system

Abstract

The system according to this embodiment aims to connect a user's vague image with a specific product. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a search unit, and a suggestion unit. The reception unit receives an ambiguous image from the user. The generation unit analyzes the image input by the reception unit and generates a concrete image. The search unit searches for products based on the image generated by the generation unit. The suggestion unit suggests the products found by the search unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to associate an ambiguous image of a user with a specific product.

[0005] The system according to the embodiment aims to associate an ambiguous image of a user with a specific product.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, a search unit, and a proposal unit. The reception unit inputs an ambiguous image of a user. The generation unit analyzes the image input by the reception unit and generates a specific image. The search unit searches for products based on the image generated by the generation unit. The proposal unit proposes the products searched by the search unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can connect a user's vague image to a specific product. [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 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. 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 uses a generating AI to convert a user's vague image into a concrete image, and then searches for and suggests products based on that image. When a user has a vague image such as "I want something like this," the product search system uses the generating AI to convert that image into a concrete image. Next, it searches for similar products based on that image and suggests them to the user. For example, if the user says, "I want a blue shirt," the generating AI generates an image of a blue shirt, and then searches for similar products on online shopping sites based on that image. As a search result, multiple blue shirts are displayed, and the user can choose from among them. Furthermore, the system sells the user's opinions and requests to companies. Here, "selling" does not mean a monetary transaction, but rather a request for preferential treatment such as a sale. For example, if a user says, "I want this product," that information is provided to the company, and the company can then conduct a sale based on that information. As a result, the user can obtain the product they want, and the company can provide products that meet the user's needs. Through this mechanism, users can easily find products that match their image, and companies can provide products that meet the user's needs. Furthermore, by requesting preferential treatment such as sales rather than monetary transactions, new value can be created between companies and users. As a result, the product search system can convert the user's vague image into a concrete image, and then search for and suggest products based on that image.

[0029] The product search system according to this embodiment comprises a reception unit, a generation unit, a search unit, and a suggestion unit. The reception unit receives a vague image from the user. The reception unit receives a vague image entered by the user, for example, in text or voice. The generation unit uses a generation AI to analyze the image entered by the reception unit and generate a concrete image. The generation unit uses a generation AI to generate a concrete image based on the user's vague image. For example, the generation unit receives a vague image of "blue shirt" and generates an image of a blue shirt. For example, the generation unit receives a vague image of "summer scenery" and generates an image of a summer scenery. For example, the generation unit receives a vague image of "modern furniture" and generates an image of modern furniture. The search unit searches for products based on the image generated by the generation unit. The search unit searches for similar products on online shopping sites using the generated image. For example, the search unit searches for products on multiple online shopping sites using the generated image. For example, the search unit searches for products in a specific category using the generated image. The suggestion unit proposes products found by the search unit to the user. The suggestion unit displays, for example, multiple products obtained as search results to the user. The suggestion unit proposes the most suitable product to the user based on the search results. The suggestion unit proposes products that match the user's preferences based on the search results. Thus, the product search system according to this embodiment can convert the user's vague image into a concrete image and search for and propose products based on that image.

[0030] The reception unit receives the user's vague image as input. For example, the reception unit accepts vague images entered by the user in text or voice. Specifically, if the user enters a vague request in text, such as "I want a blue shirt" or "something like a summer landscape," the reception unit analyzes the text and extracts appropriate keywords. In the case of voice input, speech recognition technology is used to convert the user's utterance into text and similarly extracts keywords. Furthermore, the reception unit saves the user's input and makes it available for subsequent processing. For example, it saves the user's past requests as a history so that it can respond quickly if the same request is made again. The reception unit can also provide feedback on the user's input and ask questions to make the vague image more specific. This allows the reception unit to accurately understand the user's vague image and process it appropriately as input to the next generation unit.

[0031] The generation unit uses a generation AI to analyze the image input by the reception unit and generate a specific image. For example, the generation unit uses the generation AI to generate a specific image based on the user's vague image. The generation AI uses natural language processing technology to analyze the user's input and create prompts to generate relevant images. For example, if it receives the vague image "blue shirt," the generation AI will generate a specific prompt such as "blue casual shirt" and generate an image based on that. The generation AI has learned from a large amount of image data using deep learning technology and can generate high-quality images that meet the user's request. Furthermore, the generation unit can evaluate the quality of the generated image and regenerate it as needed. For example, if the generated image does not adequately meet the user's request, the generation unit will adjust the prompt again and generate a new image. The generation unit can also accept feedback from the user on the generated image and use it as training data for the generation AI to improve generation accuracy. As a result, the generation unit can convert the user's vague image into a specific and high-quality image and provide it as input to the next search unit.

[0032] The search unit searches for products based on images generated by the generation unit. For example, the search unit searches online shopping sites using the generated image to find similar products. Specifically, it extracts features from the generated image using image recognition technology and compares them with the online shopping site's database. For example, based on the generated image of a blue shirt, it extracts features such as color, shape, and design, and searches for products that match them. The search unit can search across multiple online shopping sites to find the most suitable product. In addition, the search unit can set filtering conditions such as specific categories, price ranges, and brands to narrow down products that meet the user's needs. Furthermore, the search unit can update search results in real time, immediately reflecting the addition of new products or changes in prices. As a result, the search unit can efficiently and accurately search for products based on the generated image and provide them as input to the next suggestion unit.

