows users to obtain urgently needed products on the same day.

[0032] The identification unit can identify inexpensive stores. In order for a user to identify the cheapest store, the identification unit needs to obtain price information from each store. For example, the identification unit obtains price information from each store and performs price comparisons. The identification unit can also use a generation AI to analyze the price information from each store and identify the cheapest store. For example, the identification unit obtains price information from each store and performs price comparisons taking discount information into account. The identification unit can also use a generation AI to analyze the price information from each store and identify the cheapest store. This allows the user to identify the cheapest store. For example, when a user enters a product name into an app or website, the identification unit obtains price information from each store and identifies the cheapest store. This allows the user to obtain the product they need at the cheapest price. For example, this is very convenient in business and everyday life because it can reduce costs.

[0033] The providing unit can immediately provide the identified information to the user. In order to immediately provide the information identified by the identifying unit to the user, the providing unit needs to adjust the method of providing information. For example, the providing unit displays information about the store closest to the user or the cheapest store. The providing unit can also use a generation AI to provide the identified information to the user. For example, the providing unit immediately displays information about the store closest to the user or the cheapest store. The providing unit can also use a generation AI to immediately provide the identified information to the user. This allows the user to obtain the information immediately. For example, when a user enters a product name into an app or a website, the providing unit immediately provides the user with the information identified by the identifying unit. This allows the user to quickly obtain the product they need. For example, it is possible to obtain a product that is urgently needed on the same day, which is very convenient in business and everyday life.

[0034] The reception unit can analyze the user's past input history and suggest an appropriate input format. The reception unit can use data analysis technology to analyze the user's past input history. For example, the reception unit can automatically display product information that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest product information that will be used in a specific time period based on the user's past input history. This makes it possible to provide an optimal input format based on the user's past input history. For example, automatically displaying product information that the user has frequently input in the past as candidates can reduce the effort required for input. Furthermore, by preferentially suggesting input methods that the user has used in the past, the user can input product information in a manner that the user is familiar with. Furthermore, predicting and suggesting product information that will be used in a specific time period based on the user's past input history can allow the user to quickly input the product information they need. This makes it possible to provide an optimal input format based on the user's past input history.

[0035] The reception unit can automatically complete input candidates based on the user's current purchasing trends when entering product information. The reception unit can use data analysis technology to analyze the user's current purchasing trends. For example, the reception unit can display related products as input candidates based on products recently purchased by the user. The reception unit can also automatically display frequently purchased products as input candidates based on the user's purchasing history. Furthermore, the reception unit can analyze the user's purchasing trends and suggest products that match the season or trend as input candidates. This makes it possible to automatically complete input candidates based on the user's purchasing trends. For example, displaying related products as input candidates based on the user's recently purchased products can reduce the effort required for input. Furthermore, automatically displaying frequently purchased products as input candidates based on the user's purchasing history can allow the user to quickly enter required product information. Furthermore, analyzing the user's purchasing trends and suggesting products that match the season or trend as input candidates can allow the user to quickly enter required product information. This makes it possible to automatically complete input candidates based on the user's purchasing trends.

[0036] The reception unit can present highly relevant product candidates based on the user's geographical location information when the user inputs product information. The reception unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the reception unit can display products sold in nearby stores as input candidates based on the user's current location. The reception unit can also suggest local products as input candidates based on the user's geographical location information. The reception unit can also display products that are popular in a specific region as input candidates based on the user's location information. This makes it possible to present highly relevant product candidates based on the user's geographical location information. For example, displaying products sold in nearby stores as input candidates based on the user's current location can reduce the effort required for input. Furthermore, suggesting local products as input candidates based on the user's geographical location information allows the user to quickly input required product information. Furthermore, displaying products that are popular in a specific region as input candidates based on the user's location information allows the user to quickly input required product information. This makes it possible to present highly relevant product candidates based on the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity when entering product information and suggest related products. The reception unit can use data analysis technology to analyze the user's social media activity. For example, the reception unit can display products that the user has "liked" on social media as input candidates. The reception unit can also suggest products that the user may be interested in based on the content of the user's social media posts. Furthermore, the reception unit can also display related products as input candidates by referring to products purchased by the user's followers. This makes it possible to suggest related products based on the user's social media activity. For example, by displaying products that the user has "liked" on social media as input candidates, it is possible to reduce the effort required for input. Furthermore, by suggesting products that the user may be interested in based on the content of the user's social media posts, it is possible to quickly enter the product information the user needs. Furthermore, by displaying related products as input candidates by referring to products purchased by the user's followers, it is possible to quickly enter the product information the user needs. This makes it possible to suggest related products based on the user's social media activity.

[0038] The acquisition unit can analyze past inventory data for each store to improve the accuracy of inventory information acquisition. The acquisition unit can use data analysis technology to analyze the past inventory data for each store. For example, the acquisition unit can analyze inventory fluctuation patterns based on the past inventory data for each store. The acquisition unit can also predict, from the past inventory data, when a particular product is likely to run out of stock. The acquisition unit can also improve the accuracy of inventory information acquisition by referring to the past inventory data for each store. This can improve the accuracy of inventory information acquisition based on past inventory data. For example, the acquisition unit can improve the accuracy of inventory information acquisition by analyzing inventory fluctuation patterns based on the past inventory data for each store. The acquisition unit can also improve the accuracy of inventory information acquisition by predicting, from the past inventory data, when a particular product is likely to run out of stock. Furthermore, the acquisition unit can improve the accuracy of inventory information acquisition by referring to the past inventory data for each store to improve the accuracy of inventory information acquisition, allowing users to quickly and accurately obtain the products they need. This can improve the accuracy of inventory information acquisition based on past inventory data.

[0039] The acquisition unit can take into account the business status and special sale information of each store when acquiring inventory information. The acquisition unit can use data analysis technology to take into account the business status and special sale information of each store when acquiring inventory information. For example, the acquisition unit checks the business status of each store and prioritizes acquiring inventory information from stores that are open. The acquisition unit can also take into account special sale information and prioritize acquiring products on sale. Furthermore, the acquisition unit can adjust the timing of acquiring inventory information by taking into account the business hours of each store. In this way, inventory information can be acquired by taking into account the business status and special sale information. For example, the acquisition unit checks the business status of each store and prioritizes acquiring inventory information from stores that are open, allowing a user to quickly obtain needed products. In addition, the acquisition unit takes into account special sale information and prioritizes acquiring products on sale, allowing a user to obtain needed products at the lowest price. Furthermore, the acquisition unit adjusts the timing of acquiring inventory information by taking into account the business hours of each store, allowing a user to quickly and accurately obtain needed products. In this way, inventory information can be acquired by taking into account the business status and special sale information.

