system

Abstract

The system according to this embodiment aims to efficiently identify product information and check inventory status. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a confirmation unit. The reception unit inputs product information. The generation unit analyzes the information input by the reception unit and identifies the product. The confirmation unit confirms the inventory status based on the product identified by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently identify product information and check the inventory status.

[0005] The system according to the embodiment aims to efficiently identify product information and check the inventory status.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a confirmation unit. The reception unit inputs product information. The generation unit analyzes the information input by the reception unit to identify the product. The confirmation unit checks the inventory status based on the product identified by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently identify product information and check inventory status. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 3, 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The product search system according to an embodiment of the present invention is a system that allows users to search whether a product they saw on television or the internet is sold at a nearby store. This system allows users to input either a product name, barcode, photograph, or image, and uses a generation AI and image recognition technology to identify the product and check its inventory status at nearby stores. For example, a user uploads a photograph of a product they saw on television. This information is input to the generation AI and image recognition technology. Next, the generation AI and image recognition technology analyze the input information. The generation AI identifies the product from ambiguous product descriptions, and the image recognition technology identifies the product from the uploaded image. For example, based on a photograph of a product uploaded by the user, the generation AI identifies the product name, and the image recognition technology identifies the product. Based on the identified product, the system checks the inventory status at nearby stores. For example, based on the product name identified by the generation AI, the system displays inventory information for nearby stores in real time. Map display and route guidance are also provided. This allows users to easily search for products and check their inventory status. For example, a user can search for a product they saw on television using a photograph and check its inventory status at nearby stores. Furthermore, product reviews and usage instructions are also provided by the AI, allowing users to obtain detailed product information. This enables the product search system to easily search for products and check their availability.

[0029] The product search system according to this embodiment comprises a reception unit, a generation unit, and a confirmation unit. The reception unit receives product information from the user. Product information includes, but is not limited to, product names, barcodes, photos, and images. For example, the reception unit can allow the user to upload a photo of a product they saw on television. The generation unit uses a generation AI to analyze the information entered by the reception unit and identify the product. The generation AI can, for example, identify a product from an ambiguous product description. The generation unit can also use image recognition technology to identify a product from an uploaded image. For example, the generation AI identifies the product name, and the image recognition technology identifies the product. The confirmation unit checks the inventory status at nearby stores based on the product identified by the generation unit. The confirmation unit can, for example, display inventory information of nearby stores in real time based on the product name identified by the generation AI. The confirmation unit can also provide map display and route guidance. For example, the confirmation unit can display inventory information of nearby stores on a map, allowing the user to check the route to the store. As a result, the product search system according to this embodiment allows the user to input product information, identify the product using a generating AI, and check its inventory status.

[0030] The reception desk allows users to input product information. This information includes, but is not limited to, product names, barcodes, photos, and images. Specifically, users can use smartphones or computers to input product names as text, scan barcodes, or upload photos and images of products. For example, a user can upload a photo of a product they saw on television. The reception desk provides an intuitive and user-friendly interface to support these input methods. When users upload photos, guidelines regarding image resolution and format are displayed to help them select appropriate images. The barcode scanning function automatically adjusts camera focus and light intensity for smooth scanning. Furthermore, the reception desk temporarily stores the information entered by the user, allowing for later editing and additions. This allows users to input information on multiple products at once, enabling efficient product searches. The reception desk plays a role in quickly processing the entered information and sending it to the generation desk.

[0031] The generation unit uses a generation AI to analyze information entered by the reception unit and identify products. For example, the generation AI can identify products from vague product descriptions. Specifically, the generation AI uses natural language processing technology to analyze text information entered by the user and extract product names and features. For example, it can identify a specific smartphone model from a vague description such as "the latest model smartphone with a red cover." The generation unit can also identify products from uploaded images using image recognition technology. The image recognition technology uses a deep learning algorithm to extract features within the image and compare them with known product images in the database. For example, the generation AI identifies the product name, and the image recognition technology identifies the product. This allows the generation unit to integrate both text and image information to identify products more accurately. Furthermore, the generation unit also has a function to suggest related and similar products based on the information entered by the user. This allows the user to consider not only the desired product but also other options. The generation unit sends the identified product information to the confirmation unit and prepares it for checking the inventory status.

[0032] The verification unit checks the inventory status of nearby stores based on the products identified by the generation unit. For example, the verification unit can display real-time inventory information of nearby stores based on the product name identified by the generation AI. Specifically, the verification unit accesses the inventory database of each store to check whether a specific product is in stock. This includes linking with each store's inventory management system via API. The verification unit can also provide map display and route guidance. For example, the verification unit can display inventory information of nearby stores on a map, allowing the user to check the route to the store. For map display, it uses map services such as Google Maps® and OpenStreetMap® to calculate the optimal route from the user's current location to the nearest store. Furthermore, the verification unit ensures real-time information by increasing the frequency of inventory status updates, so that users can obtain the latest information. For example, if store inventory information changes, it is immediately reflected in the system. The verification unit also provides a function for users to reserve specific products, allowing for in-store pickup or delivery arrangements. This ensures that users can reliably obtain products and improves convenience. The verification unit supports a smooth purchasing experience by providing users with inventory information and route guidance.

[0033] The product search system includes an identification unit that identifies products using image recognition technology. The identification unit, for example, uses image recognition technology to identify products from uploaded product photos. For example, the identification unit can analyze images using a CNN (Convolutional Neural Network) to identify products. It can also classify images using an SVM (Support Vector Machine) to identify products. Furthermore, the identification unit can analyze images in real time using YOLO (You Only Look Once) to identify products. This allows for accurate product identification using image recognition technology. Some or all of the above-described processes in the identification unit may be performed using AI, or without AI. For example, the identification unit can input uploaded image data into a generating AI and have the generating AI perform product identification from the image data.

[0034] The product search system includes a display unit that displays inventory information of nearby stores in real time. The display unit, for example, displays inventory information of nearby stores in real time. For example, the display unit can periodically update store inventory information to provide users with the latest inventory status. The display unit can also set the frequency of inventory information updates, allowing users to check inventory information at their preferred time. Furthermore, the display unit can minimize the delay time of inventory information and display inventory status in real time. This allows users to quickly check inventory status by displaying inventory information of nearby stores in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input inventory information of nearby stores into a generating AI and have the generating AI perform the display of inventory information in real time.