[0033] The suggestion unit proposes products found by the search unit to the user. For example, the suggestion unit displays multiple products obtained as search results to the user. Specifically, it displays the search results in a list or grid format through the user interface, providing detailed information and images for each product. Based on the user's past purchase and browsing history, the suggestion unit can prioritize displaying products that match the user's preferences. The suggestion unit can also accept user feedback and continuously improve the accuracy of its suggestions. For example, if a user takes actions such as adding a particular product to their "favorites" or "purchased," the suggestion unit adjusts the next suggestion based on that information. Furthermore, the suggestion unit can also suggest related products and recommended products to the user. For example, to a user who searched for a blue shirt, it might suggest shirts in other colors from the same brand, or pants and accessories that match the shirt. In this way, the suggestion unit can propose the most suitable products to the user and increase their purchase intent.

[0034] The service provider provides users' opinions and requests to companies. For example, if a user says, "I want this product," the service provider provides that information to the company. For example, if a user says, "I would like to buy this product if it were cheaper," the service provider provides that information to the company. For example, if a user says, "I would like to buy this product if this feature were added," the service provider provides that information to the company. In this way, by providing users' opinions and requests to companies, companies can provide products that meet users' needs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input users' opinions and requests into AI, and the AI ​​can provide that information to companies.

[0035] The service provider provides users' opinions and requests to companies in the form of requests for preferential treatment such as sales, rather than monetary exchanges. For example, if a user says, "I want this product," the service provider provides that information to the company, which can then implement a sale based on that information. For example, if a user says, "I would like to buy this product if it were cheaper," the service provider provides that information to the company, which can then implement a discount based on that information. For example, if a user says, "I would like to buy this product if this feature were added," the service provider provides that information to the company, which can then improve the product based on that information. In this way, by providing users' opinions and requests to companies in the form of requests for preferential treatment such as sales, rather than monetary exchanges, new value can be created between companies and users. Some or all of the above processing by the service provider may be performed using AI, or not using AI. For example, the service provider can input user opinions and requests into AI, which can then provide that information to companies.

[0036] The reception desk analyzes the user's past input history and proposes the optimal input method. For example, the reception desk automatically displays images that the user has frequently entered in the past as candidates. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests images that the user will use at a specific time of day based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0037] The input unit completes the input based on the user's current interests and trends when an ambiguous image is entered. For example, the input unit suggests relevant images based on keywords the user has recently searched or pages they have viewed. For example, the input unit analyzes the user's social media activity and completes the input based on their current interests. For example, the input unit suggests images that the user might be interested in based on the latest trend information. This allows for the input of more appropriate images by completing the input based on the user's current interests and trends. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input user interest and trend information into AI and have the AI ​​complete the input.

[0038] The reception unit, when receiving ambiguous image input, prioritizes accepting images that are highly relevant, taking into account the user's geographical location. For example, the reception unit prioritizes displaying relevant images based on the user's current location. For example, the reception unit suggests highly relevant images based on the user's past location information. For example, the reception unit analyzes the user's visit history and prioritizes displaying relevant images. In this way, by considering the user's geographical location information, highly relevant images can be prioritized. 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 the user's geographical location information into AI and have the AI ​​perform the priority acceptance of highly relevant images.

[0039] The reception desk analyzes the user's social media activity and supplements relevant images when an ambiguous image is input. For example, the reception desk suggests relevant images based on the user's recent posts. For example, the reception desk supplements relevant images based on content shared by the user's followers or friends. For example, the reception desk analyzes posts the user has "liked" and suggests relevant images. In this way, relevant images can be supplemented 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 into AI and have the AI ​​perform the supplementation of relevant images.

[0040] The generation unit generates the optimal image by analyzing the user's past preferences during the generation process. For example, the generation unit generates the optimal image based on the colors and designs the user has liked in the past. For example, the generation unit analyzes the user's past purchase history and generates relevant images. For example, the generation unit generates the optimal image based on the products the user has viewed in the past. In this way, the optimal image can be generated by analyzing the user's past preferences. 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 past preference data into a generation AI and have the generation AI perform the generation of the optimal image.

[0041] The generation unit customizes the image content based on the user's current interests and trends during generation. For example, the generation unit generates relevant images based on keywords the user has recently searched for. For example, the generation unit analyzes the user's social media activity and customizes images based on current interests. For example, the generation unit generates images that the user is likely to be interested in based on the latest trend information. By customizing the image content based on the user's current interests and trends, more appropriate images can be generated. 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 interest and trend information into a generation AI and have the generation AI perform image customization.

[0042] The generation unit generates the optimal image by considering the user's geographical location information during generation. For example, the generation unit generates relevant images based on the user's current location. For example, the generation unit generates highly relevant images based on the user's past location information. For example, the generation unit analyzes the user's visit history and generates relevant images. In this way, the optimal image can be generated by considering the user's geographical location 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 the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal image.

[0043] The generation unit analyzes the user's social media activity during generation to supplement the image content. For example, the generation unit generates relevant images based on the user's recent posts. For example, the generation unit generates relevant images based on content shared by the user's followers and friends. For example, the generation unit analyzes posts that the user has "liked" and generates relevant images. In this way, the image content can be supplemented by analyzing the user's social media activity. 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 social media activity into a generation AI and have the generation AI perform the supplementation of the image content.