[0040] When acquiring inventory information, the acquisition unit can prioritize acquiring inventory information from highly relevant stores by taking into account the user's geographical location information. The acquisition unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the acquisition unit prioritizes acquiring inventory information from the nearest store based on the user's current location. The acquisition unit can also prioritize acquiring inventory information for products that are popular in a specific area based on the user's geographical location information. Furthermore, the acquisition unit can also prioritize acquiring inventory information for stores that are easily accessible based on the user's location information. This makes it possible to acquire inventory information from highly relevant stores based on the user's geographical location information. For example, by preferentially acquiring inventory information from the nearest store based on the user's current location, the user can quickly obtain the product he or she needs. Furthermore, by preferentially acquiring inventory information for products that are popular in a specific area based on the user's geographical location information, the user can quickly obtain the product he or she needs. Furthermore, by preferentially acquiring inventory information from stores that are easily accessible based on the user's location information, the user can quickly obtain the product he or she needs. This makes it possible to acquire inventory information from highly relevant stores based on the user's geographical location information.

[0041] When acquiring inventory information, the acquisition unit can analyze the user's social media activity and acquire related inventory information. The acquisition unit can use data analysis technology to analyze the user's social media activity. For example, the acquisition unit acquires inventory information of stores that sell products that the user has "liked" on social media. The acquisition unit can also acquire inventory information of products that the user is likely to be interested in based on the content of the user's social media posts. Furthermore, the acquisition unit can also acquire related inventory information by referring to products purchased by the user's followers. In this way, related inventory information can be acquired based on the user's social media activity. For example, by acquiring inventory information of stores that sell products that the user has "liked" on social media, the user can quickly obtain the product he or she needs. In addition, by acquiring inventory information of products that the user is likely to be interested in based on the content of the user's social media posts, the user can quickly obtain the product he or she needs. Furthermore, by acquiring related inventory information by referring to products purchased by the user's followers, the user can quickly obtain the product he or she needs. In this way, related inventory information can be acquired based on the user's social media activity.

[0042] During the identification, the identification unit can analyze the user's past purchase history to select an appropriate store. The identification unit can use data analysis technology to analyze the user's past purchase history. For example, the identification unit prioritizes identifying stores that the user has frequently used in the past. The identification unit can also select a store that sells a specific product from the user's past purchase history. The identification unit can also analyze the user's purchase history and select a store that provides the highest level of satisfaction. This makes it possible to select an optimal store based on the user's past purchase history. For example, by preferentially identifying stores that the user has frequently used in the past, the user can quickly obtain the product they need. By selecting a store that sells a specific product from the user's past purchase history, the user can quickly obtain the product they need. Furthermore, by analyzing the user's purchase history and selecting a store that provides the highest level of satisfaction, the user can quickly obtain the product they need. This makes it possible to select an optimal store based on the user's past purchase history.

[0043] During the identification process, the identification unit can select an appropriate store by taking into consideration the past ratings and reviews of each store. The identification unit can use data analysis technology to consider the past ratings and reviews of each store. For example, the identification unit selects the store with the highest rating based on the past ratings of each store. The identification unit can also analyze the reviews of each store to select a store that meets the user's needs. The identification unit can also select a highly reliable store by referring to the ratings and reviews of each store. This makes it possible to select the optimal store based on the ratings and reviews of each store. For example, the identification unit can select the store with the highest rating based on the past ratings of each store, allowing the user to quickly obtain the product they need. The identification unit can analyze the reviews of each store and select a store that meets the user's needs, allowing the user to quickly obtain the product they need. The identification unit can select a highly reliable store by referring to the ratings and reviews of each store, allowing the user to quickly obtain the product they need. This makes it possible to select the optimal store based on the ratings and reviews of each store.

[0044] During identification, the identification unit can select an appropriate store by taking into consideration the user's geographical location information. The identification unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the identification unit selects the nearest store based on the user's current location. The identification unit can also select a store that is easily accessible based on the user's geographical location information. Furthermore, the identification unit can select a popular store in a specific area based on the user's location information. This makes it possible to select the optimal store based on the user's geographical location information. For example, the identification unit can select the nearest store based on the user's current location, allowing the user to quickly obtain the product they need. The identification unit can also select a store that is easily accessible based on the user's geographical location information, allowing the user to quickly obtain the product they need. Furthermore, the identification unit can select a popular store in a specific area based on the user's location information, allowing the user to quickly obtain the product they need. This makes it possible to select the optimal store based on the user's geographical location information.

[0045] During the identification, the identification unit can analyze the user's social media activity and identify related stores. The identification unit can use data analysis technology to analyze the user's social media activity. For example, the identification unit prioritizes identifying stores that the user has "liked" on social media. The identification unit can also identify stores that the user is likely to be interested in based on the content of the user's social media posts. Furthermore, the identification unit can also identify related stores by referring to stores that the user's followers have visited. In this way, related stores can be identified based on the user's social media activity. For example, the identification unit prioritizes identifying stores that the user has "liked" on social media, allowing the user to quickly obtain the products they need. Furthermore, the identification unit can identify stores that the user is likely to be interested in based on the content of the user's social media posts, allowing the user to quickly obtain the products they need. Furthermore, the identification unit can identify related stores by referring to stores that the user's followers have visited. In this way, related stores can be identified based on the user's social media activity.

[0046] When providing information, the providing unit can analyze the user's past response history and select the optimal information providing method. The providing unit can use data analysis technology to analyze the user's past response history. For example, the providing unit can prioritize selecting an information providing method that the user previously preferred. The providing unit can also select the most effective information providing method from the user's past response history. Furthermore, the providing unit can analyze the user's past response history and provide information at the optimal timing. This makes it possible to provide the optimal information providing method based on the user's past response history. For example, the providing unit prioritizes selecting an information providing method that the user previously preferred, allowing the user to quickly understand the information. Furthermore, the providing unit selects the most effective information providing method from the user's past response history, allowing the user to understand the information in detail. Furthermore, the providing unit analyzes the user's past response history and provides information at the optimal timing, allowing the user to quickly obtain the information they need. This makes it possible to provide the optimal information providing method based on the user's past response history.

[0047] The providing unit can customize the information display method based on the user's current situation when providing information. The providing unit can use information such as time of day, location, and device status to acquire the user's current situation. For example, when the user is traveling, the providing unit can provide concise and highly visible information. Furthermore, when the user is at home, the providing unit can provide a provision method including detailed information. Furthermore, when the user is in a meeting, the providing unit can reduce notifications and allow the user to check the information later. This makes it possible to provide an information display method according to the user's current situation. For example, when the user is traveling, providing concise and highly visible information allows the user to quickly understand the information. Furthermore, when the user is at home, providing a provision method including detailed information allows the user to understand the information in detail. Furthermore, when the user is in a meeting, providing reduce notifications and allow the user to check the information later allows the user to obtain the information at an appropriate time. This makes it possible to provide an information display method according to the user's current situation.

