System

Abstract

An object of a system according to an embodiment is to identify a potential need of a user and propose an optimal product or service based on the identified potential need.SOLUTION: A system includes a reception unit, an analysis unit, a proposal unit, and a learning unit. The reception unit inputs a specific desire or purpose of the user in a natural language. The analysis unit analyzes a specific desire or purpose input by the reception unit, and specifies a potential need of the user. The proposal unit searches for and proposes a product or service based on the need identified by the analysis unit. The learning unit collects feedback of the user for the product or the service proposed by the proposal unit and improves the proposal content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty identifying the products and services that users truly want, and there is room for improvement.

[0005] The system according to the embodiment aims to identify the potential needs of a user and, based on those needs, propose optimal products and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a learning unit. The reception unit inputs a user's specific wishes and objectives in natural language. The analysis unit analyzes the specific wishes and objectives input by the reception unit and identifies the user's latent needs. The proposal unit searches for and proposes products and services based on the needs identified by the analysis unit. The learning unit collects user feedback on the products and services proposed by the proposal unit and improves the proposal content. [Effects of the Invention]

[0007] The system according to the embodiment can identify the potential needs of a user and propose optimal products and services based on those needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An e-commerce site service according to an embodiment of the present invention is a system that uses a generative AI to suggest products and services that can help users achieve their true desires. While conventional e-commerce sites require users to search for products based on their own subjective opinions and knowledge, the present invention uses a generative AI as an interface to elicit the user's latent needs and then suggests optimal products and services based on those needs. For example, a user inputs a request to the generative AI in natural language, such as "I want to create a relaxing space." This input is sent to the generative AI, which analyzes it and identifies the user's true desires. The generative AI then understands the user's input and elicits the underlying latent needs. For example, the specific need, such as "I need furniture, interior decor, and aroma products for relaxation," is identified. Based on the identified needs, the generative AI searches for products and services within the e-commerce site and makes optimal suggestions. For example, it suggests furniture, interior decor, and aroma products for relaxation. These suggestions select the products and services that best fit the user's desires and goals, allowing the user to find the best options to fulfill their desires. Furthermore, the generative AI receives user feedback and improves the suggestions. When users input ratings and comments on suggested products and services, the AI ​​learns from that feedback and incorporates it into future suggestions. This improves the accuracy of suggestions and increases user satisfaction. This allows e-commerce site services to easily find products and services that will help users achieve their true goals without relying on the user's subjective opinion or knowledge. For example, if a user wishes to create a relaxing space, the AI ​​can suggest the most suitable furniture, interior design, and aroma products, allowing the user to realize their ideal space.

[0029] An e-commerce site service according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a learning unit. The reception unit inputs a user's specific wishes and objectives in natural language. For example, the user may input a wish such as "I want to create a relaxing space." The analysis unit uses a generation AI to analyze the specific wishes and objectives input by the reception unit and identify the user's potential needs. For example, the generation AI uses natural language processing technology to understand the user's wishes and objectives and identify the underlying potential needs. The proposal unit searches for and proposes products and services based on the needs identified by the analysis unit. For example, the proposal unit makes optimal proposals taking into account product ratings, reviews, price, stock availability, etc. The learning unit collects user feedback on the products and services proposed by the proposal unit and improves the proposals. For example, the learning unit collects user ratings and comments and improves the proposals using a machine learning algorithm. As a result, the e-commerce site service according to an embodiment can propose optimal products and services based on the user's wishes and objectives and improve the proposals by reflecting the feedback.

[0030] The analysis unit can use natural language processing technology to understand the user's wishes and objectives and identify the underlying latent needs. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to break down the user's input content into words and grammatical analysis to analyze the sentence structure. The analysis unit can also use semantic analysis to understand the meaning of the user's input content and identify the underlying latent needs. For example, if the user inputs "I want to create a relaxing space," the analysis unit can use morphological analysis to extract words such as "relaxation," "space," and "want to create," analyze the sentence structure using grammatical analysis, and identify specific needs such as "I need furniture, interior design, and aroma products for relaxation" using semantic analysis. In this way, natural language processing technology can more accurately identify the user's latent needs.

[0031] The suggestion unit can make appropriate suggestions by taking into consideration product ratings, reviews, prices, and stock status. Product ratings and reviews include, but are not limited to, star ratings, comments, and user feedback, for example. The suggestion unit selects an optimal product based on, for example, the star ratings of the product. The suggestion unit can also select an optimal product based on product comments and user feedback. Furthermore, the suggestion unit can make optimal suggestions by taking into consideration the price and stock status of the product. For example, the suggestion unit selects a product by taking into consideration discount prices, market prices, competitive prices, etc. The suggestion unit can also select a product by taking into consideration real-time inventory, predicted inventory, etc. In this way, it is possible to suggest optimal products and services to the user by taking into consideration product ratings, reviews, prices, stock status, etc.

[0032] The learning unit can collect user ratings and comments and improve the suggestions using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, neural networks, decision trees, and support vector machines. For example, the learning unit can collect user ratings and comments and improve the suggestions using a neural network. The learning unit can also analyze user ratings and comments using a decision tree to improve the suggestions. Furthermore, the learning unit can analyze user ratings and comments using a support vector machine to improve the suggestions. In this way, the suggestions can be continuously improved by collecting user ratings and comments and using a machine learning algorithm.

[0033] The suggestion unit can suggest furniture, interior items, and aroma products for relaxation. Examples of furniture, interior items, and aroma products for relaxation include, but are not limited to, sofas, lighting, and aroma diffusers. The suggestion unit can suggest, for example, sofas for relaxation. The suggestion unit can also suggest lighting for relaxation. Furthermore, the suggestion unit can also suggest aroma diffusers for relaxation. In this way, specific products and services for relaxation can be suggested, thereby realizing the user's wishes.