[0044] The search unit improves search accuracy by analyzing the features of the generated image in detail during the search. For example, the search unit analyzes the color and shape of the generated image to search for similar products. For example, the search unit analyzes the texture and pattern of the generated image to search for related products. For example, the search unit analyzes the context of the generated image to search for the most suitable product. In this way, search accuracy can be improved by analyzing the features of the generated image in detail. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the feature data of the generated image into AI and have the AI ​​perform the search accuracy improvement.

[0045] The search unit searches for the most suitable products by considering the user's past purchase history during a search. For example, the search unit searches for related products based on products the user has previously purchased. For example, the search unit prioritizes searches based on the user's past purchase history, such as preferred brands or categories. For example, the search unit analyzes the user's purchasing patterns and suggests the most suitable products. In this way, the search unit can find the most suitable products by considering the user's past purchase history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's past purchase history data into AI and have the AI ​​perform the search for the most suitable products.

[0046] The search unit prioritizes searching for highly relevant products by considering the user's geographical location information during a search. For example, the search unit prioritizes displaying relevant products based on the user's current location. For example, the search unit searches for highly relevant products based on the user's past location information. For example, the search unit analyzes the user's visit history and prioritizes displaying relevant products. In this way, by considering the user's geographical location information, it is possible to prioritize searching for highly relevant products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's geographical location information into AI and have the AI ​​perform a search for highly relevant products.

[0047] The search unit analyzes the user's social media activity to find relevant products during a search. For example, the search unit searches for relevant products based on the user's recent posts. For example, the search unit searches for relevant products based on content shared by the user's followers and friends. For example, the search unit analyzes posts that the user has "liked" to find relevant products. In this way, relevant products can be found by analyzing the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity into AI and have the AI ​​perform a search for relevant products.

[0048] The suggestion department adjusts the level of detail in its suggestions based on the importance of the product. For example, for expensive or important products, the suggestion department provides suggestions with detailed information. For example, for everyday products, the suggestion department provides suggestions with concise information. For example, for products that the user shows particular interest in, the suggestion department provides suggestions with detailed information. By adjusting the level of detail in suggestions based on the importance of the product, more appropriate suggestions become possible. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input product importance data into AI and have the AI ​​perform the adjustment of the level of detail in suggestions.

[0049] The suggestion unit applies different suggestion algorithms depending on the product category. For example, for fashion items, the suggestion unit makes suggestions based on trend information. For example, for home appliances, the suggestion unit makes suggestions that emphasize technical specifications. For example, for food products, the suggestion unit makes suggestions that include nutritional information and recipes. By applying different suggestion algorithms depending on the product category, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product category data into AI and have the AI ​​execute the application of the suggestion algorithm.

[0050] The proposal department determines the priority of proposals based on the timing of product submission. For example, the proposal department prioritizes new products and limited-edition products. For example, it prioritizes products during sales periods. For example, it proposes seasonal products at the appropriate time. By prioritizing proposals based on the timing of product submission, more appropriate proposals can be made. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input product submission timing data into AI and have the AI ​​perform the determination of proposal priorities.

[0051] The suggestion unit adjusts the order of suggestions based on the relevance of the products. For example, the suggestion unit prioritizes suggesting products that are highly relevant to products the user has previously purchased. For example, the suggestion unit prioritizes suggesting products that are highly relevant to products the user is currently interested in. For example, the suggestion unit prioritizes suggesting highly relevant products based on the user's past search history. By adjusting the order of suggestions based on the relevance of the products, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product relevance data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0052] The service provider analyzes the user's past opinions and requests to provide the most relevant information at the time of delivery. For example, the service provider provides relevant information based on opinions and requests previously submitted by the user. For example, the service provider analyzes the user's past feedback to provide the most relevant information. For example, the service provider provides information about products the user has shown interest in in the past. In this way, by analyzing the user's past opinions and requests, the service provider can provide the company with the most relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's past opinions and requests into AI and have the AI ​​perform the task of providing the most relevant information.

[0053] The service provider provides optimal information by considering the user's geographical location at the time of delivery. For example, the service provider provides relevant information based on the user's current location. For example, the service provider provides highly relevant information based on the user's past location information. For example, the service provider analyzes the user's visit history and provides relevant information. This allows the service provider to provide optimal information to companies by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​perform the task of providing optimal information.

[0054] The service provider analyzes the user's social media activity and provides relevant information at the time of delivery. For example, the service provider provides relevant information based on the user's recent posts. For example, the service provider provides relevant information based on the content shared by the user's followers and friends. For example, the service provider analyzes posts that the user has "liked" and provides relevant information. In this way, relevant information can be provided to companies by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​perform the provision of relevant information.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception system can complete user input by considering the user's past search history when they enter vague images. For example, it can suggest relevant images based on keywords the user has previously searched for and pages they have viewed. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest images that the user might use at a specific time of day based on their past search history. This allows users to input more appropriate images by considering their past search history.

[0057] The input system can complete ambiguous image inputs based on the user's current interests and trends. For example, it can suggest relevant images based on keywords the user has recently searched for or pages they have viewed. It can also analyze the user's social media activity and complete inputs based on their current interests. Furthermore, it can suggest images that the user might be interested in based on the latest trend information. This allows for more appropriate image input by completing inputs based on the user's current interests and trends.

[0058] The search unit can improve search accuracy by analyzing the features of the generated image in detail during the search process. For example, it can analyze the color and shape of the generated image to search for similar products. It can also analyze the texture and pattern of the generated image to search for related products. Furthermore, it can analyze the context of the generated image to search for the most suitable product. In this way, by analyzing the features of the generated image in detail, search accuracy can be improved.