[0048] When providing information, the providing unit can provide appropriate information by taking into account the user's geographical location information. The providing unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the providing unit can provide nearby store information based on the user's current location. The providing unit can also provide area-specific sales information based on the user's geographical location information. Furthermore, the providing unit can provide information on easily accessible stores based on the user's location information. This makes it possible to provide optimal information based on the user's geographical location information. For example, the providing unit can provide nearby store information based on the user's current location, allowing the user to quickly obtain the information they need. The providing unit can also provide area-specific sales information based on the user's geographical location information. Furthermore, the providing unit can provide information on easily accessible stores based on the user's location information. This makes it possible to provide optimal information based on the user's geographical location information.

[0049] The providing unit can analyze the user's social media activity and provide related information when providing information. The providing unit can use data analysis technology to analyze the user's social media activity. For example, the providing unit can provide information about products that the user has "liked" on social media. The providing unit can also provide information that is likely to be of interest to the user based on the content of the user's social media posts. Furthermore, the providing unit can also provide information about products purchased by the user's followers. In this way, related information can be provided based on the user's social media activity. For example, the providing unit can provide information about products that the user has "liked" on social media, allowing the user to quickly obtain the information he or she needs. In addition, the providing unit can provide information that is likely to be of interest to the user based on the content of the user's social media posts. Furthermore, the providing unit can provide information about products purchased by the user's followers, allowing the user to quickly obtain the information he or she needs. In this way, related information can be provided based on the user's social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can support voice input using voice recognition technology when a user inputs product information. For example, the user can input product names by voice, saving time and effort. The reception unit can also use noise canceling technology to remove ambient noise when a user inputs product information, enabling accurate voice input. Furthermore, the reception unit can analyze the user's pronunciation and accent during voice input and automatically complete appropriate product names. This allows the user to quickly and accurately input product information using voice input. For example, when a user inputs "smartphone," the reception unit can remove ambient noise using noise canceling technology and accurately recognize the word as "smartphone." When a user inputs "smartphone case," the reception unit can analyze the pronunciation and accent and automatically complete appropriate product names. Furthermore, when a user inputs "smartphone accessories," the reception unit can display related product names as candidates. This allows the user to quickly and accurately input product information using voice input.

[0052] When acquiring inventory information for each store, the acquisition unit can ensure the reliability of the inventory information by using blockchain technology. For example, the acquisition unit records the inventory information for each store on a blockchain to prevent tampering and fraud. The acquisition unit can also update the inventory information on the blockchain in real time to acquire the latest inventory status. Furthermore, the acquisition unit can also use blockchain technology to ensure transparency of the inventory information and provide users with reliable information. This allows users to select products based on reliable inventory information. For example, the acquisition unit checks on the blockchain whether a "product" is in stock at a specific store. The acquisition unit can also update the inventory information on the blockchain in real time to acquire the latest inventory status. Furthermore, the acquisition unit can use blockchain technology to ensure transparency of the inventory information and provide users with reliable information. This allows users to select products based on reliable inventory information.

[0053] The identification unit can identify the optimal store by taking into consideration not only the user's current location but also the user's travel route. For example, if the user is commuting or traveling, the identification unit prioritizes identifying stores on the user's travel route. The identification unit can also identify the most efficiently accessible store by taking into consideration the user's travel speed and traffic conditions. Furthermore, the identification unit can suggest an optimal route that passes through multiple stores based on the user's travel route. This allows the user to efficiently obtain products even while traveling. For example, if the user wants to purchase a product while commuting, the identification unit prioritizes identifying stores on the user's commute route. Furthermore, if the user wants to purchase a product while traveling, the identification unit prioritizes identifying stores on the user's travel route. Furthermore, the identification unit can also identify the most efficiently accessible store by taking into consideration the user's travel speed and traffic conditions. This allows the user to efficiently obtain products even while traveling.

[0054] The identification unit can analyze the user's purchase history and identify stores that sell products that are highly related to products purchased in the past. For example, the identification unit can prioritize stores that sell related products based on product information about products purchased in the past by the user. The identification unit can also identify stores that sell products of a specific brand or category from the user's purchase history. Furthermore, the identification unit can analyze the user's purchase history and identify stores that sell new products that the user may be interested in. This allows the user to select the optimal store based on their past purchase history. For example, if the user previously purchased a "smartphone," the identification unit prioritizes stores that sell related "smartphone accessories." Furthermore, if the user previously purchased products from a specific brand, the identification unit can identify stores that sell products from that brand. Furthermore, if the user previously purchased products from a specific category, the identification unit can also identify stores that sell products in that category. This allows the user to select the optimal store based on their past purchase history.

[0055] When a user receives product information, the providing unit can provide the information visually using augmented reality (AR) technology. For example, the providing unit can display the location of a store and product information using AR technology through the user's smartphone camera. The providing unit can also visually display detailed product information and reviews using AR technology. Furthermore, the providing unit can use AR technology to provide the user with an experience that feels as if they are actually holding the product. This allows the user to visually confirm the information and select a product. For example, when a user confirms the location of a store using the smartphone camera, the providing unit displays the location of the store using AR technology. Furthermore, when the user confirms the product information, the providing unit can visually display detailed product information and reviews using AR technology. Furthermore, when the user selects a product, the providing unit can use AR technology to provide the user with an experience that feels as if they are actually holding the product. This allows the user to visually confirm the information and select a product.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit is a section where the user inputs the necessary product information. For example, the user can input the product name into an app or website. The reception unit can also use generation AI to analyze the product information entered by the user. Step 2: The acquisition unit accesses the database of each store based on the product information entered by the reception unit and acquires inventory information in real time. For example, the acquisition unit checks whether a specific store has the "product" in stock. The acquisition unit can also use the generation AI to acquire inventory information from the database of each store. Step 3: The identification unit identifies the optimal store based on the inventory information acquired by the acquisition unit. For example, the identification unit identifies the store closest to the user's current location or the cheapest store. The identification unit can also use a generation AI to identify the optimal store. Step 4: The providing unit immediately provides the information identified by the identifying unit to the user. For example, the providing unit displays information about the store closest to the user or the cheapest store. The providing unit can also use the generating AI to provide the identified information to the user.

[0058] (Example 2) A system according to an embodiment of the present invention uses a generation AI to instantly identify the store where a desired product can be purchased in the shortest time. When a user inputs the desired product into an app or website, the generation AI connects with each store to check its availability and stock status. The generation AI identifies the closest store to the user's current location and the store with the lowest price, and immediately provides this information to the user. This allows the user to quickly obtain the desired product. For example, a user inputs a specific product name, such as "product," into an app or website. This information is input into the generation AI. The generation AI then analyzes the input product information and connects with each store to check its availability and stock status. The generation AI accesses each store's database to obtain inventory information in real time. For example, it checks whether a specific store has the "product" in stock. Based on the obtained inventory information, the generation AI identifies the closest store to the user's current location and the store with the lowest price. For example, if the closest store to the user's current location is "Store A" and the cheapest store is "Store B," the system provides that information to the user. Based on the information provided by the generative AI, users can select the optimal store and quickly obtain products. For example, by going to "Store A," users can obtain products in the shortest time. Alternatively, by going to "Store B," users can obtain products at the lowest price. This system allows users to obtain the products they need quickly and efficiently. For example, it is extremely convenient in business and daily life because it allows users to obtain urgently needed products on the same day. Furthermore, identifying the cheapest store can reduce costs. In this way, systems using generative AI allow users to quickly obtain the products they need.

