system

Abstract

The system according to this embodiment aims to suggest actions that are appropriate to the user's objectives. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The suggestion unit proposes actions that suit the user's purpose based on the information analyzed by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to determine whether the product searched by the user matches the original purpose.

[0005] The system according to the embodiment aims to propose actions that match the user's purpose.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives the user's input. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes an action that matches the user's purpose based on the information analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can suggest actions that are appropriate to the user's objectives. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The proposed system according to an embodiment of the present invention is a system that uses generative AI to help users find what they truly need. When a user searches for a product, the proposed system uses a hearing tab inquiring about the user's purpose. For example, if a user searches for "I want a Roomba," the generative AI asks "Why do you want one?" If the user answers "Because I want to clean easily," the AI ​​provides information such as "You still have to tidy up the floor yourself," and further suggests "How about a housekeeping service?" This mechanism allows users to take actions that align with their true purpose and avoid unnecessary purchases. Furthermore, the generative AI can learn from e-commerce site data and make optimal suggestions for the user's purpose. The proposed system includes a reception unit where the generative AI receives user input, and an analysis unit that analyzes the information received by the reception unit. The analysis unit understands the user's purpose and provides information to the proposed unit, which proposes appropriate actions. Based on the information provided by the analysis unit, the proposed unit proposes actions that align with the user's purpose and provides support beyond just purchasing products. In addition, the system also includes a learning unit where the generative AI learns from e-commerce site data and makes optimal suggestions for the user's purpose. This allows the suggestion system to help users find what they truly need.

[0029] The proposed system according to the embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, image input, etc. The reception unit can, for example, ask the user about their purpose through a hearing tab when the user searches for a product. For example, if the user searches for "I want a Roomba," the reception unit can ask "Why do you want one?" The analysis unit analyzes the information received by the reception unit. The analysis unit can, for example, use a generative AI to understand the user's purpose and propose appropriate actions. The generative AI, for example, analyzes the user's input and provides information to propose actions that match the user's purpose. The proposal unit proposes actions that match the user's purpose based on the information analyzed by the analysis unit. The proposal unit can, for example, use a generative AI to propose actions that match the user's purpose. For example, if the user answers "I want to clean easily," the proposal unit can provide information such as "You have to tidy up the floor yourself" and further propose "How about a housekeeping service?" This allows the suggestion system to avoid unnecessary purchases by receiving and analyzing user input and suggesting actions that align with the user's objectives. Some or all of the above-described processes in the reception, analysis, and suggestion units may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit inputs the user's input into the generative AI, and the analysis unit allows the suggestion unit to make suggestions based on the information analyzed by the generative AI.

[0030] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, in the case of text input, it receives strings of characters entered by the user using a keyboard or touchscreen. In the case of voice input, it receives the user's speech as audio data via a microphone and converts it to text using speech recognition technology. In the case of image input, it receives image data provided by the user using a camera or scanner. This input data is centrally managed by the reception unit and transmitted to the analysis unit. Furthermore, when a user searches for a product, the reception unit can ask about the user's purpose through a hearing tab. For example, if a user searches for "I want a Roomba," the reception unit can ask "Why do you want one?" In this way, the reception unit can understand the user's specific needs and purposes in detail. The reception unit can also input the user's input content into the generation AI and perform preprocessing so that the generation AI can analyze the user's intent. For example, it can perform grammatical checks on the input text, noise reduction on the voice data, and preprocessing on the image data to enable the analysis unit to perform analysis efficiently. This allows the reception desk to handle a variety of user input formats and collect information accurately and quickly.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit can use generative AI to understand the user's purpose and propose appropriate actions. Generative AI, for example, analyzes the user's input and provides information to propose actions that match the user's purpose. Specifically, generative AI uses natural language processing technology to analyze text data and understand the user's intentions and emotions. For example, if a user inputs "I want a Roomba," the generative AI analyzes the user's underlying needs and identifies the purpose of "I want to clean easily." In the case of voice input, it analyzes the data converted to text using speech recognition technology and similarly understands the user's intentions. In the case of image input, it analyzes image data using image recognition technology to identify the object or scene indicated by the user. Based on these analysis results, the analysis unit generates information to propose actions that match the user's purpose. Furthermore, the analysis unit can refer to past user data and behavioral history to perform more accurate analysis. For example, it can analyze the behavioral patterns of users who have performed similar searches in the past and make optimal suggestions for the current user. In addition, the analysis unit can perform flexible analysis based on the user's situation and environment, using data that is updated in real time. This allows the analysis unit to respond to the diverse needs of users and provide accurate action suggestions.