[0059] The proposal department can adjust the level of detail in proposals based on the importance of the product. For example, for expensive or important products, proposals can include detailed information. For products used daily, proposals can include concise information. Furthermore, for products that users show particular interest in, proposals can include detailed information. By adjusting the level of detail in proposals based on the importance of the product, more appropriate proposals can be made.

[0060] The service provider can analyze users' past opinions and requests to provide the most relevant information at the time of delivery. For example, it can provide relevant information based on opinions and requests previously submitted by users. It can also analyze users' past feedback to provide the most relevant information. Furthermore, it can provide information about products that users have shown interest in in the past. In this way, by analyzing users' past opinions and requests, the service provider can provide companies with the most relevant information.

[0061] The suggestion function can apply different suggestion algorithms depending on the product category. For example, for fashion items, suggestions can be based on trend information. For home appliances, suggestions can focus on technical specifications. Furthermore, for food products, suggestions can include nutritional information and recipes. By applying different suggestion algorithms depending on the product category, more appropriate suggestions can be made.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk takes in the user's vague image. For example, it accepts vague images entered by the user in text or voice. Step 2: The generation unit uses a generation AI to analyze the image input by the reception unit and generate specific images. For example, the generation AI generates specific images based on vague images such as "blue shirt," "summer landscape," and "modern furniture." Step 3: The search unit searches for products based on the images generated by the generation unit. For example, it searches online shopping sites using the generated images to find similar products. It can also search multiple online shopping sites or products in specific categories. Step 4: The suggestion unit suggests products found by the search unit to the user. For example, it displays multiple products obtained as search results to the user and suggests the most suitable product or product that matches the user's preferences.

[0064] (Example of form 2) The product search system according to an embodiment of the present invention is a system that uses a generating AI to convert a user's vague image into a concrete image, and then searches for and suggests products based on that image. When a user has a vague image such as "I want something like this," the product search system uses the generating AI to convert that image into a concrete image. Next, it searches for similar products based on that image and suggests them to the user. For example, if the user says, "I want a blue shirt," the generating AI generates an image of a blue shirt, and then searches for similar products on online shopping sites based on that image. As a search result, multiple blue shirts are displayed, and the user can choose from among them. Furthermore, the system sells the user's opinions and requests to companies. Here, "selling" does not mean a monetary transaction, but rather a request for preferential treatment such as a sale. For example, if a user says, "I want this product," that information is provided to the company, and the company can then conduct a sale based on that information. As a result, the user can obtain the product they want, and the company can provide products that meet the user's needs. Through this mechanism, users can easily find products that match their image, and companies can provide products that meet the user's needs. Furthermore, by requesting preferential treatment such as sales rather than monetary transactions, new value can be created between companies and users. As a result, the product search system can convert the user's vague image into a concrete image, and then search for and suggest products based on that image.

[0065] The product search system according to this embodiment comprises a reception unit, a generation unit, a search unit, and a suggestion unit. The reception unit receives a vague image from the user. The reception unit receives a vague image entered by the user, for example, in text or voice. The generation unit uses a generation AI to analyze the image entered by the reception unit and generate a concrete image. The generation unit uses a generation AI to generate a concrete image based on the user's vague image. For example, the generation unit receives a vague image of "blue shirt" and generates an image of a blue shirt. For example, the generation unit receives a vague image of "summer scenery" and generates an image of a summer scenery. For example, the generation unit receives a vague image of "modern furniture" and generates an image of modern furniture. The search unit searches for products based on the image generated by the generation unit. The search unit searches for similar products on online shopping sites using the generated image. For example, the search unit searches for products on multiple online shopping sites using the generated image. For example, the search unit searches for products in a specific category using the generated image. The suggestion unit proposes products found by the search unit to the user. The suggestion unit displays, for example, multiple products obtained as search results to the user. The suggestion unit proposes the most suitable product to the user based on the search results. The suggestion unit proposes products that match the user's preferences based on the search results. Thus, the product search system according to this embodiment can convert the user's vague image into a concrete image and search for and propose products based on that image.

[0066] The reception unit receives the user's vague image as input. For example, the reception unit accepts vague images entered by the user in text or voice. Specifically, if the user enters a vague request in text, such as "I want a blue shirt" or "something like a summer landscape," the reception unit analyzes the text and extracts appropriate keywords. In the case of voice input, speech recognition technology is used to convert the user's utterance into text and similarly extracts keywords. Furthermore, the reception unit saves the user's input and makes it available for subsequent processing. For example, it saves the user's past requests as a history so that it can respond quickly if the same request is made again. The reception unit can also provide feedback on the user's input and ask questions to make the vague image more specific. This allows the reception unit to accurately understand the user's vague image and process it appropriately as input to the next generation unit.

[0067] The generation unit uses a generation AI to analyze the image input by the reception unit and generate a specific image. For example, the generation unit uses the generation AI to generate a specific image based on the user's vague image. The generation AI uses natural language processing technology to analyze the user's input and create prompts to generate relevant images. For example, if it receives the vague image "blue shirt," the generation AI will generate a specific prompt such as "blue casual shirt" and generate an image based on that. The generation AI has learned from a large amount of image data using deep learning technology and can generate high-quality images that meet the user's request. Furthermore, the generation unit can evaluate the quality of the generated image and regenerate it as needed. For example, if the generated image does not adequately meet the user's request, the generation unit will adjust the prompt again and generate a new image. The generation unit can also accept feedback from the user on the generated image and use it as training data for the generation AI to improve generation accuracy. As a result, the generation unit can convert the user's vague image into a specific and high-quality image and provide it as input to the next search unit.