[0059] A product identification system according to an embodiment includes a reception unit, an acquisition unit, an identification unit, and a provision unit. The reception unit is a component through which a user inputs necessary product information. For example, the user can input a product name into an app or a website. The reception unit can also analyze the product information input by the user using a generation AI. The acquisition unit accesses each store's database based on the product information input by the reception unit and acquires inventory information in real time. For example, the acquisition unit checks whether a specific store has the "product" in stock. The acquisition unit can also acquire inventory information from each store's database using a generation AI. The identification unit identifies the optimal store based on the inventory information acquired by the acquisition unit. For example, the identification unit identifies the store closest to the user's current location or the store with the lowest price. The identification unit can also identify the optimal store using a generation AI. The provision unit immediately provides the information identified by the identification unit to the user. For example, the provision unit displays information about the store closest to the user or the store with the lowest price. The provision unit can also provide the identified information to the user using a generation AI. As a result, the product identification system according to the embodiment allows users to quickly obtain the products they need. For example, when a user enters the name of a product into an app or website, the generation AI connects with each store to check the product availability and stock status. The generation AI identifies the store closest to the user's current location where the product can be obtained, or the store with the lowest price, and immediately provides this information to the user. This allows users to quickly obtain the products they need. For example, products that are urgently needed can be obtained on the same day, which is extremely convenient in business and daily life. Furthermore, identifying the cheapest store can reduce costs.

[0060] The acquisition unit can access each store's database and obtain inventory information in real time. To obtain inventory information in real time, it is necessary to access each store's database. For example, the acquisition unit checks whether a specific store has a product in stock. The acquisition unit can also use generative AI to obtain inventory information from each store's database. For example, the acquisition unit accesses each store's database and obtains inventory information in real time. To obtain inventory information in real time, it is necessary to consider the database update interval and delay time. For example, the acquisition unit prioritizes obtaining inventory information from stores with short database update intervals. The acquisition unit can also prioritize obtaining inventory information from stores with short delay times. This allows inventory information to be obtained in real time. For example, when a user enters a product name into an app or website, the acquisition unit accesses each store's database and obtains inventory information in real time. This allows users to quickly obtain the products they need. For example, it allows users to obtain urgently needed products on the same day, which is very convenient in business and everyday life.

[0061] The identification unit can identify stores where products can be purchased near the user's current location. The identification unit needs to acquire current location information to identify the store closest to the user's current location. For example, the identification unit acquires the user's current location using GPS information or an IP address. The identification unit can also use a generation AI to identify the store closest to the user's current location. For example, the identification unit acquires location information of each store to identify the store closest to the user's current location. The identification unit can also use a generation AI to analyze the location information of each store and identify the store closest to the user's current location. This allows the user to identify the nearest store. For example, when a user enters a product name into an app or website, the identification unit identifies the store closest to the user's current location. This allows the user to quickly obtain the product they need. For example, this is very convenient in business and everyday life because it allows users to obtain urgently needed products on the same day.

[0062] The identification unit can identify inexpensive stores. In order for a user to identify the cheapest store, the identification unit needs to obtain price information from each store. For example, the identification unit obtains price information from each store and performs price comparisons. The identification unit can also use a generation AI to analyze the price information from each store and identify the cheapest store. For example, the identification unit obtains price information from each store and performs price comparisons taking discount information into account. The identification unit can also use a generation AI to analyze the price information from each store and identify the cheapest store. This allows the user to identify the cheapest store. For example, when a user enters a product name into an app or website, the identification unit obtains price information from each store and identifies the cheapest store. This allows the user to obtain the product they need at the cheapest price. For example, this is very convenient in business and everyday life because it can reduce costs.

[0063] The providing unit can immediately provide the identified information to the user. In order to immediately provide the information identified by the identifying unit to the user, the providing unit needs to adjust the method of providing information. For example, the providing unit displays information about the store closest to the user or the cheapest store. The providing unit can also use a generation AI to provide the identified information to the user. For example, the providing unit immediately displays information about the store closest to the user or the cheapest store. The providing unit can also use a generation AI to immediately provide the identified information to the user. This allows the user to obtain the information immediately. For example, when a user enters a product name into an app or a website, the providing unit immediately provides the user with the information identified by the identifying unit. This allows the user to quickly obtain the product they need. For example, it is possible to obtain a product that is urgently needed on the same day, which is very convenient in business and everyday life.

[0064] The reception unit can estimate the user's emotions and adjust the input method for product information based on the estimated user emotions. The reception unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input product information. This makes it possible to provide an input method that suits the user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface, minimizing input steps and reducing the burden on the user. Furthermore, if the user is relaxed, the reception unit can provide detailed input options, allowing the user to customize the input method to suit their preferences. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input product information. This makes it possible to provide an input method that suits the user's emotions.

[0065] The reception unit can analyze the user's past input history and suggest an appropriate input format. The reception unit can use data analysis technology to analyze the user's past input history. For example, the reception unit can automatically display product information that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (such as voice and text) that the user has used in the past. The reception unit can also predict and suggest product information that will be used in a specific time period based on the user's past input history. This makes it possible to provide an optimal input format based on the user's past input history. For example, automatically displaying product information that the user has frequently input in the past as candidates can reduce the effort required for input. Furthermore, by preferentially suggesting input methods that the user has used in the past, the user can input product information in a manner that the user is familiar with. Furthermore, predicting and suggesting product information that will be used in a specific time period based on the user's past input history can allow the user to quickly input the product information they need. This makes it possible to provide an optimal input format based on the user's past input history.

[0066] The reception unit can automatically complete input candidates based on the user's current purchasing trends when entering product information. The reception unit can use data analysis technology to analyze the user's current purchasing trends. For example, the reception unit can display related products as input candidates based on products recently purchased by the user. The reception unit can also automatically display frequently purchased products as input candidates based on the user's purchasing history. Furthermore, the reception unit can analyze the user's purchasing trends and suggest products that match the season or trend as input candidates. This makes it possible to automatically complete input candidates based on the user's purchasing trends. For example, displaying related products as input candidates based on the user's recently purchased products can reduce the effort required for input. Furthermore, automatically displaying frequently purchased products as input candidates based on the user's purchasing history can allow the user to quickly enter required product information. Furthermore, analyzing the user's purchasing trends and suggesting products that match the season or trend as input candidates can allow the user to quickly enter required product information. This makes it possible to automatically complete input candidates based on the user's purchasing trends.