[0035] The product search system includes a guidance unit that provides map display and route guidance. For example, the guidance unit can display the route from the user's current location to nearby stores on a map. It can also calculate the shortest route and guide the user to the most efficient route. Furthermore, the guidance unit can adjust the route considering traffic information to help the user arrive at the store while avoiding congestion. This allows users to easily confirm the route to the store by providing map display and route guidance. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's current location data into a generating AI and have the generating AI execute the optimal route guidance.

[0036] The product search system includes a generation unit that generates product reviews and usage instructions. The generation unit generates product reviews and usage instructions using, for example, a generation AI. For example, the generation unit can automatically generate product reviews using natural language generation technology. The generation unit can also generate product usage instructions using template-based generation technology. Furthermore, the generation unit can generate customized product reviews and usage instructions based on user input. This allows users to obtain detailed product information by generating product reviews and usage instructions. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user data into a generation AI and have the generation AI generate product reviews and usage instructions.

[0037] The reception desk can analyze the user's past search history and suggest the optimal input method. For example, the reception desk can automatically display product information that the user has frequently searched for in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest product information that the user will use at a specific time of day based on their past search history. In this way, the optimal input method can be suggested by analyzing the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's search history data into a generating AI and have the generating AI suggest the optimal input method.

[0038] The reception unit can filter product information based on the user's current areas of interest when the user enters product information. For example, the reception unit can prioritize displaying relevant product information based on the product categories the user has recently searched for. It can also prioritize displaying product information for a specific brand if the user is interested in that brand. Furthermore, if the reception unit is interested in products within a particular price range, it can prioritize displaying product information within that price range. This allows for the provision of highly relevant information by filtering product information based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0039] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when product information is entered. For example, the reception desk can prioritize displaying product information from stores near the user's current location. Furthermore, if the user is interested in a specific region, the reception desk can prioritize displaying product information from stores in that region. Additionally, if the user is traveling, the reception desk can prioritize displaying product information from stores in their travel destination. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0040] The reception desk can analyze the user's social media activity and input relevant information when product information is entered. For example, the reception desk can prioritize displaying product information that the user has "liked" on social media. It can also prioritize displaying product information from brands that the user follows. Furthermore, it can prioritize displaying product information that the user has shared on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0041] The generation unit can adjust the level of detail based on the importance of the product when identifying it. For example, in the case of expensive products, the generation unit can identify products based on detailed specifications. In addition, in the case of products used daily, the generation unit can identify products based on a concise description. Furthermore, in the case of new products, the generation unit can identify products based on the latest review information. This allows for the provision of appropriate information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product importance data into a generation AI and have the generation AI perform the adjustment of the level of detail.

[0042] The generation unit can apply different identification algorithms depending on the product category when identifying a product. For example, in the case of electronic devices, the generation unit can apply an algorithm that emphasizes specifications. In the case of fashion items, the generation unit can also apply an algorithm that emphasizes image recognition. Furthermore, in the case of food products, the generation unit can apply an algorithm that emphasizes ingredient information. By applying different identification algorithms depending on the product category, highly accurate product identification becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product category data into a generation AI and have the generation AI execute the application of the identification algorithm.

[0043] The generation unit can determine a specific priority based on the product's submission date when identifying a product. For example, in the case of a new product, the generation unit identifies it based on the latest information. Furthermore, in the case of a seasonal product, the generation unit can identify it based on seasonal information. In addition, in the case of a sale product, the generation unit can identify it based on discount information. This allows the system to provide appropriate products by determining a specific priority based on the product's submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product submission date data into a generation AI and have the generation AI perform the determination of a specific priority.

[0044] The generation unit can adjust a specific order based on the relevance of products when identifying them. For example, the generation unit can prioritize identifying products related to products the user has previously purchased. It can also prioritize identifying products related to products the user has searched for. Furthermore, the generation unit can prioritize identifying products in categories that the user is interested in. By adjusting a specific order based on the relevance of products, it can provide the user with products that are highly relevant to them. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product relevance data into a generation AI and have the generation AI perform the adjustment of a specific order.

[0045] The verification unit can predict the current inventory status by referring to past inventory data when checking inventory status. For example, the verification unit predicts the current inventory status based on past inventory data. The verification unit can also predict inventory fluctuations based on past sales data. Furthermore, the verification unit can predict the inventory status of seasonal products based on past seasonal data. In this way, the current inventory status can be predicted by referring to past inventory data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input past inventory data into a generating AI and have the generating AI perform a prediction of the current inventory status.

[0046] The verification unit can apply different verification methods to each product category when checking inventory status. For example, in the case of electronic devices, the verification unit can apply an inventory verification method that emphasizes specification information. In the case of fashion items, the verification unit can also apply an inventory verification method that emphasizes size information. Furthermore, in the case of food products, the verification unit can apply an inventory verification method that emphasizes expiration date information. By applying different verification methods to each product category, highly accurate inventory verification becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input product category data into a generating AI and have the generating AI execute the application of the verification method.

[0047] The verification unit can analyze inventory changes based on the product submission date when checking inventory status. For example, in the case of new products, the verification unit analyzes the inventory status based on the latest inventory information. In addition, in the case of seasonal products, the verification unit can analyze the inventory status based on seasonal inventory information. Furthermore, in the case of sale products, the verification unit can analyze the inventory status based on discount information. This allows for the provision of appropriate inventory information by analyzing inventory changes based on the product submission date. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input product submission date data into a generating AI and have the generating AI perform an analysis of inventory changes.

[0048] The verification unit can analyze inventory by referring to relevant market data for the product when checking inventory status. For example, the verification unit can analyze inventory status based on relevant market data. The verification unit can also analyze inventory status based on the inventory data of competitors. Furthermore, the verification unit can analyze inventory status based on market trend data. This allows for a more accurate analysis of inventory status by referring to relevant market data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input relevant market data into a generating AI and have the generating AI perform the inventory analysis.