[0068] The search unit searches for products based on images generated by the generation unit. For example, the search unit searches online shopping sites using the generated image to find similar products. Specifically, it extracts features from the generated image using image recognition technology and compares them with the online shopping site's database. For example, based on the generated image of a blue shirt, it extracts features such as color, shape, and design, and searches for products that match them. The search unit can search across multiple online shopping sites to find the most suitable product. In addition, the search unit can set filtering conditions such as specific categories, price ranges, and brands to narrow down products that meet the user's needs. Furthermore, the search unit can update search results in real time, immediately reflecting the addition of new products or changes in prices. As a result, the search unit can efficiently and accurately search for products based on the generated image and provide them as input to the next suggestion unit.

[0069] The suggestion unit proposes products found by the search unit to the user. For example, the suggestion unit displays multiple products obtained as search results to the user. Specifically, it displays the search results in a list or grid format through the user interface, providing detailed information and images for each product. Based on the user's past purchase and browsing history, the suggestion unit can prioritize displaying products that match the user's preferences. The suggestion unit can also accept user feedback and continuously improve the accuracy of its suggestions. For example, if a user takes actions such as adding a particular product to their "favorites" or "purchased," the suggestion unit adjusts the next suggestion based on that information. Furthermore, the suggestion unit can also suggest related products and recommended products to the user. For example, to a user who searched for a blue shirt, it might suggest shirts in other colors from the same brand, or pants and accessories that match the shirt. In this way, the suggestion unit can propose the most suitable products to the user and increase their purchase intent.

[0070] The service provider provides users' opinions and requests to companies. For example, if a user says, "I want this product," the service provider provides that information to the company. For example, if a user says, "I would like to buy this product if it were cheaper," the service provider provides that information to the company. For example, if a user says, "I would like to buy this product if this feature were added," the service provider provides that information to the company. In this way, by providing users' opinions and requests to companies, companies can provide products that meet users' needs. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input users' opinions and requests into AI, and the AI ​​can provide that information to companies.

[0071] The service provider provides users' opinions and requests to companies in the form of requests for preferential treatment such as sales, rather than monetary exchanges. For example, if a user says, "I want this product," the service provider provides that information to the company, which can then implement a sale based on that information. For example, if a user says, "I would like to buy this product if it were cheaper," the service provider provides that information to the company, which can then implement a discount based on that information. For example, if a user says, "I would like to buy this product if this feature were added," the service provider provides that information to the company, which can then improve the product based on that information. In this way, by providing users' opinions and requests to companies in the form of requests for preferential treatment such as sales, rather than monetary exchanges, new value can be created between companies and users. Some or all of the above processing by the service provider may be performed using AI, or not using AI. For example, the service provider can input user opinions and requests into AI, which can then provide that information to companies.

[0072] The reception desk estimates the user's emotions and adjusts the input method for ambiguous images based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of ambiguous images. This allows for more appropriate input by adjusting the input method for ambiguous images 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 user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk analyzes the user's past input history and proposes the optimal input method. For example, the reception desk automatically displays images that the user has frequently entered in the past as candidates. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests images that the user will use at a specific time of day based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​suggest the optimal input method.

[0074] The input unit completes the input based on the user's current interests and trends when an ambiguous image is entered. For example, the input unit suggests relevant images based on keywords the user has recently searched or pages they have viewed. For example, the input unit analyzes the user's social media activity and completes the input based on their current interests. For example, the input unit suggests images that the user might be interested in based on the latest trend information. This allows for the input of more appropriate images by completing the input based on the user's current interests and trends. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input user interest and trend information into AI and have the AI ​​complete the input.

[0075] The reception unit estimates the user's emotions and determines the priority of input images based on the estimated emotions. For example, if the user is excited, the reception unit prioritizes displaying highly relevant images. For example, if the user is relaxed, the reception unit prioritizes displaying detailed images. For example, if the user is stressed, the reception unit prioritizes displaying simple images. This allows for the processing of more appropriate images by prioritizing input images 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 unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception unit, when receiving ambiguous image input, prioritizes accepting images that are highly relevant, taking into account the user's geographical location. For example, the reception unit prioritizes displaying relevant images based on the user's current location. For example, the reception unit suggests highly relevant images based on the user's past location information. For example, the reception unit analyzes the user's visit history and prioritizes displaying relevant images. In this way, by considering the user's geographical location information, highly relevant images can be prioritized. 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 the user's geographical location information into AI and have the AI ​​perform the priority acceptance of highly relevant images.

[0077] The reception desk analyzes the user's social media activity and supplements relevant images when an ambiguous image is input. For example, the reception desk suggests relevant images based on the user's recent posts. For example, the reception desk supplements relevant images based on content shared by the user's followers or friends. For example, the reception desk analyzes posts the user has "liked" and suggests relevant images. In this way, relevant images can be supplemented 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 into AI and have the AI ​​perform the supplementation of relevant images.

[0078] The generation unit estimates the user's emotions and adjusts the style of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit generates images with soft colors. For example, if the user is excited, the generation unit generates images with vibrant colors. For example, if the user is stressed, the generation unit generates images with calm colors. By adjusting the style of the generated images according to the user's emotions, more appropriate images can be produced. 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 using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the image style.