[0067] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. The reception unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide an interface with simple, high visibility to make input work easier. In this way, an input interface can be provided that corresponds to the user's emotion. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide an interface with simple, high visibility to make input work more enjoyable. In this way, an input interface can be provided that corresponds to the user's emotion.

[0068] The reception unit can present highly relevant product candidates based on the user's geographical location information when the user inputs product information. The reception unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the reception unit can display products sold in nearby stores as input candidates based on the user's current location. The reception unit can also suggest local products as input candidates based on the user's geographical location information. The reception unit can also display products that are popular in a specific region as input candidates based on the user's location information. This makes it possible to present highly relevant product candidates based on the user's geographical location information. For example, displaying products sold in nearby stores as input candidates based on the user's current location can reduce the effort required for input. Furthermore, suggesting local products as input candidates based on the user's geographical location information allows the user to quickly input required product information. Furthermore, displaying products that are popular in a specific region as input candidates based on the user's location information allows the user to quickly input required product information. This makes it possible to present highly relevant product candidates based on the user's geographical location information.

[0069] The reception unit can analyze the user's social media activity when entering product information and suggest related products. The reception unit can use data analysis technology to analyze the user's social media activity. For example, the reception unit can display products that the user has "liked" on social media as input candidates. The reception unit can also suggest products that the user may be interested in based on the content of the user's social media posts. Furthermore, the reception unit can also display related products as input candidates by referring to products purchased by the user's followers. This makes it possible to suggest related products based on the user's social media activity. For example, by displaying products that the user has "liked" on social media as input candidates, it is possible to reduce the effort required for input. Furthermore, by suggesting products that the user may be interested in based on the content of the user's social media posts, it is possible to quickly enter the product information the user needs. Furthermore, by displaying related products as input candidates by referring to products purchased by the user's followers, it is possible to quickly enter the product information the user needs. This makes it possible to suggest related products based on the user's social media activity.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring inventory information based on the estimated user emotions. The acquisition unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, if the user is in a hurry, the acquisition unit can immediately acquire inventory information. Furthermore, if the user is relaxed, the acquisition unit can take a little time to acquire detailed inventory information. Furthermore, if the user is feeling stressed, the acquisition unit can quickly acquire inventory information, giving the user a sense of security. This makes it possible to provide the timing of acquiring inventory information according to the user's emotions. For example, if the user is in a hurry, the inventory information can be acquired immediately, allowing the user to quickly obtain the product they need. Furthermore, if the user is relaxed, the detailed inventory information can be acquired over a little time, allowing the user to accurately obtain the product they need. Furthermore, if the user is feeling stressed, the inventory information can be acquired quickly, giving the user a sense of security. This makes it possible to provide the timing of acquiring inventory information according to the user's emotions.

[0071] The acquisition unit can analyze past inventory data for each store to improve the accuracy of inventory information acquisition. The acquisition unit can use data analysis technology to analyze the past inventory data for each store. For example, the acquisition unit can analyze inventory fluctuation patterns based on the past inventory data for each store. The acquisition unit can also predict, from the past inventory data, when a particular product is likely to run out of stock. The acquisition unit can also improve the accuracy of inventory information acquisition by referring to the past inventory data for each store. This can improve the accuracy of inventory information acquisition based on past inventory data. For example, the acquisition unit can improve the accuracy of inventory information acquisition by analyzing inventory fluctuation patterns based on the past inventory data for each store. The acquisition unit can also improve the accuracy of inventory information acquisition by predicting, from the past inventory data, when a particular product is likely to run out of stock. Furthermore, the acquisition unit can improve the accuracy of inventory information acquisition by referring to the past inventory data for each store to improve the accuracy of inventory information acquisition, allowing users to quickly and accurately obtain the products they need. This can improve the accuracy of inventory information acquisition based on past inventory data.

[0072] The acquisition unit can take into account the business status and special sale information of each store when acquiring inventory information. The acquisition unit can use data analysis technology to take into account the business status and special sale information of each store when acquiring inventory information. For example, the acquisition unit checks the business status of each store and prioritizes acquiring inventory information from stores that are open. The acquisition unit can also take into account special sale information and prioritize acquiring products on sale. Furthermore, the acquisition unit can adjust the timing of acquiring inventory information by taking into account the business hours of each store. In this way, inventory information can be acquired by taking into account the business status and special sale information. For example, the acquisition unit checks the business status of each store and prioritizes acquiring inventory information from stores that are open, allowing a user to quickly obtain needed products. In addition, the acquisition unit takes into account special sale information and prioritizes acquiring products on sale, allowing a user to obtain needed products at the lowest price. Furthermore, the acquisition unit adjusts the timing of acquiring inventory information by taking into account the business hours of each store, allowing a user to quickly and accurately obtain needed products. In this way, inventory information can be acquired by taking into account the business status and special sale information.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of inventory information to be acquired based on the estimated user's emotions. The acquisition unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, if the user is in a hurry, the acquisition unit can prioritize acquiring inventory information from the nearest store. Furthermore, if the user is relaxed, the acquisition unit can also prioritize acquiring inventory information with an emphasis on price and quality. Furthermore, if the user is feeling stressed, the acquisition unit can prioritize acquiring information from stores that are sure to have the product in stock. This makes it possible to provide a priority order of inventory information according to the user's emotions. For example, if the user is in a hurry, the acquisition unit can prioritize acquiring inventory information from the nearest store, allowing the user to quickly obtain the product they need. Furthermore, if the user is relaxed, the acquisition unit can prioritize acquiring inventory information with an emphasis on price and quality, allowing the user to accurately obtain the product they need. Furthermore, if the user is feeling stressed, the acquisition unit can prioritize acquiring information from stores that are sure to have the product in stock, giving the user a sense of security. This makes it possible to provide a priority order of inventory information according to the user's emotions.

[0074] When acquiring inventory information, the acquisition unit can prioritize acquiring inventory information from highly relevant stores by taking into account the user's geographical location information. The acquisition unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the acquisition unit prioritizes acquiring inventory information from the nearest store based on the user's current location. The acquisition unit can also prioritize acquiring inventory information for products that are popular in a specific area based on the user's geographical location information. Furthermore, the acquisition unit can also prioritize acquiring inventory information for stores that are easily accessible based on the user's location information. This makes it possible to acquire inventory information from highly relevant stores based on the user's geographical location information. For example, by preferentially acquiring inventory information from the nearest store based on the user's current location, the user can quickly obtain the product he or she needs. Furthermore, by preferentially acquiring inventory information for products that are popular in a specific area based on the user's geographical location information, the user can quickly obtain the product he or she needs. Furthermore, by preferentially acquiring inventory information from stores that are easily accessible based on the user's location information, the user can quickly obtain the product he or she needs. This makes it possible to acquire inventory information from highly relevant stores based on the user's geographical location information.