[0034] The learning unit can learn user feedback and reflect it in subsequent proposals. Feedback includes, but is not limited to, for example, surveys, reviews, and comments. The learning unit, for example, collects user survey results and reflects them in subsequent proposals. The learning unit can also collect user reviews and reflect them in subsequent proposals. Furthermore, the learning unit can collect user comments and reflect them in subsequent proposals. In this way, by learning user feedback and reflecting it in subsequent proposals, the accuracy of proposals can be improved.

[0035] Furthermore, the e-commerce site service includes a reception unit that analyzes the user's past input history and suggests the optimal input method. The reception unit uses a generation AI to analyze the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays as candidates the wishes and purposes that the user has frequently entered in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the wishes and purposes to be used during a specific time period from the user's past input history. In this way, it is possible to suggest the optimal input method by analyzing the user's past input history. Past input history includes, for example, log data, a history database, etc., but is not limited to these examples.

[0036] Furthermore, the e-commerce site service includes a reception unit that provides various input methods using voice recognition and image recognition when the user inputs information. The reception unit uses the generation AI to provide various input methods using voice recognition and image recognition when the user inputs information. For example, when the user inputs their wishes or goals by voice, the generation AI analyzes the input content using voice recognition technology. Also, when the user uploads an image, the generation AI can identify the wishes or goals using image recognition technology. Furthermore, the generation AI can analyze content entered by handwriting by the user using handwriting recognition technology. In this way, various input methods can be provided to the user using voice recognition and image recognition. Examples of voice recognition and image recognition include, but are not limited to, voice commands and image capture.

[0037] Furthermore, the e-commerce site service includes a reception unit that analyzes user input content in real time and provides appropriate feedback immediately. The reception unit uses a generation AI to analyze the user input content in real time and provide appropriate feedback immediately. For example, when a user inputs their wishes or goals, the generation AI analyzes the input content in real time and provides appropriate feedback immediately. If the user changes the input content, the generation AI can also analyze it immediately and provide appropriate feedback. If the user has any questions about the input content, the generation AI can analyze it in real time and provide supplementary information. This allows the user's input content to be analyzed in real time and appropriate feedback to be provided immediately, thereby improving user convenience. Examples of real-time analysis include, but are not limited to, real-time data processing and immediate feedback.

[0038] Furthermore, the e-commerce site service includes a reception unit that proposes input content that meets region-specific needs by taking into account the user's geographical location information. The reception unit uses a generation AI to propose input content that meets region-specific needs by taking into account the user's geographical location information. For example, if the user is in a specific region, products and services that are specific to that region can be proposed. Also, if the user is traveling, products and services that meet the region-specific needs of the user's travel destination can be proposed. Also, if the user is planning to move, products and services that will be needed in the new region can be proposed. In this way, by taking into account the user's geographical location information, input content that meets region-specific needs can be proposed. Geographical location information includes, but is not limited to, GPS data, location information services, etc.

[0039] Furthermore, the e-commerce site service includes a reception unit that analyzes a user's social media activity and automatically completes related input content. The reception unit uses a generative AI to analyze the user's social media activity and automatically completes related input content. For example, the reception unit automatically completes related hopes and goals based on content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related input content. The reception unit can also automatically complete related hopes and goals based on the activity of the user's friends on social media. In this way, the reception unit can automatically complete related input content by analyzing the user's social media activity. Social media activity includes, but is not limited to, post content, likes, comments, etc.

[0040] Furthermore, the e-commerce site service includes a reception unit that customizes the input interface by reflecting the user's past feedback. The reception unit uses a generation AI to customize the input interface by reflecting the user's past feedback. For example, the input interface is customized based on feedback previously provided by the user. The reception unit can also analyze the user's past feedback and propose an optimal input interface. The input interface can also be improved by reflecting the user's feedback in real time. This allows the input interface to be customized by reflecting the user's past feedback. Past feedback includes, but is not limited to, survey results, review history, etc.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. Past behavioral data includes, but is not limited to, log data, history databases, etc. The analysis unit can improve the accuracy of the analysis by referring to, for example, the user's past purchase history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past search history. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past ratings and comments. In this way, the analysis accuracy can be improved by referring to the user's past behavioral data.

[0042] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input content. Categories include, but are not limited to, product categories and service categories. For example, if the user inputs their preferences regarding furniture, the generation AI can apply an analysis method specialized for furniture. Also, if the user inputs their preferences regarding interior design, the generation AI can apply an analysis method specialized for interior design. Also, if the user inputs their preferences regarding aroma products, the generation AI can apply an analysis method specialized for aroma products. In this way, by applying different analysis methods depending on the category of the user's input content, more appropriate analysis results can be provided.

[0043] During analysis, the analysis unit can customize the analysis results by taking into account the user's attribute information. Attribute information includes, but is not limited to, for example, age, gender, and occupation. The analysis unit customizes the analysis results by taking into account, for example, the user's age and gender. The analysis unit can also customize the analysis results by taking into account the user's hobbies and interests. The analysis unit can also customize the analysis results by taking into account the user's lifestyle. In this way, the analysis results can be customized by taking into account the user's attribute information.

[0044] During the analysis, the analysis unit can analyze region-specific needs by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific region, the analysis unit analyzes the region-specific needs. Furthermore, if the user is traveling, the analysis unit can analyze the region-specific needs of the user's travel destination. Furthermore, if the user is planning to move, the analysis unit can analyze the needs that will be required in the new region. In this way, region-specific needs can be analyzed by taking into account the user's geographical location information.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data sources. Examples of external data sources include, but are not limited to, public databases and API data. For example, the analysis unit can improve the accuracy of the analysis by referring to related market data. The analysis unit can also improve the accuracy of the analysis by referring to related trend data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data from related review sites. In this way, by referring to related external data sources, the accuracy of the analysis can be improved.