[0079] The generation unit generates the optimal image by analyzing the user's past preferences during the generation process. For example, the generation unit generates the optimal image based on the colors and designs the user has liked in the past. For example, the generation unit analyzes the user's past purchase history and generates relevant images. For example, the generation unit generates the optimal image based on the products the user has viewed in the past. In this way, the optimal image can be generated by analyzing the user's past preferences. 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 past preference data into a generation AI and have the generation AI perform the generation of the optimal image.

[0080] The generation unit customizes the image content based on the user's current interests and trends during generation. For example, the generation unit generates relevant images based on keywords the user has recently searched for. For example, the generation unit analyzes the user's social media activity and customizes images based on current interests. For example, the generation unit generates images that the user is likely to be interested in based on the latest trend information. By customizing the image content based on the user's current interests and trends, more appropriate images can be generated. 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 interest and trend information into a generation AI and have the generation AI perform image customization.

[0081] The generation unit estimates the user's emotions and determines the priority of images to generate based on the estimated emotions. For example, if the user is excited, the generation unit will prioritize displaying highly relevant images. For example, if the user is relaxed, the generation unit will prioritize displaying detailed images. For example, if the user is stressed, the generation unit will prioritize displaying simple images. In this way, by determining the priority of images to generate according to the user's emotions, more appropriate images can be displayed preferentially. 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 perform the determination of image priorities.

[0082] The generation unit generates the optimal image by considering the user's geographical location information during generation. For example, the generation unit generates relevant images based on the user's current location. For example, the generation unit generates highly relevant images based on the user's past location information. For example, the generation unit analyzes the user's visit history and generates relevant images. In this way, the optimal image can be generated by considering the user's geographical location 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 the user's geographical location information into a generation AI and have the generation AI perform the generation of the optimal image.

[0083] The generation unit analyzes the user's social media activity during generation to supplement the image content. For example, the generation unit generates relevant images based on the user's recent posts. For example, the generation unit generates relevant images based on content shared by the user's followers and friends. For example, the generation unit analyzes posts that the user has "liked" and generates relevant images. In this way, the image content can be supplemented by analyzing the user's social media activity. 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 social media activity into a generation AI and have the generation AI perform the supplementation of the image content.

[0084] The search unit estimates the user's emotions and adjusts how search results are displayed based on the estimated emotions. For example, if the user is nervous, the search unit provides a simple and highly visible display. If the user is relaxed, the search unit provides a display that includes detailed information. If the user is in a hurry, the search unit provides a display that gets straight to the point. By adjusting how search results are displayed according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI adjust how search results are displayed.

[0085] The search unit improves search accuracy by analyzing the features of the generated image in detail during the search. For example, the search unit analyzes the color and shape of the generated image to search for similar products. For example, the search unit analyzes the texture and pattern of the generated image to search for related products. For example, the search unit analyzes the context of the generated image to search for the most suitable product. In this way, search accuracy can be improved by analyzing the features of the generated image in detail. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the feature data of the generated image into AI and have the AI ​​perform the search accuracy improvement.

[0086] The search unit searches for the most suitable products by considering the user's past purchase history during a search. For example, the search unit searches for related products based on products the user has previously purchased. For example, the search unit prioritizes searches based on the user's past purchase history, such as preferred brands or categories. For example, the search unit analyzes the user's purchasing patterns and suggests the most suitable products. In this way, the search unit can find the most suitable products by considering the user's past purchase history. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's past purchase history data into AI and have the AI ​​perform the search for the most suitable products.

[0087] The search unit estimates the user's emotions and determines the priority of search results based on the estimated emotions. For example, if the user is excited, the search unit will prioritize displaying highly relevant products. For example, if the user is relaxed, the search unit will prioritize displaying detailed product information. For example, if the user is stressed, the search unit will prioritize displaying simple product information. In this way, by determining the priority of search results according to the user's emotions, more appropriate products can be displayed preferentially. 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 search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform the determination of search result priorities.

[0088] The search unit prioritizes searching for highly relevant products by considering the user's geographical location information during a search. For example, the search unit prioritizes displaying relevant products based on the user's current location. For example, the search unit searches for highly relevant products based on the user's past location information. For example, the search unit analyzes the user's visit history and prioritizes displaying relevant products. In this way, by considering the user's geographical location information, it is possible to prioritize searching for highly relevant products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's geographical location information into AI and have the AI ​​perform a search for highly relevant products.

[0089] The search unit analyzes the user's social media activity to find relevant products during a search. For example, the search unit searches for relevant products based on the user's recent posts. For example, the search unit searches for relevant products based on content shared by the user's followers and friends. For example, the search unit analyzes posts that the user has "liked" to find relevant products. In this way, relevant products can be found by analyzing the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the user's social media activity into AI and have the AI ​​perform a search for relevant products.

[0090] The suggestion unit estimates the user's emotions and adjusts the way suggestions are presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit will make simple and highly visible suggestions. If the user is relaxed, the suggestion unit will make suggestions that include detailed information. If the user is in a hurry, the suggestion unit will make suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0091] The suggestion department adjusts the level of detail in its suggestions based on the importance of the product. For example, for expensive or important products, the suggestion department provides suggestions with detailed information. For example, for everyday products, the suggestion department provides suggestions with concise information. For example, for products that the user shows particular interest in, the suggestion department provides suggestions with detailed information. By adjusting the level of detail in suggestions based on the importance of the product, more appropriate suggestions become possible. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input product importance data into AI and have the AI ​​perform the adjustment of the level of detail in suggestions.