[0075] When acquiring inventory information, the acquisition unit can analyze the user's social media activity and acquire related inventory information. The acquisition unit can use data analysis technology to analyze the user's social media activity. For example, the acquisition unit acquires inventory information of stores that sell products that the user has "liked" on social media. The acquisition unit can also acquire inventory information of products that the user is likely to be interested in based on the content of the user's social media posts. Furthermore, the acquisition unit can also acquire related inventory information by referring to products purchased by the user's followers. In this way, related inventory information can be acquired based on the user's social media activity. For example, by acquiring inventory information of stores that sell products that the user has "liked" on social media, the user can quickly obtain the product he or she needs. In addition, by acquiring inventory information of products that the user is likely to be interested in based on the content of the user's social media posts, the user can quickly obtain the product he or she needs. Furthermore, by acquiring related inventory information by referring to products purchased by the user's followers, the user can quickly obtain the product he or she needs. In this way, related inventory information can be acquired based on the user's social media activity.

[0076] The identification unit can estimate the user's emotions and adjust the optimal store identification method based on the estimated user's emotions. The identification unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, when the user is in a hurry, the identification unit prioritizes identifying the nearest store. Furthermore, when the user is relaxed, the identification unit can prioritize identifying a store that is sure to have the product in stock. This makes it possible to provide a store identification method that corresponds to the user's emotions. For example, when the user is in a hurry, the identification unit prioritizes identifying the nearest store, allowing the user to quickly obtain the product they need. Furthermore, when the user is relaxed, the identification unit prioritizes identifying a store that is sure to have the product in stock, allowing the user to accurately obtain the product they need. Furthermore, when the user is stressed, the identification unit prioritizes identifying a store that is sure to have the product in stock, giving the user a sense of security. This makes it possible to provide a store identification method that corresponds to the user's emotions.

[0077] During the identification, the identification unit can analyze the user's past purchase history to select an appropriate store. The identification unit can use data analysis technology to analyze the user's past purchase history. For example, the identification unit prioritizes identifying stores that the user has frequently used in the past. The identification unit can also select a store that sells a specific product from the user's past purchase history. The identification unit can also analyze the user's purchase history and select a store that provides the highest level of satisfaction. This makes it possible to select an optimal store based on the user's past purchase history. For example, by preferentially identifying stores that the user has frequently used in the past, the user can quickly obtain the product they need. By selecting a store that sells a specific product from the user's past purchase history, the user can quickly obtain the product they need. Furthermore, by analyzing the user's purchase history and selecting a store that provides the highest level of satisfaction, the user can quickly obtain the product they need. This makes it possible to select an optimal store based on the user's past purchase history.

[0078] During the identification process, the identification unit can select an appropriate store by taking into consideration the past ratings and reviews of each store. The identification unit can use data analysis technology to consider the past ratings and reviews of each store. For example, the identification unit selects the store with the highest rating based on the past ratings of each store. The identification unit can also analyze the reviews of each store to select a store that meets the user's needs. The identification unit can also select a highly reliable store by referring to the ratings and reviews of each store. This makes it possible to select the optimal store based on the ratings and reviews of each store. For example, the identification unit can select the store with the highest rating based on the past ratings of each store, allowing the user to quickly obtain the product they need. The identification unit can analyze the reviews of each store and select a store that meets the user's needs, allowing the user to quickly obtain the product they need. The identification unit can select a highly reliable store by referring to the ratings and reviews of each store, allowing the user to quickly obtain the product they need. This makes it possible to select the optimal store based on the ratings and reviews of each store.

[0079] The identification unit can estimate the user's emotions and adjust the display order of the identified stores based on the estimated user's emotions. The identification unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, if the user is in a hurry, the identification unit can display the nearest store at the top. Furthermore, if the user is relaxed, the identification unit can display stores that emphasize price and quality. Furthermore, if the user is stressed, the identification unit can display stores that are sure to have the product in stock at the top. This makes it possible to provide a display order of stores according to the user's emotions. For example, if the user is in a hurry, the nearest store can be displayed at the top, allowing the user to quickly obtain the product they need. Furthermore, if the user is relaxed, the stores can be displayed with an emphasis on price and quality, allowing the user to accurately obtain the product they need. Furthermore, if the user is stressed, the stores that are sure to have the product in stock can be displayed at the top, giving the user a sense of security. This makes it possible to provide a display order of stores according to the user's emotions.

[0080] During identification, the identification unit can select an appropriate store by taking into consideration the user's geographical location information. The identification unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the identification unit selects the nearest store based on the user's current location. The identification unit can also select a store that is easily accessible based on the user's geographical location information. Furthermore, the identification unit can select a popular store in a specific area based on the user's location information. This makes it possible to select the optimal store based on the user's geographical location information. For example, the identification unit can select the nearest store based on the user's current location, allowing the user to quickly obtain the product they need. The identification unit can also select a store that is easily accessible based on the user's geographical location information, allowing the user to quickly obtain the product they need. Furthermore, the identification unit can select a popular store in a specific area based on the user's location information, allowing the user to quickly obtain the product they need. This makes it possible to select the optimal store based on the user's geographical location information.

[0081] During the identification, the identification unit can analyze the user's social media activity and identify related stores. The identification unit can use data analysis technology to analyze the user's social media activity. For example, the identification unit prioritizes identifying stores that the user has "liked" on social media. The identification unit can also identify stores that the user is likely to be interested in based on the content of the user's social media posts. Furthermore, the identification unit can also identify related stores by referring to stores that the user's followers have visited. In this way, related stores can be identified based on the user's social media activity. For example, the identification unit prioritizes identifying stores that the user has "liked" on social media, allowing the user to quickly obtain the products they need. Furthermore, the identification unit can identify stores that the user is likely to be interested in based on the content of the user's social media posts, allowing the user to quickly obtain the products they need. Furthermore, the identification unit can identify related stores by referring to stores that the user's followers have visited. In this way, related stores can be identified based on the user's social media activity.

[0082] The providing unit can estimate the user's emotion and adjust the information provision method based on the estimated user's emotion. The providing unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotion. For example, when the user is nervous, the providing unit can provide a simple, highly visible information provision method. Furthermore, when the user is relaxed, the providing unit can provide a method of providing detailed information. Furthermore, when the user is in a hurry, the providing unit can provide a method of providing information that focuses on the main points. This makes it possible to provide an information provision method that corresponds to the user's emotion. For example, when the user is nervous, a simple, highly visible information provision method can be provided, allowing the user to quickly understand the information. Furthermore, when the user is relaxed, a method of providing detailed information can be provided, allowing the user to understand the information in detail. Furthermore, when the user is in a hurry, an information provision method that focuses on the main points can be provided, allowing the user to quickly obtain the information they need. This makes it possible to provide an information provision method that corresponds to the user's emotion.