[0049] The identification unit can improve the accuracy of identification by considering the interrelationships between products during the identification process. For example, the identification unit can improve the accuracy of identification based on information about related products. It can also improve the accuracy of identification based on product information within the same category. Furthermore, the identification unit can improve the accuracy of identification based on the user's past purchase history. This improves the accuracy of identification by considering the interrelationships between products. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input interrelationship data between products into a generating AI and have the generating AI perform the task of improving the identification accuracy.

[0050] The identification unit can perform identification by considering the attribute information of the product submitter. For example, the identification unit can perform identification based on the submitter's age information. It can also perform identification based on the submitter's gender information. Furthermore, the identification unit can perform identification based on the submitter's regional information. This improves the accuracy of identification by considering the submitter's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the identification.

[0051] The identification unit can perform identification while considering the geographical distribution of products. For example, the identification unit can prioritize identifying information about products close to the user's current location. It can also prioritize identifying information about products related to a specific region. Furthermore, if the user is traveling, the identification unit can prioritize identifying information about products in the user's travel destination. This improves the accuracy of identification by considering the geographical distribution of products. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data of products into a generating AI and have the generating AI perform the identification.

[0052] The identification unit can improve the accuracy of its identification by referring to relevant literature on the product during the identification process. For example, the identification unit can improve the accuracy of its identification based on relevant literature. It can also improve the accuracy of its identification based on information on competing products. Furthermore, the identification unit can improve the accuracy of its identification based on market trend data. As a result, the accuracy of identification is improved by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the identification accuracy.

[0053] The display unit can select the optimal display method when displaying inventory information by referring to the user's past operation history. For example, the display unit can provide the optimal display method based on the display methods the user has used in the past. The display unit can also select a display method with high visibility from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0054] The display unit can select the optimal display method when displaying inventory information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0055] The guidance unit can suggest the optimal route by referring to the user's past travel history when providing route guidance. For example, the guidance unit can suggest the optimal route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, the guidance unit can analyze the user's past travel history and suggest the most efficient route. This allows the guidance unit to suggest the optimal route by referring to the user's past travel history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's travel history data into a generating AI and have the generating AI suggest the optimal route.

[0056] The guidance unit can propose the optimal route when providing route guidance, taking into account the user's geographical location information. For example, the guidance unit can propose the optimal route based on the user's current location. Furthermore, if the user is interested in a particular region, the guidance unit can prioritize routes within that region. Additionally, if the user is traveling, the guidance unit can prioritize routes within their travel destination. This allows the guidance unit to propose the optimal route by considering the user's geographical location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location data into a generating AI and have the generating AI propose the optimal route.

[0057] The generation unit can select the optimal generation method by referring to the user's past review history when generating product reviews and usage instructions. For example, the generation unit can select the optimal generation method based on reviews previously posted by the user. The generation unit can also generate highly visible reviews from the user's past review history. Furthermore, the generation unit can analyze the user's past review history and select the most efficient generation method. In this way, the optimal generation method can be provided by referring to the user's past review history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's review history data into a generation AI and have the generation AI select the optimal generation method.

[0058] The generation unit can select the optimal generation method when generating product reviews and usage instructions, taking into account the user's device information. For example, if the user is using a smartphone, the generation unit will generate a review adapted to the screen size. Furthermore, if the user is using a tablet, the generation unit can generate a review optimized for a larger screen. Additionally, if the user is using a smartwatch, the generation unit can generate a concise and highly visible review. This allows the generation unit to provide the optimal method by considering the user's device information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user device information data into a generation AI and have the generation AI select the optimal generation method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze user voice input and input product information using speech recognition technology. For example, if a user voice-inputs a product name, the speech recognition technology converts the voice into text and inputs it into the reception desk as product information. Furthermore, if a user voice-introduces a product's features, the voice data can be analyzed and used by a generating AI to identify the product. If the voice input is ambiguous, the reception desk can ask the user additional questions to gather more specific information. This improves the convenience of inputting product information without using hands by utilizing voice input.

[0061] The identification unit can refer to the user's past purchase history and prioritize identifying similar products. For example, it can prioritize identifying products in the same category as products the user has previously purchased. It can also prioritize identifying products similar to products the user has previously given high ratings to. Furthermore, it can prioritize identifying products from brands the user has frequently purchased in the past. This allows for the rapid identification of products that match the user's preferences by referring to past purchase history.

[0062] The display unit can adjust the inventory information display layout according to the screen size of the user's device. For example, when using a smartphone, the information is displayed compactly to fit the screen size. When using a tablet, the information can be displayed in a layout optimized for the larger screen. Furthermore, when using a desktop computer, a layout that displays multiple pieces of information simultaneously can be provided. This improves visibility and usability by providing the optimal display layout for the user's device.

[0063] The guidance system can adjust route guidance according to the user's mode of transportation. For example, if the user is traveling on foot, it will guide them along a pedestrian-only route. If the user is using a bicycle, it can guide them along a bicycle-only route. Furthermore, if the user is using a car, it can provide route guidance that includes parking information. This improves the convenience of travel by providing optimal route guidance tailored to the user's mode of transportation.

[0064] The generation unit can analyze a user's purchase history and generate relevant product reviews and usage instructions. For example, it can generate reviews related to products a user has previously purchased. It can also suggest usage instructions for newly purchased products based on how the user has used previously purchased products. Furthermore, it can generate reviews for similar products by referring to reviews of products that the user has previously given high ratings to. This allows the system to leverage the user's purchase history to provide more relevant information.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives product information from the user. This information includes, for example, the product name, barcode, photos, and images. The user can, for example, upload a photo of a product they saw on television. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and identify the product. The generation AI can identify products from ambiguous product descriptions and can also identify products from uploaded images using image recognition technology. Step 3: The verification unit checks the inventory status at nearby stores based on the products identified by the generation unit. Based on the product names identified by the generation AI, the verification unit can display inventory information at nearby stores in real time and also provide map display and route guidance.