[0046] During analysis, the analysis unit can provide analysis results taking into account the user's market trends. Market trends include, but are not limited to, market research reports, trend analyses, and the like. For example, the analysis unit can provide analysis results taking into account current market trends. The analysis unit can also provide analysis results taking into account predicted market trends. Furthermore, the analysis unit can provide analysis results taking into account past market trends. This makes it possible to provide more appropriate analysis results by taking into account the user's market trends.

[0047] When making a proposal, the suggestion unit can analyze product ratings and reviews in real time and make an optimal proposal. Real-time analysis includes, but is not limited to, real-time data processing, instant feedback, and the like. For example, the suggestion unit can analyze product ratings in real time and suggest an optimal product. The suggestion unit can also analyze product reviews in real time and suggest an optimal product. Furthermore, the suggestion unit can comprehensively analyze product ratings and reviews and suggest an optimal product. This makes it possible to suggest optimal products and services by analyzing product ratings and reviews in real time.

[0048] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past purchase history. Past purchase history includes, but is not limited to, for example, a purchase history database, transaction data, etc. The suggestion unit, for example, refers to the user's past purchase history to suggest related products. The suggestion unit can also analyze the user's past purchase history to suggest optimal products. Furthermore, the suggestion unit can also suggest products that match the user's preferences based on the user's past purchase history. In this way, by referring to the user's past purchase history, the accuracy of the suggestion can be improved.

[0049] When making a suggestion, the suggestion unit can customize the suggestion content by taking into account the user's attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The suggestion unit customizes the suggestion content by taking into account, for example, the user's age and gender. The suggestion unit can also customize the suggestion content by taking into account the user's hobbies and interests. The suggestion unit can also customize the suggestion content by taking into account the user's lifestyle. In this way, the suggestion content can be customized by taking into account the user's attribute information.

[0050] When making a suggestion, the suggestion unit can suggest products specific to a region by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the user is in a specific region, the suggestion unit can suggest products specific to that region. Furthermore, if the user is traveling, the suggestion unit can also suggest products specific to the region the user is traveling to. Furthermore, if the user is planning to move, the suggestion unit can also suggest products that will be needed in the new region. In this way, region-specific products can be suggested by taking into account the user's geographical location information.

[0051] The suggestion unit can improve the accuracy of the suggestion by referring to related external data sources when making a suggestion. Examples of external data sources include, but are not limited to, public databases and API data. For example, the suggestion unit can improve the accuracy of the suggestion by referring to related market data. The suggestion unit can also improve the accuracy of the suggestion by referring to related trend data. Furthermore, the suggestion unit can improve the accuracy of the suggestion by referring to data from related review sites. In this way, the accuracy of the suggestion can be improved by referring to related external data sources.

[0052] When making a proposal, the suggestion unit can provide the proposal content taking into consideration the user's market trends. Market trends include, but are not limited to, market research reports, trend analyses, and the like. For example, the suggestion unit can provide the proposal content taking into consideration current market trends. The suggestion unit can also provide the proposal content taking into consideration predicted market trends. Furthermore, the suggestion unit can also provide the proposal content taking into consideration past market trends. This makes it possible to provide more appropriate proposal content by taking into consideration the user's market trends.

[0053] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, for example, a training data set and historical data. For example, the learning unit optimizes the learning algorithm by referring to the past learning data. The learning unit can also analyze the past learning data and optimize the learning algorithm. Furthermore, the learning unit can optimize the learning algorithm based on the past learning data. In this way, the learning algorithm can be optimized by referring to the past learning data.

[0054] During learning, the learning unit can update the learning data by reflecting user feedback in real time. Reflecting in real time includes, but is not limited to, real-time data processing, instant feedback, and the like. For example, the learning unit can reflect user feedback in real time and update the learning data. The learning unit can also reflect user ratings and comments in real time and update the learning data. Furthermore, the learning unit can update the learning data in real time based on user feedback. This allows the learning data to be kept up to date by reflecting user feedback in real time.

[0055] The learning unit can customize the learning content by taking into account the user's attribute information during learning. Attribute information includes, but is not limited to, for example, age, gender, and occupation. The learning unit customizes the learning content by taking into account, for example, the user's age and gender. The learning unit can also customize the learning content by taking into account the user's hobbies and interests. The learning unit can also customize the learning content by taking into account the user's lifestyle. In this way, the learning content can be customized by taking into account the user's attribute information.

[0056] During learning, the learning unit can learn region-specific data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific region, the learning unit learns data specific to that region. Also, if the user is traveling, the learning unit can learn data specific to the region of the user's travel destination. Furthermore, if the user is planning to move, the learning unit can learn data required in the new region. In this way, region-specific data can be learned by taking into account the user's geographical location information.

[0057] During learning, the learning unit can integrate related external data sources to enrich the learning data. Examples of external data sources include, but are not limited to, public databases and API data. For example, the learning unit can integrate related market data to enrich the learning data. The learning unit can also integrate related trend data to enrich the learning data. Furthermore, the learning unit can integrate data from related review sites to enrich the learning data. In this way, the learning data can be enriched by integrating related external data sources.

[0058] During learning, the learning unit can update the learning content taking into account the user's market trends. Market trends include, but are not limited to, market research reports, trend analyses, and the like. For example, the learning unit updates the learning content taking into account current market trends. The learning unit can also update the learning content taking into account predicted market trends. Furthermore, the learning unit can also update the learning content taking into account past market trends. In this way, by taking into account the user's market trends, the learning content can be adapted to the latest market situation.