[0083] When providing information, the providing unit can analyze the user's past response history and select the optimal information providing method. The providing unit can use data analysis technology to analyze the user's past response history. For example, the providing unit can prioritize selecting an information providing method that the user previously preferred. The providing unit can also select the most effective information providing method from the user's past response history. Furthermore, the providing unit can analyze the user's past response history and provide information at the optimal timing. This makes it possible to provide the optimal information providing method based on the user's past response history. For example, the providing unit prioritizes selecting an information providing method that the user previously preferred, allowing the user to quickly understand the information. Furthermore, the providing unit selects the most effective information providing method from the user's past response history, allowing the user to understand the information in detail. Furthermore, the providing unit analyzes the user's past response history and provides information at the optimal timing, allowing the user to quickly obtain the information they need. This makes it possible to provide the optimal information providing method based on the user's past response history.

[0084] The providing unit can customize the information display method based on the user's current situation when providing information. The providing unit can use information such as time of day, location, and device status to acquire the user's current situation. For example, when the user is traveling, the providing unit can provide concise and highly visible information. Furthermore, when the user is at home, the providing unit can provide a provision method including detailed information. Furthermore, when the user is in a meeting, the providing unit can reduce notifications and allow the user to check the information later. This makes it possible to provide an information display method according to the user's current situation. For example, when the user is traveling, providing concise and highly visible information allows the user to quickly understand the information. Furthermore, when the user is at home, providing a provision method including detailed information allows the user to understand the information in detail. Furthermore, when the user is in a meeting, providing reduce notifications and allow the user to check the information later allows the user to obtain the information at an appropriate time. This makes it possible to provide an information display method according to the user's current situation.

[0085] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. The providing unit can use technologies such as facial expression recognition and voice analysis to estimate the user's emotions. For example, when the user is in a hurry, the providing unit can prioritize providing the most important information. Furthermore, when the user is relaxed, the providing unit can also provide a method of providing detailed information. Furthermore, when the user is stressed, the providing unit can prioritize providing information that gives a sense of security. This makes it possible to provide the priority of information provision according to the user's emotions. For example, when the user is in a hurry, the most important information can be prioritized, allowing the user to quickly obtain the information they need. Furthermore, when the user is relaxed, the providing unit can provide a method of providing detailed information, allowing the user to understand the information in detail. Furthermore, when the user is stressed, the providing unit can prioritize providing information that gives a sense of security, allowing the user to quickly understand the information. This makes it possible to provide the priority of information provision according to the user's emotions.

[0086] When providing information, the providing unit can provide appropriate information by taking into account the user's geographical location information. The providing unit can use technologies such as GPS information and IP addresses to acquire the user's geographical location information. For example, the providing unit can provide nearby store information based on the user's current location. The providing unit can also provide area-specific sales information based on the user's geographical location information. Furthermore, the providing unit can provide information on easily accessible stores based on the user's location information. This makes it possible to provide optimal information based on the user's geographical location information. For example, the providing unit can provide nearby store information based on the user's current location, allowing the user to quickly obtain the information they need. The providing unit can also provide area-specific sales information based on the user's geographical location information. Furthermore, the providing unit can provide information on easily accessible stores based on the user's location information. This makes it possible to provide optimal information based on the user's geographical location information.

[0087] The providing unit can analyze the user's social media activity and provide related information when providing information. The providing unit can use data analysis technology to analyze the user's social media activity. For example, the providing unit can provide information about products that the user has "liked" on social media. The providing unit can also provide information that is likely to be of interest to the user based on the content of the user's social media posts. Furthermore, the providing unit can also provide information about products purchased by the user's followers. In this way, related information can be provided based on the user's social media activity. For example, the providing unit can provide information about products that the user has "liked" on social media, allowing the user to quickly obtain the information he or she needs. In addition, the providing unit can provide information that is likely to be of interest to the user based on the content of the user's social media posts. Furthermore, the providing unit can provide information about products purchased by the user's followers, allowing the user to quickly obtain the information he or she needs. In this way, related information can be provided based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, acquisition unit, identification unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows a user to input a product name into an app or a website. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and accesses the database of each store to acquire inventory information in real time. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the store closest to the user's current location or the store with the lowest price. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and immediately provides the identified information to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, acquisition unit, identification unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to input a product name into an app or a website. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and accesses the database of each store to acquire inventory information in real time. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the store closest to the user's current location or the store with the lowest price. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and immediately provides the identified information to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, acquisition unit, identification unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and allows a user to input a product name into an app or a website. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and accesses the database of each store to acquire inventory information in real time. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the store closest to the user's current location or the store with the lowest price. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and immediately provides the identified information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, identification unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to input a product name into an app or a website. The acquisition unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and accesses the database of each store to acquire inventory information in real time. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the store closest to the user's current location or the store with the lowest price. The provision unit is realized, for example, by the control unit 46A of the robot 414, and immediately provides the identified information to the user.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The reception unit can support voice input using voice recognition technology when a user inputs product information. For example, the user can input product names by voice, saving time and effort. The reception unit can also use noise canceling technology to remove ambient noise when a user inputs product information, enabling accurate voice input. Furthermore, the reception unit can analyze the user's pronunciation and accent during voice input and automatically complete appropriate product names. This allows the user to quickly and accurately input product information using voice input. For example, when a user inputs "smartphone," the reception unit can remove ambient noise using noise canceling technology and accurately recognize the word as "smartphone." When a user inputs "smartphone case," the reception unit can analyze the pronunciation and accent and automatically complete appropriate product names. Furthermore, when a user inputs "smartphone accessories," the reception unit can display related product names as candidates. This allows the user to quickly and accurately input product information using voice input.

[0090] When acquiring inventory information for each store, the acquisition unit can ensure the reliability of the inventory information by using blockchain technology. For example, the acquisition unit records the inventory information for each store on a blockchain to prevent tampering and fraud. The acquisition unit can also update the inventory information on the blockchain in real time to acquire the latest inventory status. Furthermore, the acquisition unit can also use blockchain technology to ensure transparency of the inventory information and provide users with reliable information. This allows users to select products based on reliable inventory information. For example, the acquisition unit checks on the blockchain whether a "product" is in stock at a specific store. The acquisition unit can also update the inventory information on the blockchain in real time to acquire the latest inventory status. Furthermore, the acquisition unit can use blockchain technology to ensure transparency of the inventory information and provide users with reliable information. This allows users to select products based on reliable inventory information.

[0091] The identification unit can identify the optimal store by taking into consideration not only the user's current location but also the user's travel route. For example, if the user is commuting or traveling, the identification unit prioritizes identifying stores on the user's travel route. The identification unit can also identify the most efficiently accessible store by taking into consideration the user's travel speed and traffic conditions. Furthermore, the identification unit can suggest an optimal route that passes through multiple stores based on the user's travel route. This allows the user to efficiently obtain products even while traveling. For example, if the user wants to purchase a product while commuting, the identification unit prioritizes identifying stores on the user's commute route. Furthermore, if the user wants to purchase a product while traveling, the identification unit prioritizes identifying stores on the user's travel route. Furthermore, the identification unit can also identify the most efficiently accessible store by taking into consideration the user's travel speed and traffic conditions. This allows the user to efficiently obtain products even while traveling.