[0067] (Example of form 2) The product search system according to an embodiment of the present invention is a system that allows users to search whether a product they saw on television or the internet is sold at a nearby store. This system allows users to input either a product name, barcode, photograph, or image, and uses a generation AI and image recognition technology to identify the product and check its inventory status at nearby stores. For example, a user uploads a photograph of a product they saw on television. This information is input to the generation AI and image recognition technology. Next, the generation AI and image recognition technology analyze the input information. The generation AI identifies the product from ambiguous product descriptions, and the image recognition technology identifies the product from the uploaded image. For example, based on a photograph of a product uploaded by the user, the generation AI identifies the product name, and the image recognition technology identifies the product. Based on the identified product, the system checks the inventory status at nearby stores. For example, based on the product name identified by the generation AI, the system displays inventory information for nearby stores in real time. Map display and route guidance are also provided. This allows users to easily search for products and check their inventory status. For example, a user can search for a product they saw on television using a photograph and check its inventory status at nearby stores. Furthermore, product reviews and usage instructions are also provided by the AI, allowing users to obtain detailed product information. This enables the product search system to easily search for products and check their availability.

[0068] The product search system according to this embodiment comprises a reception unit, a generation unit, and a confirmation unit. The reception unit receives product information from the user. Product information includes, but is not limited to, product names, barcodes, photos, and images. For example, the reception unit can allow the user to upload a photo of a product they saw on television. The generation unit uses a generation AI to analyze the information entered by the reception unit and identify the product. The generation AI can, for example, identify a product from an ambiguous product description. The generation unit can also use image recognition technology to identify a product from an uploaded image. For example, the generation AI identifies the product name, and the image recognition technology identifies the product. The confirmation unit checks the inventory status at nearby stores based on the product identified by the generation unit. The confirmation unit can, for example, display inventory information of nearby stores in real time based on the product name identified by the generation AI. The confirmation unit can also provide map display and route guidance. For example, the confirmation unit can display inventory information of nearby stores on a map, allowing the user to check the route to the store. As a result, the product search system according to this embodiment allows the user to input product information, identify the product using a generating AI, and check its inventory status.

[0069] The reception desk allows users to input product information. This information includes, but is not limited to, product names, barcodes, photos, and images. Specifically, users can use smartphones or computers to input product names as text, scan barcodes, or upload photos and images of products. For example, a user can upload a photo of a product they saw on television. The reception desk provides an intuitive and user-friendly interface to support these input methods. When users upload photos, guidelines regarding image resolution and format are displayed to help them select appropriate images. The barcode scanning function automatically adjusts camera focus and light intensity for smooth scanning. Furthermore, the reception desk temporarily stores the information entered by the user, allowing for later editing and additions. This allows users to input information on multiple products at once, enabling efficient product searches. The reception desk plays a role in quickly processing the entered information and sending it to the generation desk.

[0070] The generation unit uses a generation AI to analyze information entered by the reception unit and identify products. For example, the generation AI can identify products from vague product descriptions. Specifically, the generation AI uses natural language processing technology to analyze text information entered by the user and extract product names and features. For example, it can identify a specific smartphone model from a vague description such as "the latest model smartphone with a red cover." The generation unit can also identify products from uploaded images using image recognition technology. The image recognition technology uses a deep learning algorithm to extract features within the image and compare them with known product images in the database. For example, the generation AI identifies the product name, and the image recognition technology identifies the product. This allows the generation unit to integrate both text and image information to identify products more accurately. Furthermore, the generation unit also has a function to suggest related and similar products based on the information entered by the user. This allows the user to consider not only the desired product but also other options. The generation unit sends the identified product information to the confirmation unit and prepares it for checking the inventory status.

[0071] The verification unit checks the inventory status of nearby stores based on the products identified by the generation unit. For example, the verification unit can display real-time inventory information of nearby stores based on the product name identified by the generation AI. Specifically, the verification unit accesses the inventory database of each store to check whether a specific product is in stock. This includes linking with each store's inventory management system via API. The verification unit can also provide map display and route guidance. For example, the verification unit can display inventory information of nearby stores on a map, allowing the user to check the route to the store. For map display, it uses map services such as Google Maps or OpenStreetMap to calculate the optimal route from the user's current location to the nearest store. Furthermore, the verification unit ensures real-time information by increasing the frequency of inventory status updates, so that users can get the latest information. For example, if store inventory information changes, it is immediately reflected in the system. The verification unit also provides a function for users to reserve specific products, allowing them to reserve items at the store or arrange for delivery. This ensures that users can obtain products reliably and improves convenience. The verification unit supports a smooth purchasing experience by providing users with inventory information and route guidance.

[0072] The product search system includes an identification unit that identifies products using image recognition technology. The identification unit, for example, uses image recognition technology to identify products from uploaded product photos. For example, the identification unit can analyze images using a CNN (Convolutional Neural Network) to identify products. It can also classify images using an SVM (Support Vector Machine) to identify products. Furthermore, the identification unit can analyze images in real time using YOLO (You Only Look Once) to identify products. This allows for accurate product identification using image recognition technology. Some or all of the above-described processes in the identification unit may be performed using AI, or without AI. For example, the identification unit can input uploaded image data into a generating AI and have the generating AI perform product identification from the image data.

[0073] The product search system includes a display unit that displays inventory information of nearby stores in real time. The display unit, for example, displays inventory information of nearby stores in real time. For example, the display unit can periodically update store inventory information to provide users with the latest inventory status. The display unit can also set the frequency of inventory information updates, allowing users to check inventory information at their preferred time. Furthermore, the display unit can minimize the delay time of inventory information and display inventory status in real time. This allows users to quickly check inventory status by displaying inventory information of nearby stores in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input inventory information of nearby stores into a generating AI and have the generating AI perform the display of inventory information in real time.

[0074] The product search system includes a guidance unit that provides map display and route guidance. For example, the guidance unit can display the route from the user's current location to nearby stores on a map. It can also calculate the shortest route and guide the user to the most efficient route. Furthermore, the guidance unit can adjust the route considering traffic information to help the user arrive at the store while avoiding congestion. This allows users to easily confirm the route to the store by providing map display and route guidance. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's current location data into a generating AI and have the generating AI execute the optimal route guidance.

[0075] The product search system includes a generation unit that generates product reviews and usage instructions. The generation unit generates product reviews and usage instructions using, for example, a generation AI. For example, the generation unit can automatically generate product reviews using natural language generation technology. The generation unit can also generate product usage instructions using template-based generation technology. Furthermore, the generation unit can generate customized product reviews and usage instructions based on user input. This allows users to obtain detailed product information by generating product reviews and usage instructions. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user data into a generation AI and have the generation AI generate product reviews and usage instructions.