[0032] The Proposal Department proposes actions that align with the user's objectives based on information analyzed by the Analysis Department. For example, the Proposal Department can use generative AI to propose actions that match the user's objectives. Specifically, the generative AI proposes the optimal action to the user based on the information provided by the Analysis Department. For example, if the user responds, "I want to clean easily," the Proposal Department can provide information such as, "You have to tidy up the floor yourself," and further suggest, "How about a housekeeping service?" The Proposal Department presents multiple options according to the user's needs, helping the user make the best choice. For example, if a user is undecided between buying a cleaning robot or using a housekeeping service, the Proposal Department compares the advantages and disadvantages of each option and proposes the best choice for the user. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can receive feedback on the results of actions taken by users following the proposals and reflect them in future proposals. Furthermore, the Proposal Department can provide flexible proposals tailored to the user's situation and environment. For example, it can offer time-saving suggestions when the user is busy, and provide more detailed information when the user has more free time. This allows the proposal department to respond to the diverse needs of users and provide accurate and effective action suggestions.

[0033] The suggestion department can provide support other than purchasing products. For example, the suggestion department can suggest actions that match the user's objectives and provide support other than purchasing products. For example, if a user responds, "I want to clean easily," the suggestion department can provide information such as, "You have to clean the floor yourself," and further suggest, "How about a housekeeping service?" In this way, the suggestion department promotes actions that match the user's original objectives by suggesting actions that match the user's objectives and providing support other than purchasing products. Some or all of the above processing in the suggestion department may be performed using, for example, generative AI, or not using generative AI. For example, the suggestion department can make suggestions based on information analyzed by generative AI.

[0034] The analysis unit can understand the user's objective and propose appropriate actions. For example, the analysis unit can use generative AI to understand the user's objective and propose appropriate actions. For example, the generative AI analyzes the user's input and provides information to propose actions that match the user's objective. In this way, the analysis unit promotes actions that match the user's original objective by understanding the user's objective and proposing appropriate actions. Some or all of the above processing in the analysis unit may be performed using generative AI, for example, or without using generative AI. For example, the analysis unit may have a proposal unit make suggestions based on the information analyzed by the generative AI.

[0035] The reception desk can ask about the user's purpose through a hearing tab when the user searches for a product. For example, if the user searches for "I want a Roomba," the reception desk can ask, "Why do you want one?" By asking about the user's purpose when they search for a product, the reception desk can suggest actions that align with the user's true purpose. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's input into a generative AI, and the suggestion desk can make suggestions based on the information analyzed by the generative AI.

[0036] The learning unit can learn from data on e-commerce sites and make suggestions that are best suited to the user's purpose. For example, the learning unit can learn from data on e-commerce sites using generative AI and make suggestions that are best suited to the user's purpose. For example, the generative AI learns from data such as purchase history, browsing history, and user ratings on e-commerce sites and provides information to make suggestions that are best suited to the user's purpose. As a result, the learning unit can make suggestions that are best suited to the user's purpose by learning from data on e-commerce sites. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can have the suggestion unit make suggestions based on the information learned by the generative AI.

[0037] The reception desk can analyze the user's past search history and select the most appropriate interview method. For example, the reception desk can ask relevant questions based on keywords the user has frequently searched for in the past. Furthermore, the reception desk can identify specific patterns from the user's past search history and customize questions based on them. In addition, the reception desk can ask relevant questions by referring to product categories the user has searched for in the past. This allows the reception desk to select the most appropriate interview method by analyzing the user's past search history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's past search history into a generative AI and adjust the questions based on the information analyzed by the generative AI.

[0038] The reception desk can customize interview questions based on the user's current living situation and areas of interest. For example, if the user has recently moved, the reception desk can ask questions related to their new living environment. It can also ask questions related to a user's hobby if that hobby is present. Furthermore, if the user has experienced a specific life event (such as marriage or childbirth), the reception desk can ask questions related to that event. This allows the reception desk to ask more appropriate questions by customizing them based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's current living situation and areas of interest into a generative AI and adjust the questions based on the information analyzed by the generative AI.

[0039] The reception desk can prioritize asking highly relevant questions by considering the user's geographical location. For example, if the user is in a specific region, the reception desk can ask questions related to that region. If the user is traveling, the reception desk can ask questions related to their travel destination. Furthermore, if the user is near a specific store, the reception desk can ask questions related to the store's products. In this way, the reception desk can prioritize asking highly relevant questions by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and adjust the question content based on the information analyzed by the generative AI.