[0092] The suggestion unit applies different suggestion algorithms depending on the product category. For example, for fashion items, the suggestion unit makes suggestions based on trend information. For example, for home appliances, the suggestion unit makes suggestions that emphasize technical specifications. For example, for food products, the suggestion unit makes suggestions that include nutritional information and recipes. By applying different suggestion algorithms depending on the product category, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product category data into AI and have the AI ​​execute the application of the suggestion algorithm.

[0093] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit will provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0094] The proposal department determines the priority of proposals based on the timing of product submission. For example, the proposal department prioritizes new products and limited-edition products. For example, it prioritizes products during sales periods. For example, it proposes seasonal products at the appropriate time. By prioritizing proposals based on the timing of product submission, more appropriate proposals can be made. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input product submission timing data into AI and have the AI ​​perform the determination of proposal priorities.

[0095] The suggestion unit adjusts the order of suggestions based on the relevance of the products. For example, the suggestion unit prioritizes suggesting products that are highly relevant to products the user has previously purchased. For example, the suggestion unit prioritizes suggesting products that are highly relevant to products the user is currently interested in. For example, the suggestion unit prioritizes suggesting highly relevant products based on the user's past search history. By adjusting the order of suggestions based on the relevance of the products, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input product relevance data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0096] When the service provider delivers user opinions and requests to companies, it estimates the user's emotions and determines the priority of the information to be delivered based on the estimated emotions. For example, if a user has a strong request, the service provider will prioritize delivering that request to the company. For example, the service provider will prioritize delivering opinions to companies about products that the user is interested in. For example, the service provider will prioritize delivering opinions to companies about points of dissatisfaction. By prioritizing the information to be delivered according to the user's emotions, more appropriate information can be delivered to companies. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0097] The service provider analyzes the user's past opinions and requests to provide the most relevant information at the time of delivery. For example, the service provider provides relevant information based on opinions and requests previously submitted by the user. For example, the service provider analyzes the user's past feedback to provide the most relevant information. For example, the service provider provides information about products the user has shown interest in in the past. In this way, by analyzing the user's past opinions and requests, the service provider can provide the company with the most relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's past opinions and requests into AI and have the AI ​​perform the task of providing the most relevant information.

[0098] The information provider estimates the user's emotions and adjusts how the information is displayed based on the estimated emotions. For example, if the user is nervous, the provider provides a simple and highly visible display method. If the user is relaxed, the provider provides a display method that includes detailed information. If the user is in a hurry, the provider provides a display method that gets straight to the point. By adjusting how the information is displayed according to the user's emotions, the provider can provide more appropriate information to the company. 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 information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust how the information is displayed.

[0099] The service provider provides optimal information by considering the user's geographical location at the time of delivery. For example, the service provider provides relevant information based on the user's current location. For example, the service provider provides highly relevant information based on the user's past location information. For example, the service provider analyzes the user's visit history and provides relevant information. This allows the service provider to provide optimal information to companies by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​perform the task of providing optimal information.

[0100] The service provider analyzes the user's social media activity and provides relevant information at the time of delivery. For example, the service provider provides relevant information based on the user's recent posts. For example, the service provider provides relevant information based on the content shared by the user's followers and friends. For example, the service provider analyzes posts that the user has "liked" and provides relevant information. In this way, relevant information can be provided to companies by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​perform the provision of relevant information.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception system can complete user input by considering the user's past search history when they enter vague images. For example, it can suggest relevant images based on keywords the user has previously searched for and pages they have viewed. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest images that the user might use at a specific time of day based on their past search history. This allows users to input more appropriate images by considering their past search history.

[0103] The information provision department can estimate the user's emotions when providing user opinions and requests to companies, and determine the priority of the information to be provided based on those estimated emotions. For example, if a user has a strong request, that request can be provided to the company as a priority. It is also possible to prioritize providing opinions on products that the user is interested in to the company. Furthermore, it is possible to prioritize providing opinions on points of dissatisfaction to the company. In this way, by determining the priority of information to be provided according to the user's emotions, more appropriate information can be provided to companies.

[0104] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is nervous, it can present a simple and highly visible proposal. If the user is relaxed, it can present a proposal that includes detailed information. Furthermore, if the user is in a hurry, it can present a proposal that gets straight to the point. By adjusting the way the proposal is presented according to the user's emotions, it becomes possible to provide more appropriate proposals.

[0105] The generation unit can estimate the user's emotions and adjust the style of the generated images based on those emotions. For example, if the user is relaxed, it can generate images with soft colors. If the user is excited, it can generate images with vibrant colors. Furthermore, if the user is stressed, it can generate images with calm colors. By adjusting the style of the generated images according to the user's emotions, it is possible to produce more appropriate images.

[0106] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how search results are displayed according to the user's emotions, a more appropriate display becomes possible.

[0107] The input system can complete ambiguous image inputs based on the user's current interests and trends. For example, it can suggest relevant images based on keywords the user has recently searched for or pages they have viewed. It can also analyze the user's social media activity and complete inputs based on their current interests. Furthermore, it can suggest images that the user might be interested in based on the latest trend information. This allows for more appropriate image input by completing inputs based on the user's current interests and trends.