[0076] The reception desk can estimate the user's emotions and adjust the method of inputting product information based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of product information. This improves user convenience by adjusting the method of inputting product information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can analyze the user's past search history and suggest the optimal input method. For example, the reception desk can automatically display product information that the user has frequently searched for in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest product information that the user will use at a specific time of day based on their past search history. In this way, the optimal input method can be suggested by analyzing the user's past search history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's search history data into a generating AI and have the generating AI suggest the optimal input method.

[0078] The reception unit can filter product information based on the user's current areas of interest when the user enters product information. For example, the reception unit can prioritize displaying relevant product information based on the product categories the user has recently searched for. It can also prioritize displaying product information for a specific brand if the user is interested in that brand. Furthermore, if the reception unit is interested in products within a particular price range, it can prioritize displaying product information within that price range. This allows for the provision of highly relevant information by filtering product information based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0079] The reception desk can estimate the user's emotions and determine the priority of product information to be entered based on the estimated emotions. For example, if the user is excited, the reception desk may prioritize displaying popular product information. It may also prioritize displaying detailed product information if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize displaying immediately available product information. This allows for the provision of information tailored to the user's needs by prioritizing product information according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of product information.

[0080] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when product information is entered. For example, the reception desk can prioritize displaying product information from stores near the user's current location. Furthermore, if the user is interested in a specific region, the reception desk can prioritize displaying product information from stores in that region. Additionally, if the user is traveling, the reception desk can prioritize displaying product information from stores in their travel destination. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting highly relevant information.

[0081] The reception desk can analyze the user's social media activity and input relevant information when product information is entered. For example, the reception desk can prioritize displaying product information that the user has "liked" on social media. It can also prioritize displaying product information from brands that the user follows. Furthermore, it can prioritize displaying product information that the user has shared on social media. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0082] The generation unit can estimate the user's emotions and adjust the method of identifying products based on the estimated emotions. For example, if the user is relaxed, the generation unit can identify products based on detailed product descriptions. If the user is in a hurry, the generation unit can also identify products based on concise product descriptions. Furthermore, if the user is excited, the generation unit can prioritize identifying visually appealing products. This allows for the identification of products that meet the user's needs by adjusting the product identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the product identification method.

[0083] The generation unit can adjust the level of detail based on the importance of the product when identifying it. For example, in the case of expensive products, the generation unit can identify products based on detailed specifications. In addition, in the case of products used daily, the generation unit can identify products based on a concise description. Furthermore, in the case of new products, the generation unit can identify products based on the latest review information. This allows for the provision of appropriate information by adjusting the level of detail based on the importance of the product. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product importance data into a generation AI and have the generation AI perform the adjustment of the level of detail.

[0084] The generation unit can apply different identification algorithms depending on the product category when identifying a product. For example, in the case of electronic devices, the generation unit can apply an algorithm that emphasizes specifications. In the case of fashion items, the generation unit can also apply an algorithm that emphasizes image recognition. Furthermore, in the case of food products, the generation unit can apply an algorithm that emphasizes ingredient information. By applying different identification algorithms depending on the product category, highly accurate product identification becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product category data into a generation AI and have the generation AI execute the application of the identification algorithm.

[0085] The generation unit can estimate the user's emotions and determine specific priorities for the products to generate based on the estimated emotions. For example, if the user is excited, the generation unit will prioritize popular products. If the user is relaxed, the generation unit can also prioritize products based on detailed product information. Furthermore, if the user is in a hurry, the generation unit can prioritize products that are immediately available for purchase. This allows the system to provide products that meet the user's needs by determining specific priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine specific priorities for products.

[0086] The generation unit can determine a specific priority based on the product's submission date when identifying a product. For example, in the case of a new product, the generation unit identifies it based on the latest information. Furthermore, in the case of a seasonal product, the generation unit can identify it based on seasonal information. In addition, in the case of a sale product, the generation unit can identify it based on discount information. This allows the system to provide appropriate products by determining a specific priority based on the product's submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product submission date data into a generation AI and have the generation AI perform the determination of a specific priority.

[0087] The generation unit can adjust a specific order based on the relevance of products when identifying them. For example, the generation unit can prioritize identifying products related to products the user has previously purchased. It can also prioritize identifying products related to products the user has searched for. Furthermore, the generation unit can prioritize identifying products in categories that the user is interested in. By adjusting a specific order based on the relevance of products, it can provide the user with products that are highly relevant to them. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input product relevance data into a generation AI and have the generation AI perform the adjustment of a specific order.

[0088] The confirmation unit can estimate the user's emotions and adjust the inventory status confirmation method based on the estimated emotions. For example, if the user is nervous, the confirmation unit can display a simple and highly visible inventory status. If the user is relaxed, the confirmation unit can also display detailed inventory information. Furthermore, if the user is in a hurry, the confirmation unit can display concise inventory information. This improves user convenience by adjusting the inventory status confirmation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI, or not using AI. For example, the confirmation unit can input user emotion data into the generative AI and have the generative AI adjust the inventory status confirmation method.

[0089] The verification unit can predict the current inventory status by referring to past inventory data when checking inventory status. For example, the verification unit predicts the current inventory status based on past inventory data. The verification unit can also predict inventory fluctuations based on past sales data. Furthermore, the verification unit can predict the inventory status of seasonal products based on past seasonal data. In this way, the current inventory status can be predicted by referring to past inventory data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input past inventory data into a generating AI and have the generating AI perform a prediction of the current inventory status.

[0090] The verification unit can apply different verification methods to each product category when checking inventory status. For example, in the case of electronic devices, the verification unit can apply an inventory verification method that emphasizes specification information. In the case of fashion items, the verification unit can also apply an inventory verification method that emphasizes size information. Furthermore, in the case of food products, the verification unit can apply an inventory verification method that emphasizes expiration date information. By applying different verification methods to each product category, highly accurate inventory verification becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input product category data into a generating AI and have the generating AI execute the application of the verification method.