[0040] The reception desk can analyze a user's social media activity and ask relevant questions. For example, it can ask questions related to topics the user has recently mentioned on social media. It can also customize questions based on the activity of accounts the user follows. Furthermore, it can ask questions related to topics in online communities the user participates in. In this way, the reception desk can ask relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not. For example, the reception desk can input the user's social media activity into generative AI and adjust the question content based on the information analyzed by the generative AI.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the user's past search history. Furthermore, the analysis unit can customize the analysis results by referring to the user's past purchase history. In addition, the analysis unit can analyze the user's past browsing history and provide highly relevant information. This allows the analysis unit to improve the accuracy of its analysis by referring to the user's past behavioral data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past behavioral data into a generative AI and improve the accuracy of its analysis based on the information analyzed by the generative AI.

[0042] The analysis unit can customize its analysis methods based on the user's current living situation during analysis. For example, if the user has recently moved, the analysis unit can prioritize analyzing information related to the new living environment. Furthermore, if the user has experienced a specific life event (such as marriage or childbirth), the analysis unit can analyze information related to that event. Additionally, if the user has a specific hobby, the analysis unit can prioritize analyzing information related to that hobby. This allows the analysis unit to perform more appropriate analysis by customizing its methods based on the user's current living situation. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without one. For example, the analysis unit can input the user's current living situation into a generative AI and customize its analysis methods based on the information analyzed by the generative AI.

[0043] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can prioritize analyzing information related to that region. Also, if the user is traveling, the analysis unit can analyze information related to the travel destination. Furthermore, if the user is near a specific store, the analysis unit can prioritize analyzing information related to the store's products. In this way, the analysis unit can perform more relevant analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and perform analysis based on the information analyzed by the generative AI.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature that the user has read in the past. Furthermore, the analysis unit can customize the analysis results by referring to the opinions of experts that the user follows. In addition, the analysis unit can analyze topics in online communities that the user participates in and provide highly relevant information. This allows the analysis unit to improve the accuracy of its analysis by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's relevant literature into a generative AI and improve the accuracy of its analysis based on the information analyzed by the generative AI.

[0045] The proposal unit can adjust the level of detail of its proposals based on the user's objectives. For example, if the user has a specific objective, the proposal unit can provide detailed suggestions. If the user has a vague objective, the proposal unit can provide simple suggestions. Furthermore, if the user has multiple objectives, the proposal unit can provide suggestions tailored to each objective. In this way, the proposal unit can provide the most suitable suggestions for the user by adjusting the level of detail based on the user's objectives. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the user's objectives into a generative AI and adjust the level of detail of its suggestions based on the information analyzed by the generative AI.

[0046] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, if the user is looking for home appliances, the suggestion unit can use a suggestion algorithm specialized for home appliances. If the user is looking for fashion items, the suggestion unit can use a suggestion algorithm specialized for fashion. Furthermore, if the user is looking for books, the suggestion unit can use a suggestion algorithm specialized for books. In this way, the suggestion unit can make the best suggestions for the user by applying the optimal suggestion algorithm according to the user's category. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's category into a generative AI and apply a suggestion algorithm based on the information analyzed by the generative AI.

[0047] The proposal department can prioritize proposals based on the user's submission timing. For example, if the user is in a hurry, the proposal department can prioritize proposals that can be addressed quickly. Furthermore, if the user has a specific deadline, the proposal department can provide proposals tailored to that deadline. Additionally, if the user has a long-term plan, the proposal department can provide phased proposals. This allows the proposal department to provide the best possible proposal for the user by prioritizing proposals based on their submission timing. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without one. For instance, the proposal department can input the user's submission timing into a generative AI and determine the proposal priority based on the information analyzed by the generative AI.

[0048] The suggestion unit can adjust the order of suggestions based on user relevance. For example, it can prioritize suggestions related to products the user has previously purchased. It can also prioritize suggestions related to a specific category if the user is interested in that category. Furthermore, it can prioritize suggestions related to a specific brand if the user is interested in that brand. This allows the suggestion unit to provide the most suitable suggestions for the user by adjusting the order of suggestions based on user relevance. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without one. For example, the suggestion unit can input user relevance into a generative AI and adjust the order of suggestions based on the information analyzed by the generative AI.

[0049] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the optimal learning parameters based on past learning data. Furthermore, the learning unit can utilize insights gained from past learning data to streamline the learning of new data. In addition, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. Thus, by referring to past learning data, the learning unit can optimize the learning algorithm and improve the accuracy of learning. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI and optimize its learning algorithm based on the information analyzed by the generative AI.