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

[0060] When analyzing the user's input content, the analysis unit can perform a more accurate analysis by referring to the user's past purchase history and search history. For example, needs related to the current input content can be identified based on data on products and services the user has purchased in the past. In addition, by referring to the user's past search history and analyzing similar search patterns, potential needs can be identified more accurately. Furthermore, the analysis unit can analyze the user's past ratings and comments to make appropriate suggestions for the current input content. In this way, the accuracy of the analysis can be improved by utilizing the user's past behavioral data.

[0061] The reception unit can analyze the user's input content in real time and provide appropriate feedback immediately. For example, when the user inputs their wishes or goals, the generation AI analyzes it in real time and provides immediate feedback on the input content. In addition, if the user changes the input content, the generation AI can also analyze it immediately and provide appropriate feedback. Furthermore, if the user has any questions about the input content, the generation AI can analyze it in real time and provide supplementary information. In this way, the user's input content can be analyzed in real time and appropriate feedback can be provided immediately, improving user convenience.

[0062] The suggestion unit can suggest products and services specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest products and services specific to that region. Also, if the user is traveling, the suggestion unit can suggest products and services specific to the region the user is traveling to. Furthermore, if the user is planning to move, the suggestion unit can suggest products and services that will be needed in the new region. In this way, by taking into account the user's geographical location information, suggestions can be made that meet region-specific needs.

[0063] The suggestion unit can improve the accuracy of suggestions by referring to the user's past purchase history. For example, related products can be suggested based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history to suggest optimal products. Furthermore, the suggestion unit can also suggest products that match the user's preferences based on the user's past purchase history. In this way, the suggestion unit can improve the accuracy of suggestions by referring to the user's past purchase history.

[0064] The learning unit can update the learning data by reflecting user feedback in real time. For example, it can update the learning data by reflecting user ratings and comments in real time. It can also update the learning data in real time based on user feedback. Furthermore, it can collect user feedback and improve the suggestions using a machine learning algorithm. This allows the learning data to be kept up to date by reflecting user feedback in real time.

[0065] The suggestion unit can improve the accuracy of suggestions by referring to related external data sources. For example, the suggestion unit can improve the accuracy of suggestions by referring to related market data. The suggestion unit can also improve the accuracy of suggestions by referring to related trend data. Furthermore, the suggestion unit can improve the accuracy of suggestions by referring to data from related review sites. In this way, the suggestion unit can improve the accuracy of suggestions by referring to related external data sources.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs the user's specific wishes and objectives in natural language. For example, the user inputs a wish such as "I want to create a space where I can relax." Step 2: The analysis unit uses the generation AI to analyze the specific wishes and objectives entered by the reception unit and identify the user's latent needs. For example, the generation AI uses natural language processing technology to understand the user's wishes and objectives and identify the underlying latent needs. Step 3: The suggestion unit searches for and suggests products and services based on the needs identified by the analysis unit. For example, the suggestion unit may consider product ratings, reviews, price, stock availability, etc. to make optimal suggestions. Step 4: The learning unit collects user feedback on the products and services proposed by the suggestion unit and improves the suggestions. For example, the learning unit collects user ratings and comments and uses a machine learning algorithm to improve the suggestions.

[0068] (Example 2) An e-commerce site service according to an embodiment of the present invention is a system that uses a generative AI to suggest products and services that can help users achieve their true desires. While conventional e-commerce sites require users to search for products based on their own subjective opinions and knowledge, the present invention uses a generative AI as an interface to elicit the user's latent needs and then suggests optimal products and services based on those needs. For example, a user inputs a request to the generative AI in natural language, such as "I want to create a relaxing space." This input is sent to the generative AI, which analyzes it and identifies the user's true desires. The generative AI then understands the user's input and elicits the underlying latent needs. For example, the specific need, such as "I need furniture, interior decor, and aroma products for relaxation," is identified. Based on the identified needs, the generative AI searches for products and services within the e-commerce site and makes optimal suggestions. For example, it suggests furniture, interior decor, and aroma products for relaxation. These suggestions select the products and services that best fit the user's desires and goals, allowing the user to find the best options to fulfill their desires. Furthermore, the generative AI receives user feedback and improves the suggestions. When users input ratings and comments on suggested products and services, the AI ​​learns from that feedback and incorporates it into future suggestions. This improves the accuracy of suggestions and increases user satisfaction. This allows e-commerce site services to easily find products and services that will help users achieve their true goals without relying on the user's subjective opinion or knowledge. For example, if a user wishes to create a relaxing space, the AI ​​can suggest the most suitable furniture, interior design, and aroma products, allowing the user to realize their ideal space.

[0069] An e-commerce site service according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a learning unit. The reception unit inputs a user's specific wishes and objectives in natural language. For example, the user may input a wish such as "I want to create a relaxing space." The analysis unit uses a generation AI to analyze the specific wishes and objectives input by the reception unit and identify the user's potential needs. For example, the generation AI uses natural language processing technology to understand the user's wishes and objectives and identify the underlying potential needs. The proposal unit searches for and proposes products and services based on the needs identified by the analysis unit. For example, the proposal unit makes optimal proposals taking into account product ratings, reviews, price, stock availability, etc. The learning unit collects user feedback on the products and services proposed by the proposal unit and improves the proposals. For example, the learning unit collects user ratings and comments and improves the proposals using a machine learning algorithm. As a result, the e-commerce site service according to an embodiment can propose optimal products and services based on the user's wishes and objectives and improve the proposals by reflecting the feedback.

[0070] The analysis unit can use natural language processing technology to understand the user's wishes and objectives and identify the underlying latent needs. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to break down the user's input content into words and grammatical analysis to analyze the sentence structure. The analysis unit can also use semantic analysis to understand the meaning of the user's input content and identify the underlying latent needs. For example, if the user inputs "I want to create a relaxing space," the analysis unit can use morphological analysis to extract words such as "relaxation," "space," and "want to create," analyze the sentence structure using grammatical analysis, and identify specific needs such as "I need furniture, interior design, and aroma products for relaxation" using semantic analysis. In this way, natural language processing technology can more accurately identify the user's latent needs.