[0091] The confirmation unit can estimate the user's emotions and adjust the importance of inventory status based on the estimated emotions. For example, if the user is excited, the confirmation unit can prioritize displaying the inventory status of popular items. It can also display detailed inventory information if the user is relaxed. Furthermore, if the user is in a hurry, the confirmation unit can prioritize displaying the inventory status of items available for immediate purchase. This allows for the provision of information tailored to the user's needs by adjusting the importance of inventory status according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the confirmation unit may be performed using AI or not. For example, the confirmation unit can input user emotion data into a generative AI and have the generative AI adjust the importance of inventory status.

[0092] The verification unit can analyze inventory changes based on the product submission date when checking inventory status. For example, in the case of new products, the verification unit analyzes the inventory status based on the latest inventory information. In addition, in the case of seasonal products, the verification unit can analyze the inventory status based on seasonal inventory information. Furthermore, in the case of sale products, the verification unit can analyze the inventory status based on discount information. This allows for the provision of appropriate inventory information by analyzing inventory changes based on the product submission date. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input product submission date data into a generating AI and have the generating AI perform an analysis of inventory changes.

[0093] The verification unit can analyze inventory by referring to relevant market data for the product when checking inventory status. For example, the verification unit can analyze inventory status based on relevant market data. The verification unit can also analyze inventory status based on the inventory data of competitors. Furthermore, the verification unit can analyze inventory status based on market trend data. This allows for a more accurate analysis of inventory status by referring to relevant market data. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input relevant market data into a generating AI and have the generating AI perform the inventory analysis.

[0094] The identification unit can estimate the user's emotions and adjust its identification method based on the estimated emotions. For example, if the user is relaxed, the identification unit can identify based on detailed product information. If the user is in a hurry, the identification unit can also identify based on concise product information. Furthermore, if the user is excited, the identification unit can prioritize identifying visually appealing products. This improves the accuracy of identification by adjusting the identification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into the generative AI and have the generative AI adjust the identification method.

[0095] The identification unit can improve the accuracy of identification by considering the interrelationships between products during the identification process. For example, the identification unit can improve the accuracy of identification based on information about related products. It can also improve the accuracy of identification based on product information within the same category. Furthermore, the identification unit can improve the accuracy of identification based on the user's past purchase history. This improves the accuracy of identification by considering the interrelationships between products. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input interrelationship data between products into a generating AI and have the generating AI perform the task of improving the identification accuracy.

[0096] The identification unit can perform identification by considering the attribute information of the product submitter. For example, the identification unit can perform identification based on the submitter's age information. It can also perform identification based on the submitter's gender information. Furthermore, the identification unit can perform identification based on the submitter's regional information. This improves the accuracy of identification by considering the submitter's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the identification.

[0097] The identification unit can estimate the user's emotions and adjust the order in which the identification results are displayed based on the estimated emotions. For example, if the user is excited, the identification unit may prioritize displaying popular products. It can also prioritize displaying detailed product information if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize displaying products available for immediate purchase. This allows for the provision of information tailored to the user's needs by adjusting the order in which the identification results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using AI, or not. For example, the identification unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the identification results.

[0098] The identification unit can perform identification while considering the geographical distribution of products. For example, the identification unit can prioritize identifying information about products close to the user's current location. It can also prioritize identifying information about products related to a specific region. Furthermore, if the user is traveling, the identification unit can prioritize identifying information about products in the user's travel destination. This improves the accuracy of identification by considering the geographical distribution of products. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data of products into a generating AI and have the generating AI perform the identification.

[0099] The identification unit can improve the accuracy of its identification by referring to relevant literature on the product during the identification process. For example, the identification unit can improve the accuracy of its identification based on relevant literature. It can also improve the accuracy of its identification based on information on competing products. Furthermore, the identification unit can improve the accuracy of its identification based on market trend data. As a result, the accuracy of identification is improved by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the identification accuracy.

[0100] The display unit can estimate the user's emotions and adjust the way inventory information is displayed based on the estimated emotions. For example, if the user is stressed, the display unit can provide a simple and highly visible display. If the user is relaxed, the display unit can also display detailed inventory information. Furthermore, if the user is in a hurry, the display unit can display concise inventory information. This improves user convenience by adjusting the way inventory information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI adjust the way inventory information is displayed.

[0101] The display unit can select the optimal display method when displaying inventory information by referring to the user's past operation history. For example, the display unit can provide the optimal display method based on the display methods the user has used in the past. The display unit can also select a display method with high visibility from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history and provide the most efficient display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0102] The display unit can estimate the user's emotions and adjust the inventory information display procedure based on the estimated emotions. For example, if the user is excited, the display unit may prioritize displaying inventory information for popular items. It can also display detailed inventory information if the user is relaxed. Furthermore, if the user is in a hurry, the display unit may prioritize displaying inventory information for items available for immediate purchase. This allows the system to provide information tailored to the user's needs by adjusting the inventory information display procedure according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI adjust the inventory information display procedure.

[0103] The display unit can select the optimal display method when displaying inventory information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0104] The guidance unit can estimate the user's emotions and adjust the route guidance method based on the estimated emotions. For example, if the user is nervous, the guidance unit can provide simple and easy-to-understand route guidance. If the user is relaxed, the guidance unit can also provide detailed route guidance. Furthermore, if the user is in a hurry, the guidance unit can provide concise route guidance. By adjusting the route guidance method according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or not using AI. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI adjust the route guidance method.

[0105] The guidance unit can suggest the optimal route by referring to the user's past travel history when providing route guidance. For example, the guidance unit can suggest the optimal route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, the guidance unit can analyze the user's past travel history and suggest the most efficient route. This allows the guidance unit to suggest the optimal route by referring to the user's past travel history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's travel history data into a generating AI and have the generating AI suggest the optimal route.

[0106] The guidance unit can estimate the user's emotions and determine the priority of route guidance based on the estimated emotions. For example, if the user is excited, the guidance unit will prioritize popular routes. If the user is relaxed, the guidance unit can also provide detailed route guidance. Furthermore, if the user is in a hurry, the guidance unit can prioritize the shortest route. This allows for guidance tailored to the user's needs by determining the priority of route guidance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI or not. For example, the guidance unit can input user emotion data into a generative AI and have the generative AI determine the priority of route guidance.