[0092] The identification unit can analyze the user's purchase history and identify stores that sell products that are highly related to products purchased in the past. For example, the identification unit can prioritize stores that sell related products based on product information about products purchased in the past by the user. The identification unit can also identify stores that sell products of a specific brand or category from the user's purchase history. Furthermore, the identification unit can analyze the user's purchase history and identify stores that sell new products that the user may be interested in. This allows the user to select the optimal store based on their past purchase history. For example, if the user previously purchased a "smartphone," the identification unit prioritizes stores that sell related "smartphone accessories." Furthermore, if the user previously purchased products from a specific brand, the identification unit can identify stores that sell products from that brand. Furthermore, if the user previously purchased products from a specific category, the identification unit can also identify stores that sell products in that category. This allows the user to select the optimal store based on their past purchase history.

[0093] When a user receives product information, the providing unit can provide the information visually using augmented reality (AR) technology. For example, the providing unit can display the location of a store and product information using AR technology through the user's smartphone camera. The providing unit can also visually display detailed product information and reviews using AR technology. Furthermore, the providing unit can use AR technology to provide the user with an experience that feels as if they are actually holding the product. This allows the user to visually confirm the information and select a product. For example, when a user confirms the location of a store using the smartphone camera, the providing unit displays the location of the store using AR technology. Furthermore, when the user confirms the product information, the providing unit can visually display detailed product information and reviews using AR technology. Furthermore, when the user selects a product, the providing unit can use AR technology to provide the user with an experience that feels as if they are actually holding the product. This allows the user to visually confirm the information and select a product.

[0094] The reception unit can estimate the user's emotions and adjust the input method for product information based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is feeling relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input product information. In this way, an input method can be provided that suits the user's emotions. For example, if the user is feeling stressed, a simple interface can be provided, minimizing input steps and reducing the burden on the user. Furthermore, if the user is relaxed, detailed input options can be provided, allowing the user to customize the input method to suit their preferences. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing the user to quickly input product information. In this way, an input method can be provided that suits the user's emotions.

[0095] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring inventory information based on the estimated user's emotions. For example, if the user is in a hurry, the acquisition unit can immediately acquire inventory information. Furthermore, if the user is relaxed, the acquisition unit can take a little time to acquire detailed inventory information. Furthermore, if the user is feeling stressed, the acquisition unit can quickly acquire inventory information, giving the user a sense of security. This makes it possible to provide the timing of acquiring inventory information according to the user's emotions. For example, if the user is in a hurry, the acquisition of inventory information can be immediate, allowing the user to quickly obtain the product they need. Furthermore, if the user is relaxed, the acquisition of detailed inventory information can be timed, allowing the user to accurately obtain the product they need. Furthermore, if the user is feeling stressed, the acquisition of inventory information can be immediate, giving the user a sense of security. This makes it possible to provide the timing of acquiring inventory information according to the user's emotions.

[0096] The identification unit can estimate the user's emotions and adjust the optimal store identification method based on the estimated user's emotions. For example, when the user is in a hurry, the identification unit prioritizes identifying the nearest store. Furthermore, when the user is relaxed, the identification unit can also prioritize identifying a store that is sure to have the product in stock. This makes it possible to provide a store identification method that corresponds to the user's emotions. For example, when the user is in a hurry, the identification unit prioritizes identifying the nearest store, allowing the user to quickly obtain the product they need. Furthermore, when the user is relaxed, the identification unit prioritizes identifying a store that is sure to have the product in stock, allowing the user to accurately obtain the product they need. Furthermore, when the user is stressed, the identification unit prioritizes identifying a store that is sure to have the product in stock, giving the user a sense of security. This makes it possible to provide a store identification method that corresponds to the user's emotions.

[0097] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, when the user is nervous, the providing unit can provide a simple, highly visible information provision method. Furthermore, when the user is relaxed, the providing unit can also provide a provision method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide an information provision method that focuses on the main points. In this way, it is possible to provide an information provision method that corresponds to the user's emotions. For example, when the user is nervous, a simple, highly visible information provision method can be provided, allowing the user to quickly understand the information. Furthermore, when the user is relaxed, a provision method including detailed information can be provided, allowing the user to understand the information in detail. Furthermore, when the user is in a hurry, an information provision method that focuses on the main points can be provided, allowing the user to quickly obtain the information they need. In this way, it is possible to provide an information provision method that corresponds to the user's emotions.

[0098] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is in a hurry, the providing unit can prioritize providing the most important information. Furthermore, when the user is relaxed, the providing unit can provide a method of providing information including detailed information. Furthermore, when the user is feeling stressed, the providing unit can prioritize providing information that gives a sense of security. This makes it possible to provide the priority of information provision according to the user's emotions. For example, when the user is in a hurry, the most important information can be provided preferentially, allowing the user to quickly obtain the information they need. Furthermore, when the user is relaxed, the providing unit can provide a method of providing information including detailed information, allowing the user to understand the information in detail. Furthermore, when the user is feeling stressed, the providing unit can prioritize providing information that gives a sense of security, allowing the user to quickly understand the information. This makes it possible to provide the priority of information provision according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit is a section where the user inputs the necessary product information. For example, the user can input the product name into an app or website. The reception unit can also use generation AI to analyze the product information entered by the user. Step 2: The acquisition unit accesses the database of each store based on the product information entered by the reception unit and acquires inventory information in real time. For example, the acquisition unit checks whether a specific store has the "product" in stock. The acquisition unit can also use the generation AI to acquire inventory information from the database of each store. Step 3: The identification unit identifies the optimal store based on the inventory information acquired by the acquisition unit. For example, the identification unit identifies the store closest to the user's current location or the cheapest store. The identification unit can also use a generation AI to identify the optimal store. Step 4: The providing unit immediately provides the information identified by the identifying unit to the user. For example, the providing unit displays information about the store closest to the user or the cheapest store. The providing unit can also use the generating AI to provide the identified information to the user.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[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 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.

[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 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.

[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 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.

[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] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting product information; an acquisition unit that acquires inventory information based on the product information input by the reception unit; an identification unit that identifies an appropriate store based on the inventory information acquired by the acquisition unit; a providing unit that provides the information identified by the identifying unit; Equipped with A system characterized by:

2. The acquisition unit Access each store's database and obtain real-time inventory information 2. The system of claim 1.

3. The identification unit Identify stores near your current location that have products available 2. The system of claim 1.

4. The identification unit Identify low-cost stores 2. The system of claim 1.

5. The providing unit Providing identified information to the user immediately 2. The system of claim 1.

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

7. The reception unit Analyzes the user's past input history and suggests appropriate input formats 2. The system of claim 1.

8. The reception unit When entering product information, auto-complete suggestions based on the user's current purchasing habits 2. The system of claim 1.

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