[0071] The suggestion unit can make appropriate suggestions by taking into consideration product ratings, reviews, prices, and stock status. Product ratings and reviews include, but are not limited to, star ratings, comments, and user feedback, for example. The suggestion unit selects an optimal product based on, for example, the star ratings of the product. The suggestion unit can also select an optimal product based on product comments and user feedback. Furthermore, the suggestion unit can make optimal suggestions by taking into consideration the price and stock status of the product. For example, the suggestion unit selects a product by taking into consideration discount prices, market prices, competitive prices, etc. The suggestion unit can also select a product by taking into consideration real-time inventory, predicted inventory, etc. In this way, it is possible to suggest optimal products and services to the user by taking into consideration product ratings, reviews, prices, stock status, etc.

[0072] The learning unit can collect user ratings and comments and improve the suggestions using a machine learning algorithm. Examples of machine learning algorithms include, but are not limited to, neural networks, decision trees, and support vector machines. For example, the learning unit can collect user ratings and comments and improve the suggestions using a neural network. The learning unit can also analyze user ratings and comments using a decision tree to improve the suggestions. Furthermore, the learning unit can analyze user ratings and comments using a support vector machine to improve the suggestions. In this way, the suggestions can be continuously improved by collecting user ratings and comments and using a machine learning algorithm.

[0073] The suggestion unit can suggest furniture, interior items, and aroma products for relaxation. Examples of furniture, interior items, and aroma products for relaxation include, but are not limited to, sofas, lighting, and aroma diffusers. The suggestion unit can suggest, for example, sofas for relaxation. The suggestion unit can also suggest lighting for relaxation. Furthermore, the suggestion unit can also suggest aroma diffusers for relaxation. In this way, specific products and services for relaxation can be suggested, thereby realizing the user's wishes.

[0074] The learning unit can learn user feedback and reflect it in subsequent proposals. Feedback includes, but is not limited to, for example, surveys, reviews, and comments. The learning unit, for example, collects user survey results and reflects them in subsequent proposals. The learning unit can also collect user reviews and reflect them in subsequent proposals. Furthermore, the learning unit can collect user comments and reflect them in subsequent proposals. In this way, by learning user feedback and reflecting it in subsequent proposals, the accuracy of proposals can be improved.

[0075] The e-commerce site service further includes a reception unit that estimates a user's emotions and adjusts the priority of input content based on the estimated user emotions. The reception unit uses a generation AI to estimate the user's emotions and adjusts the priority of input content based on the estimated user emotions. For example, if the user is stressed, the generation AI can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the generation AI can provide detailed input options and suggest a customizable input method. Alternatively, if the user is in a hurry, the generation AI can prioritize voice input to allow the user to quickly enter their wishes and goals. This allows for a more appropriate input method to be provided by adjusting the priority of input content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] Furthermore, the e-commerce site service includes a reception unit that analyzes the user's past input history and suggests the optimal input method. The reception unit uses a generation AI to analyze the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays as candidates the wishes and purposes that the user has frequently entered in the past. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the wishes and purposes to be used during a specific time period from the user's past input history. In this way, it is possible to suggest the optimal input method by analyzing the user's past input history. Past input history includes, for example, log data, a history database, etc., but is not limited to these examples.

[0077] Furthermore, the e-commerce site service includes a reception unit that provides various input methods using voice recognition and image recognition when the user inputs information. The reception unit uses the generation AI to provide various input methods using voice recognition and image recognition when the user inputs information. For example, when the user inputs their wishes or goals by voice, the generation AI analyzes the input content using voice recognition technology. Also, when the user uploads an image, the generation AI can identify the wishes or goals using image recognition technology. Furthermore, the generation AI can analyze content entered by handwriting by the user using handwriting recognition technology. In this way, various input methods can be provided to the user using voice recognition and image recognition. Examples of voice recognition and image recognition include, but are not limited to, voice commands and image capture.

[0078] Furthermore, the e-commerce site service includes a reception unit that analyzes user input content in real time and provides appropriate feedback immediately. The reception unit uses a generation AI to analyze the user input content in real time and provide appropriate feedback immediately. For example, when a user inputs their wishes or goals, the generation AI analyzes the input content in real time and provides appropriate feedback immediately. If the user changes the input content, the generation AI can also analyze it immediately and provide appropriate feedback. If the user has any questions about the input content, the generation AI can analyze it in real time and provide supplementary information. This allows the user's input content to be analyzed in real time and appropriate feedback to be provided immediately, thereby improving user convenience. Examples of real-time analysis include, but are not limited to, real-time data processing and immediate feedback.

[0079] The e-commerce site service further includes a reception unit that estimates a user's emotions and adjusts the design of the input interface based on the estimated user emotions. The reception unit uses a generation AI to estimate the user's emotions and adjusts the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the generation AI provides an interface with subdued colors to reduce visual stress. Alternatively, if the user is having fun, the generation AI provides an interface with bright colors to make inputting more enjoyable. Alternatively, if the user is tired, the generation AI provides a simple, highly visible interface to make inputting easier. This improves the user's input experience by adjusting the design of the input interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] Furthermore, the e-commerce site service includes a reception unit that proposes input content that meets region-specific needs by taking into account the user's geographical location information. The reception unit uses a generation AI to propose input content that meets region-specific needs by taking into account the user's geographical location information. For example, if the user is in a specific region, products and services that are specific to that region can be proposed. Also, if the user is traveling, products and services that meet the region-specific needs of the user's travel destination can be proposed. Also, if the user is planning to move, products and services that will be needed in the new region can be proposed. In this way, by taking into account the user's geographical location information, input content that meets region-specific needs can be proposed. Geographical location information includes, but is not limited to, GPS data, location information services, etc.