[0107] The guidance unit can propose the optimal route when providing route guidance, taking into account the user's geographical location information. For example, the guidance unit can propose the optimal route based on the user's current location. Furthermore, if the user is interested in a particular region, the guidance unit can prioritize routes within that region. Additionally, if the user is traveling, the guidance unit can prioritize routes within their travel destination. This allows the guidance unit to propose the optimal route by considering the user's geographical location information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location data into a generating AI and have the generating AI propose the optimal route.

[0108] The generation unit can estimate the user's emotions and adjust the generation method for product reviews and usage instructions based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed product review. If the user is in a hurry, the generation unit can also generate a concise product review. Furthermore, if the user is excited, the generation unit can generate a visually appealing product review. In this way, by adjusting the generation method for product reviews and usage instructions according to the user's emotions, information tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the generation method for product reviews and usage instructions.

[0109] The generation unit can select the optimal generation method by referring to the user's past review history when generating product reviews and usage instructions. For example, the generation unit can select the optimal generation method based on reviews previously posted by the user. The generation unit can also generate highly visible reviews from the user's past review history. Furthermore, the generation unit can analyze the user's past review history and select the most efficient generation method. In this way, the optimal generation method can be provided by referring to the user's past review history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's review history data into a generation AI and have the generation AI select the optimal generation method.

[0110] The generation unit can estimate the user's emotions and prioritize product reviews and usage instructions based on those emotions. For example, if the user is excited, the generation unit may prioritize displaying popular product reviews. It can also prioritize displaying detailed product reviews if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize displaying concise product reviews. This allows for the provision of information tailored to the user's needs by prioritizing product reviews and usage instructions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of product reviews and usage instructions.

[0111] The generation unit can select the optimal generation method when generating product reviews and usage instructions, taking into account the user's device information. For example, if the user is using a smartphone, the generation unit will generate a review adapted to the screen size. Furthermore, if the user is using a tablet, the generation unit can generate a review optimized for a larger screen. Additionally, if the user is using a smartwatch, the generation unit can generate a concise and highly visible review. This allows the generation unit to provide the optimal method by considering the user's device information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user device information data into a generation AI and have the generation AI select the optimal generation method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can analyze user voice input and input product information using speech recognition technology. For example, if a user voice-inputs a product name, the speech recognition technology converts the voice into text and inputs it into the reception desk as product information. Furthermore, if a user voice-introduces a product's features, the voice data can be analyzed and used by a generating AI to identify the product. If the voice input is ambiguous, the reception desk can ask the user additional questions to gather more specific information. This improves the convenience of inputting product information without using hands by utilizing voice input.

[0114] The identification unit can refer to the user's past purchase history and prioritize identifying similar products. For example, it can prioritize identifying products in the same category as products the user has previously purchased. It can also prioritize identifying products similar to products the user has previously given high ratings to. Furthermore, it can prioritize identifying products from brands the user has frequently purchased in the past. This allows for the rapid identification of products that match the user's preferences by referring to past purchase history.

[0115] The display unit can adjust the inventory information display layout according to the screen size of the user's device. For example, when using a smartphone, the information is displayed compactly to fit the screen size. When using a tablet, the information can be displayed in a layout optimized for the larger screen. Furthermore, when using a desktop computer, a layout that displays multiple pieces of information simultaneously can be provided. This improves visibility and usability by providing the optimal display layout for the user's device.

[0116] The guidance system can adjust route guidance according to the user's mode of transportation. For example, if the user is traveling on foot, it will guide them along a pedestrian-only route. If the user is using a bicycle, it can guide them along a bicycle-only route. Furthermore, if the user is using a car, it can provide route guidance that includes parking information. This improves the convenience of travel by providing optimal route guidance tailored to the user's mode of transportation.

[0117] The generation unit can analyze a user's purchase history and generate relevant product reviews and usage instructions. For example, it can generate reviews related to products a user has previously purchased. It can also suggest usage instructions for newly purchased products based on how the user has used previously purchased products. Furthermore, it can generate reviews for similar products by referring to reviews of products that the user has previously given high ratings to. This allows the system to leverage the user's purchase history to provide more relevant information.

[0118] The reception desk can estimate the user's emotions and adjust the product information input method based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick product information entry. This improves user convenience by adjusting the product information input method according to the user's emotions.

[0119] The generation unit can estimate the user's emotions and adjust the method of identifying products based on those emotions. For example, if the user is relaxed, it can identify products based on detailed product descriptions. If the user is in a hurry, it can identify products based on concise product descriptions. Furthermore, if the user is excited, it can prioritize identifying visually appealing products. By adjusting the product identification method according to the user's emotions, it is possible to identify products that meet the user's needs.

[0120] The confirmation unit can estimate the user's emotions and adjust the inventory status confirmation method based on those emotions. For example, if the user is stressed, it can display a simple and highly visible inventory status. If the user is relaxed, it can display detailed inventory information. Furthermore, if the user is in a hurry, it can display concise inventory information. By adjusting the inventory status confirmation method according to the user's emotions, user convenience is improved.

[0121] The identification unit can estimate the user's emotions and adjust its identification method based on those emotions. For example, if the user is relaxed, it can identify products based on detailed product information. If the user is in a hurry, it can identify products based on concise product information. Furthermore, if the user is excited, it can prioritize identifying visually appealing products. By adjusting the identification method according to the user's emotions, the accuracy of the identification is improved.