[0108] The search unit can improve search accuracy by analyzing the features of the generated image in detail during the search process. For example, it can analyze the color and shape of the generated image to search for similar products. It can also analyze the texture and pattern of the generated image to search for related products. Furthermore, it can analyze the context of the generated image to search for the most suitable product. In this way, by analyzing the features of the generated image in detail, search accuracy can be improved.

[0109] The proposal department can adjust the level of detail in proposals based on the importance of the product. For example, for expensive or important products, proposals can include detailed information. For products used daily, proposals can include concise information. Furthermore, for products that users show particular interest in, proposals can include detailed information. By adjusting the level of detail in proposals based on the importance of the product, more appropriate proposals can be made.

[0110] The service provider can analyze users' past opinions and requests to provide the most relevant information at the time of delivery. For example, it can provide relevant information based on opinions and requests previously submitted by users. It can also analyze users' past feedback to provide the most relevant information. Furthermore, it can provide information about products that users have shown interest in in the past. In this way, by analyzing users' past opinions and requests, the service provider can provide companies with the most relevant information.

[0111] The suggestion function can apply different suggestion algorithms depending on the product category. For example, for fashion items, suggestions can be based on trend information. For home appliances, suggestions can focus on technical specifications. Furthermore, for food products, suggestions can include nutritional information and recipes. By applying different suggestion algorithms depending on the product category, more appropriate suggestions can be made.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk takes in the user's vague image. For example, it accepts vague images entered by the user in text or voice. Step 2: The generation unit uses a generation AI to analyze the image input by the reception unit and generate specific images. For example, the generation AI generates specific images based on vague images such as "blue shirt," "summer landscape," and "modern furniture." Step 3: The search unit searches for products based on the images generated by the generation unit. For example, it searches online shopping sites using the generated images to find similar products. It can also search multiple online shopping sites or products in specific categories. Step 4: The suggestion unit suggests products found by the search unit to the user. For example, it displays multiple products obtained as search results to the user and suggests the most suitable product or product that matches the user's preferences.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, search unit, proposal unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which takes the user's vague image as text or voice input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a concrete image using generation AI. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which searches for products based on the generated image. The proposal unit is implemented by, for example, the output device 40 of the smart device 14, which displays the search results to the user. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides the user's opinions and requests to the company. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

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

[0121] The 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.

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

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

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

[0125] Figure 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.

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

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

[0128] In the 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.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0132] The data processing system 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.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, search unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which takes the user's vague image as voice input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates a specific image using generation AI. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which searches for products based on the generated image. The proposal unit is implemented, for example, by the speaker 240 of the smart glasses 214, which provides the search results to the user by voice. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which provides the user's opinions and requests to the company. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

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

[0137] The 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.

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

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

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, search unit, proposal unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which takes the user's vague image as voice input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a concrete image using generation AI. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which searches for products based on the generated image. The proposal unit is implemented by, for example, the display 343 of the headset terminal 314, which displays the search results to the user. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides the user's opinions and requests to the company. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, search unit, proposal unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which takes the user's vague image as voice input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates a concrete image using generation AI. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which searches for products based on the generated image. The proposal unit is implemented by, for example, the speaker 240 of the robot 414, which provides the search results to the user by voice. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides the user's opinions and requests to the company. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception desk where users input vague images, A generation unit analyzes the image input by the reception unit and generates a specific image, A search unit that searches for products based on the image generated by the generation unit, The system includes a suggestion unit that suggests products found by the search unit to the user. A system characterized by the following features. (Note 2) It has a department that provides user feedback and requests to companies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Instead of monetary exchange, users provide feedback and requests to companies in the form of requests for preferential treatment such as sales. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for ambiguous images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users input vague images, the system completes the input based on their current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When an ambiguous image is entered, the system prioritizes accepting images that are more relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a vague image is entered, the system analyzes the user's social media activity and supplements it with relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the style of the generated images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the system analyzes the user's past preferences to generate the most suitable image. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the image content is customized based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and determines the priority of images to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the system takes the user's geographical location information into consideration to generate the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the image content is supplemented by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, During the search, the features of the generated images are analyzed in detail to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, the system considers the user's past purchase history to find the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned search unit, When searching, the system prioritizes finding highly relevant products by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, When you search, we analyze your social media activity to find relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Apply different suggestion algorithms depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, We will prioritize proposals based on the timing of product submissions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Adjust the order of suggestions based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing user opinions and requests to a company, the system estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we analyze the user's past opinions and requests to deliver the most relevant information. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0186] 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 desk where users input vague images, A generation unit analyzes the image input by the reception unit and generates a specific image, A search unit that searches for products based on the image generated by the generation unit, The system includes a suggestion unit that suggests products found by the search unit to the user. A system characterized by the following features.

2. It has a department that provides user feedback and requests to companies. The system according to feature 1.

3. The aforementioned supply unit is, Instead of monetary exchange, users provide feedback and requests to companies in the form of requests for preferential treatment such as sales. The system according to feature 2.

4. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for ambiguous images based on the estimated user emotions. The system according to feature 1.

5. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

6. The aforementioned reception unit is When users input vague images, the system completes the input based on their current interests and trends. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input images based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is When an ambiguous image is entered, the system prioritizes accepting images that are more relevant, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When a vague image is entered, the system analyzes the user's social media activity and supplements it with relevant images. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the style of the generated images based on those estimated emotions. The system according to feature 1.

Images