[0081] Furthermore, the e-commerce site service includes a reception unit that analyzes a user's social media activity and automatically completes related input content. The reception unit uses a generative AI to analyze the user's social media activity and automatically completes related input content. For example, the reception unit automatically completes related hopes and goals based on content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related input content. The reception unit can also automatically complete related hopes and goals based on the activity of the user's friends on social media. In this way, the reception unit can automatically complete related input content by analyzing the user's social media activity. Social media activity includes, but is not limited to, post content, likes, comments, etc.

[0082] Furthermore, the e-commerce site service includes a reception unit that customizes the input interface by reflecting the user's past feedback. The reception unit uses a generation AI to customize the input interface by reflecting the user's past feedback. For example, the input interface is customized based on feedback previously provided by the user. The reception unit can also analyze the user's past feedback and propose an optimal input interface. The input interface can also be improved by reflecting the user's feedback in real time. This allows the input interface to be customized by reflecting the user's past feedback. Past feedback includes, but is not limited to, survey results, review history, etc.

[0083] Furthermore, in the e-commerce site service, the analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The analysis unit uses a generation AI to estimate the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the generation AI performs a detailed analysis and provides highly accurate results. If the user is in a hurry, the generation AI can perform a quick analysis and provide instant results. If the user is excited, the generation AI can provide visually stimulating analysis results. This allows for more appropriate analysis results to be provided by adjusting the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. Past behavioral data includes, but is not limited to, log data, history databases, etc. The analysis unit can improve the accuracy of the analysis by referring to, for example, the user's past purchase history. The analysis unit can also improve the accuracy of the analysis by referring to the user's past search history. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past ratings and comments. In this way, the analysis accuracy can be improved by referring to the user's past behavioral data.

[0085] During analysis, the analysis unit can apply different analysis methods depending on the category of the user's input content. Categories include, but are not limited to, product categories and service categories. For example, if the user inputs their preferences regarding furniture, the generation AI can apply an analysis method specialized for furniture. Also, if the user inputs their preferences regarding interior design, the generation AI can apply an analysis method specialized for interior design. Also, if the user inputs their preferences regarding aroma products, the generation AI can apply an analysis method specialized for aroma products. In this way, by applying different analysis methods depending on the category of the user's input content, more appropriate analysis results can be provided.

[0086] During analysis, the analysis unit can customize the analysis results by taking into account the user's attribute information. Attribute information includes, but is not limited to, for example, age, gender, and occupation. The analysis unit customizes the analysis results by taking into account, for example, the user's age and gender. The analysis unit can also customize the analysis results by taking into account the user's hobbies and interests. The analysis unit can also customize the analysis results by taking into account the user's lifestyle. In this way, the analysis results can be customized by taking into account the user's attribute information.

[0087] Furthermore, in the e-commerce site service, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The analysis unit uses a generation AI to estimate the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the generation AI provides a simple, highly visible display method. If the user is relaxed, the generation AI can provide a display method that includes detailed information. If the user is in a hurry, the generation AI can provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide an optimal display method for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During the analysis, the analysis unit can analyze region-specific needs by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific region, the analysis unit analyzes the region-specific needs. Furthermore, if the user is traveling, the analysis unit can analyze the region-specific needs of the user's travel destination. Furthermore, if the user is planning to move, the analysis unit can analyze the needs that will be required in the new region. In this way, region-specific needs can be analyzed by taking into account the user's geographical location information.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data sources. Examples of external data sources include, but are not limited to, public databases and API data. For example, the analysis unit can improve the accuracy of the analysis by referring to related market data. The analysis unit can also improve the accuracy of the analysis by referring to related trend data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data from related review sites. In this way, by referring to related external data sources, the accuracy of the analysis can be improved.

[0090] During analysis, the analysis unit can provide analysis results taking into account the user's market trends. Market trends include, but are not limited to, market research reports, trend analyses, and the like. For example, the analysis unit can provide analysis results taking into account current market trends. The analysis unit can also provide analysis results taking into account predicted market trends. Furthermore, the analysis unit can provide analysis results taking into account past market trends. This makes it possible to provide more appropriate analysis results by taking into account the user's market trends.

[0091] Furthermore, in the e-commerce site service, the suggestion unit estimates the user's emotions and adjusts the suggestions based on the estimated user emotions. The suggestion unit uses a generation AI to estimate the user's emotions and adjusts the suggestions based on the estimated user emotions. For example, if the user is relaxed, the generation AI suggests products and services that will help them relax. Also, if the user is in a hurry, the generation AI can suggest products and services that can be used quickly. Also, if the user is excited, the generation AI can suggest visually stimulating products and services. In this way, by adjusting the suggestions based on the user's emotions, more appropriate products and services can be suggested. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] When making a proposal, the suggestion unit can analyze product ratings and reviews in real time and make an optimal proposal. Real-time analysis includes, but is not limited to, real-time data processing, instant feedback, and the like. For example, the suggestion unit can analyze product ratings in real time and suggest an optimal product. The suggestion unit can also analyze product reviews in real time and suggest an optimal product. Furthermore, the suggestion unit can comprehensively analyze product ratings and reviews and suggest an optimal product. This makes it possible to suggest optimal products and services by analyzing product ratings and reviews in real time.

[0093] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past purchase history. Past purchase history includes, but is not limited to, for example, a purchase history database, transaction data, etc. The suggestion unit, for example, refers to the user's past purchase history to suggest related products. The suggestion unit can also analyze the user's past purchase history to suggest optimal products. Furthermore, the suggestion unit can also suggest products that match the user's preferences based on the user's past purchase history. In this way, by referring to the user's past purchase history, the accuracy of the suggestion can be improved.