[0122] The guidance system can estimate the user's emotions and adjust the route guidance method based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand route guidance. If the user is relaxed, it can provide detailed route guidance. Furthermore, if the user is in a hurry, it can provide concise route guidance. By adjusting the route guidance method according to the user's emotions, user convenience is improved.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives product information from the user. This information includes, for example, the product name, barcode, photos, and images. The user can, for example, upload a photo of a product they saw on television. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and identify the product. The generation AI can identify products from ambiguous product descriptions and can also identify products from uploaded images using image recognition technology. Step 3: The verification unit checks the inventory status at nearby stores based on the products identified by the generation unit. Based on the product names identified by the generation AI, the verification unit can display inventory information at nearby stores in real time and also provide map display and route guidance.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] For example, the reception unit allows users to input product information using the reception device 38 of the smart device 14. The generation unit performs product identification processing using generation AI via the identification processing unit 290 of the data processing device 12. The confirmation unit checks the inventory status of nearby stores using the identification processing unit 290 of the data processing device 12 and displays it in real time using the output device 40 of the smart device 14. The identification unit takes a photo of the uploaded product using the camera 42 of the smart device 14 and identifies the product using image recognition technology via the identification processing unit 290 of the data processing device 12. The display unit displays inventory information of nearby stores in real time using the display 40A of the smart device 14. The guidance unit provides map display and route guidance using the output device 40 of the smart device 14. The generation unit generates product reviews and usage instructions using generation AI via the identification processing unit 290 of the data processing device 12 and displays them using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] For example, the reception unit allows users to input product information using the microphone 238 of the smart glasses 214. The generation unit performs product identification processing using generation AI via the identification processing unit 290 of the data processing device 12. The confirmation unit checks the inventory status of nearby stores using the identification processing unit 290 of the data processing device 12 and displays it in real time using the speaker 240 of the smart glasses 214. The identification unit takes a photo of the uploaded product using the camera 42 of the smart glasses 214 and identifies the product using image recognition technology via the identification processing unit 290 of the data processing device 12. The display unit displays inventory information of nearby stores in real time using the display of the smart glasses 214. The guidance unit provides map display and route guidance using the speaker 240 of the smart glasses 214. The generation unit generates product reviews and usage instructions using generation AI via the identification processing unit 290 of the data processing device 12 and displays them using the display of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] For example, the reception unit allows users to input product information using the microphone 238 of the headset terminal 314. The generation unit performs product identification processing using the generation AI via the identification processing unit 290 of the data processing device 12. The confirmation unit checks the inventory status of nearby stores using the identification processing unit 290 of the data processing device 12 and displays it in real time using the speaker 240 of the headset terminal 314. The identification unit takes a photo of the uploaded product using the camera 42 of the headset terminal 314 and identifies the product using image recognition technology via the identification processing unit 290 of the data processing device 12. The display unit displays the inventory information of nearby stores in real time using the display 343 of the headset terminal 314. The guidance unit provides map display and route guidance using the speaker 240 of the headset terminal 314. The generation unit generates product reviews and usage instructions using the generation AI via the identification processing unit 290 of the data processing device 12 and displays them using the display 343 of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] For example, the reception unit allows users to input product information using the microphone 238 of the robot 414. The generation unit performs product identification processing using the generation AI via the identification processing unit 290 of the data processing device 12. The confirmation unit checks the inventory status of nearby stores using the identification processing unit 290 of the data processing device 12 and displays it in real time using the speaker 240 of the robot 414. The identification unit takes a picture of the uploaded product using the camera 42 of the robot 414 and identifies the product using image recognition technology via the identification processing unit 290 of the data processing device 12. The display unit displays inventory information of nearby stores in real time using the display of the robot 414. The guidance unit provides map display and route guidance using the speaker 240 of the robot 414. The generation unit generates product reviews and usage instructions using the generation AI via the identification processing unit 290 of the data processing device 12 and displays them using the display of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A reception area for entering product information, A generation unit analyzes the information entered by the reception unit and identifies the product, A confirmation unit that checks the inventory status based on the products identified by the generation unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with an identification unit that identifies products using image recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a display unit that shows inventory information for nearby stores in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a guidance unit that provides map display and route guidance. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features an algorithm that generates product reviews and usage instructions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the product information input method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past search history and suggests appropriate input methods. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering product information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of product information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering product information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering product information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate user sentiment and adjust the method of identifying products to generate based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When identifying a product, adjust the level of detail based on the product's importance. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When identifying a product, a different identification algorithm is applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and determines specific priorities for the products to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When identifying products, a specific priority is determined based on the product submission date. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When identifying products, adjust the order based on product relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned verification unit is The system estimates user sentiment and adjusts how inventory status is checked based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned verification unit is When checking inventory status, past inventory data is used to predict the current inventory status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is When checking inventory status, different checking methods are applied for each product category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is It estimates user sentiment and adjusts the importance of inventory status based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is When checking inventory status, analyze changes in inventory based on when the products were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is When checking inventory status, analyze inventory by referring to relevant market data for the product. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned identification unit is It estimates the user's emotions and adjusts the identification method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned identification unit is When identifying products, consider their interrelationships to improve identification accuracy. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned identification unit is During identification, the attribute information of the product submitter is taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned identification unit is It estimates the user's sentiment and adjusts the order in which the identification results are displayed based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned identification unit is When identifying products, the geographical distribution of the products is taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned identification unit is During identification, we improve the accuracy of identification by referring to relevant literature on the product. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned display unit is The system estimates user sentiment and adjusts how inventory information is displayed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying inventory information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned display unit is The system estimates the user's emotions and adjusts the inventory information display procedure based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned display unit is When displaying inventory information, the system selects the optimal display method considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned guide section is The system estimates the user's emotions and adjusts the route guidance method based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned guide section is When providing route guidance, the system suggests the optimal route by referencing the user's past travel history. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned guide section is The system estimates the user's emotions and determines the priority of route guidance based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned guide section is When providing route guidance, the system proposes the optimal route considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 38) The generating unit is We estimate user sentiment and adjust how product reviews and usage instructions are generated based on that estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 39) The generating unit is When generating product reviews and usage instructions, the system selects the optimal generation method by referring to the user's past review history. The system described in Appendix 5, characterized by the features described herein. (Note 40) The generating unit is It estimates user sentiment and prioritizes product reviews and usage instructions based on that estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 41) The generating unit is When generating product reviews and usage instructions, the system selects the optimal generation method by considering the user's device information. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area for entering product information, A generation unit analyzes the information entered by the reception unit and identifies the product, A confirmation unit that checks the inventory status based on the products identified by the generation unit, Equipped with A system characterized by the following features.

2. It is equipped with an identification unit that identifies products using image recognition technology. The system according to feature 1.

3. It features a display unit that shows inventory information for nearby stores in real time. The system according to feature 1.

4. It is equipped with a guidance unit that provides map display and route guidance. The system according to feature 1.

5. It features an algorithm that generates product reviews and usage instructions. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the product information input method based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past search history and suggests appropriate input methods. The system according to feature 1.

8. The aforementioned reception unit is When entering product information, filtering is performed based on the user's current areas of interest. The system according to feature 1.

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