[0094] When making a suggestion, the suggestion unit can customize the suggestion content by taking into account the user's attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The suggestion unit customizes the suggestion content by taking into account, for example, the user's age and gender. The suggestion unit can also customize the suggestion content by taking into account the user's hobbies and interests. The suggestion unit can also customize the suggestion content by taking into account the user's lifestyle. In this way, the suggestion content can be customized by taking into account the user's attribute information.

[0095] Furthermore, in the e-commerce site service, the suggestion unit estimates the user's emotions and adjusts the display method of the suggestions based on the estimated user emotions. The suggestion unit uses a generation AI to estimate the user's emotions and adjusts the display method of the suggestions based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. If the user is relaxed, the generation AI can also provide a display method that includes detailed information. If the user is in a hurry, the generation AI can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the suggestions based on the user's emotions, it is possible to provide an optimal display method for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When making a suggestion, the suggestion unit can suggest products specific to a region by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the user is in a specific region, the suggestion unit can suggest products specific to that region. Furthermore, if the user is traveling, the suggestion unit can also suggest products specific to the region the user is traveling to. Furthermore, if the user is planning to move, the suggestion unit can also suggest products that will be needed in the new region. In this way, region-specific products can be suggested by taking into account the user's geographical location information.

[0097] The suggestion unit can improve the accuracy of the suggestion by referring to related external data sources when making a suggestion. Examples of external data sources include, but are not limited to, public databases and API data. For example, the suggestion unit can improve the accuracy of the suggestion by referring to related market data. The suggestion unit can also improve the accuracy of the suggestion by referring to related trend data. Furthermore, the suggestion unit can improve the accuracy of the suggestion by referring to data from related review sites. In this way, the accuracy of the suggestion can be improved by referring to related external data sources.

[0098] When making a proposal, the suggestion unit can provide the proposal content taking into consideration the user's market trends. Market trends include, but are not limited to, market research reports, trend analyses, and the like. For example, the suggestion unit can provide the proposal content taking into consideration current market trends. The suggestion unit can also provide the proposal content taking into consideration predicted market trends. Furthermore, the suggestion unit can also provide the proposal content taking into consideration past market trends. This makes it possible to provide more appropriate proposal content by taking into consideration the user's market trends.

[0099] Furthermore, in the e-commerce site service, a learning unit estimates a user's emotions and selects training data based on the estimated user emotions. The learning unit estimates a user's emotions using a generation AI and selects training data based on the estimated user emotions. For example, if the user is relaxed, the generation AI selects training data related to relaxation. Also, if the user is in a hurry, the generation AI can select training data that can be used quickly. Also, if the user is excited, the generation AI can select visually stimulating training data. In this way, by selecting training data based on the user's emotions, more appropriate training data can be used. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, for example, a training data set and historical data. For example, the learning unit optimizes the learning algorithm by referring to the past learning data. The learning unit can also analyze the past learning data and optimize the learning algorithm. Furthermore, the learning unit can optimize the learning algorithm based on the past learning data. In this way, the learning algorithm can be optimized by referring to the past learning data.

[0101] During learning, the learning unit can update the learning data by reflecting user feedback in real time. Reflecting in real time includes, but is not limited to, real-time data processing, instant feedback, and the like. For example, the learning unit can reflect user feedback in real time and update the learning data. The learning unit can also reflect user ratings and comments in real time and update the learning data. Furthermore, the learning unit can update the learning data in real time based on user feedback. This allows the learning data to be kept up to date by reflecting user feedback in real time.

[0102] The learning unit can customize the learning content by taking into account the user's attribute information during learning. Attribute information includes, but is not limited to, for example, age, gender, and occupation. The learning unit customizes the learning content by taking into account, for example, the user's age and gender. The learning unit can also customize the learning content by taking into account the user's hobbies and interests. The learning unit can also customize the learning content by taking into account the user's lifestyle. In this way, the learning content can be customized by taking into account the user's attribute information.

[0103] Furthermore, in the e-commerce site service, a learning unit estimates a user's emotions and adjusts the frequency of learning based on the estimated user emotions. The learning unit uses a generation AI to estimate a user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, if the user is relaxed, the generation AI sets the frequency of learning low. Also, if the user is in a hurry, the generation AI can set the frequency of learning high. Also, if the user is excited, the generation AI can adjust the frequency of learning. In this way, by adjusting the frequency of learning based on the user's emotions, a more appropriate learning schedule can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] During learning, the learning unit can learn region-specific data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, if the user is in a specific region, the learning unit learns data specific to that region. Also, if the user is traveling, the learning unit can learn data specific to the region of the user's travel destination. Furthermore, if the user is planning to move, the learning unit can learn data required in the new region. In this way, region-specific data can be learned by taking into account the user's geographical location information.

[0105] During learning, the learning unit can integrate related external data sources to enrich the learning data. Examples of external data sources include, but are not limited to, public databases and API data. For example, the learning unit can integrate related market data to enrich the learning data. The learning unit can also integrate related trend data to enrich the learning data. Furthermore, the learning unit can integrate data from related review sites to enrich the learning data. In this way, the learning data can be enriched by integrating related external data sources.

[0106] During learning, the learning unit can update the learning content taking into account the user's market trends. Market trends include, but are not limited to, market research reports, trend analyses, and the like. For example, the learning unit updates the learning content taking into account current market trends. The learning unit can also update the learning content taking into account predicted market trends. Furthermore, the learning unit can also update the learning content taking into account past market trends. In this way, by taking into account the user's market trends, the learning content can be adapted to the latest market situation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the user's wishes and objectives using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's latent needs using a generative AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes optimal products and services based on the identified needs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback and improves the proposal content. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and learning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's wishes and objectives using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's latent needs using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services based on the identified needs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback and improves the suggestions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and learning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input the user's wishes and objectives using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's latent needs using a generative AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes optimal products and services based on the identified needs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback and improves the proposal content. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's wishes and objectives using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies the user's latent needs using a generative AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes optimal products and services based on the identified needs. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and collects user feedback and improves the proposal content.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When analyzing the user's input content, the analysis unit can perform a more accurate analysis by referring to the user's past purchase history and search history. For example, needs related to the current input content can be identified based on data on products and services the user has purchased in the past. In addition, by referring to the user's past search history and analyzing similar search patterns, potential needs can be identified more accurately. Furthermore, the analysis unit can analyze the user's past ratings and comments to make appropriate suggestions for the current input content. In this way, the accuracy of the analysis can be improved by utilizing the user's past behavioral data.

[0109] The suggestion unit can estimate the user's emotions and adjust the content of suggestions based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize suggestions of products and services that have a relaxing effect. Also, if the user is excited, it can suggest products and services that provide an active experience. Furthermore, if the user is sad, it can suggest entertainment or relaxation items to lift the user's mood. In this way, by adjusting the content of suggestions based on the user's emotions, it is possible to make more personalized suggestions.

[0110] The reception unit can analyze the user's input content in real time and provide appropriate feedback immediately. For example, when the user inputs their wishes or goals, the generation AI analyzes it in real time and provides immediate feedback on the input content. In addition, if the user changes the input content, the generation AI can also analyze it immediately and provide appropriate feedback. Furthermore, if the user has any questions about the input content, the generation AI can analyze it in real time and provide supplementary information. In this way, the user's input content can be analyzed in real time and appropriate feedback can be provided immediately, improving user convenience.

[0111] The learning unit can estimate the user's emotions and select learning data based on the estimated emotions. For example, if the user is relaxed, learning data related to relaxation can be selected. If the user is in a hurry, learning data that can be used quickly can be selected. Furthermore, if the user is excited, visually stimulating learning data can be selected. In this way, by selecting learning data based on the user's emotions, more appropriate learning data can be used.

[0112] The suggestion unit can suggest products and services specific to a region by taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest products and services specific to that region. Also, if the user is traveling, the suggestion unit can suggest products and services specific to the region the user is traveling to. Furthermore, if the user is planning to move, the suggestion unit can suggest products and services that will be needed in the new region. In this way, by taking into account the user's geographical location information, suggestions can be made that meet region-specific needs.

[0113] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide highly accurate results. If the user is in a hurry, a quick analysis can be performed to provide instant results. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. In this way, by adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be provided.

[0114] The suggestion unit can improve the accuracy of suggestions by referring to the user's past purchase history. For example, related products can be suggested based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history to suggest optimal products. Furthermore, the suggestion unit can also suggest products that match the user's preferences based on the user's past purchase history. In this way, the suggestion unit can improve the accuracy of suggestions by referring to the user's past purchase history.

[0115] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is nervous, an interface with calm colors can be provided to reduce visual stress. If the user is having fun, an interface with bright colors can be provided to make input work more enjoyable. Furthermore, if the user is tired, an interface with simple and high visibility can be provided to make input work easier. In this way, the user's input experience can be improved by adjusting the design of the input interface based on the user's emotions.

[0116] The learning unit can update the learning data by reflecting user feedback in real time. For example, it can update the learning data by reflecting user ratings and comments in real time. It can also update the learning data in real time based on user feedback. Furthermore, it can collect user feedback and improve the suggestions using a machine learning algorithm. This allows the learning data to be kept up to date by reflecting user feedback in real time.

[0117] The suggestion unit can improve the accuracy of suggestions by referring to related external data sources. For example, the suggestion unit can improve the accuracy of suggestions by referring to related market data. The suggestion unit can also improve the accuracy of suggestions by referring to related trend data. Furthermore, the suggestion unit can improve the accuracy of suggestions by referring to data from related review sites. In this way, the suggestion unit can improve the accuracy of suggestions by referring to related external data sources.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit inputs the user's specific wishes and objectives in natural language. For example, the user inputs a wish such as "I want to create a space where I can relax." Step 2: The analysis unit uses the generation AI to analyze the specific wishes and objectives entered by the reception unit and identify the user's latent needs. For example, the generation AI uses natural language processing technology to understand the user's wishes and objectives and identify the underlying latent needs. Step 3: The suggestion unit searches for and suggests products and services based on the needs identified by the analysis unit. For example, the suggestion unit may consider product ratings, reviews, price, stock availability, etc. to make optimal suggestions. Step 4: The learning unit collects user feedback on the products and services proposed by the suggestion unit and improves the suggestions. For example, the learning unit collects user ratings and comments and uses a machine learning algorithm to improve the suggestions.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting specific user wishes and objectives in natural language; an analysis unit that analyzes the specific desires and purposes input by the reception unit and identifies the potential needs of the user; a suggestion unit that searches for and suggests products and services based on the needs identified by the analysis unit; a learning unit that collects user feedback on the products and services proposed by the suggestion unit and improves the content of the suggestions; Equipped with A system characterized by:

2. The analysis unit Using natural language processing technology to understand the user's wishes and goals and identify the underlying needs 2. The system of claim 1.

3. The proposal unit Make appropriate suggestions based on product ratings, reviews, price, and availability 2. The system of claim 1.

4. The learning unit Collect user ratings and comments and use machine learning algorithms to improve suggestions 2. The system of claim 1.

5. The proposal unit Proposing furniture, interior design and aroma products for relaxation 2. The system of claim 1.

6. The learning unit Learn from user feedback and incorporate it into future suggestions 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the priority of input content based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

Images