[0050] The learning unit can weight the training data based on the user's submission timing during training. For example, if the user is in a hurry, the learning unit can weight the most recent data during training. If the user has a long-term plan, the learning unit can also weight past data during training. Furthermore, if the user has a specific deadline, the learning unit can weight the data relevant to that deadline during training. In this way, the learning unit can perform optimal training for the user by weighting the training data based on the user's submission timing. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's submission timing into a generative AI and weight the training data based on the information analyzed by the generative AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The proposed system can further include a health management unit that monitors the user's health status. The health management unit collects the user's health data (e.g., heart rate, blood pressure, sleep patterns, etc.) and provides it to the analysis unit. The analysis unit evaluates the user's health status based on this data and provides information to the proposal unit. Based on the user's health status, the proposal unit can suggest, for example, relaxation products to reduce stress or fitness equipment to maintain health. This allows the proposal system to make optimal suggestions tailored to the user's health status.

[0053] The suggestion system can also include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes the user's past purchase history to understand the user's preferences and trends. Based on the information provided by the purchase history analysis unit, the analysis unit can make suggestions that match the user's preferences. For example, it can suggest products similar to those the user has purchased in the past. In this way, the suggestion system can make optimal suggestions based on the user's purchase history.

[0054] The suggestion system can further consider the user's geographical location when making recommendations. For example, if the user is in a specific region, it can suggest local specialties and tourist attractions. If the user is traveling, it can suggest recommended spots and restaurants in their destination. Furthermore, if the user is near a specific store, it can suggest special offers and new products from that store. In this way, the suggestion system can provide optimal recommendations based on the user's geographical location.

[0055] The suggestion system can also include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes topics mentioned by the user on social media and the activity of accounts they follow to understand the user's interests. Based on the information provided by the social media analysis unit, the analysis unit can make suggestions tailored to the user's interests. For example, it can suggest products or services related to topics the user has recently become interested in. This allows the suggestion system to make optimal suggestions based on the user's social media activity.

[0056] The suggestion system can further consider the user's current living situation when making recommendations. For example, if a user has recently moved, it can suggest products and services related to their new living environment. Similarly, if a user has experienced a specific life event (such as marriage or childbirth), it can suggest products and services related to that event. Furthermore, if a user has a particular hobby, it can suggest products and services related to that hobby. In this way, the suggestion system can provide optimal recommendations based on the user's current living situation.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, when a user searches for a product, the reception desk can ask about the user's purpose through the hearing tab. For example, if a user searches for "I want a Roomba," the reception desk can ask "Why do you want one?" Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses a generation AI to understand the user's objectives and provides information to suggest appropriate actions. Step 3: The proposal department suggests actions that match the user's objectives based on the information analyzed by the analysis department. For example, if the user answers, "I want to clean easily," the proposal department can provide information such as, "You have to clean the floor yourself," and further suggest, "How about a housekeeping service?"

[0059] (Example of form 2) The proposed system according to an embodiment of the present invention is a system that uses generative AI to help users find what they truly need. When a user searches for a product, the proposed system uses a hearing tab inquiring about the user's purpose. For example, if a user searches for "I want a Roomba," the generative AI asks "Why do you want one?" If the user answers "Because I want to clean easily," the AI ​​provides information such as "You still have to tidy up the floor yourself," and further suggests "How about a housekeeping service?" This mechanism allows users to take actions that align with their true purpose and avoid unnecessary purchases. Furthermore, the generative AI can learn from e-commerce site data and make optimal suggestions for the user's purpose. The proposed system includes a reception unit where the generative AI receives user input, and an analysis unit that analyzes the information received by the reception unit. The analysis unit understands the user's purpose and provides information to the proposed unit, which proposes appropriate actions. Based on the information provided by the analysis unit, the proposed unit proposes actions that align with the user's purpose and provides support beyond just purchasing products. In addition, the system also includes a learning unit where the generative AI learns from e-commerce site data and makes optimal suggestions for the user's purpose. This allows the suggestion system to help users find what they truly need.

[0060] The proposed system according to the embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, image input, etc. The reception unit can, for example, ask the user about their purpose through a hearing tab when the user searches for a product. For example, if the user searches for "I want a Roomba," the reception unit can ask "Why do you want one?" The analysis unit analyzes the information received by the reception unit. The analysis unit can, for example, use a generative AI to understand the user's purpose and propose appropriate actions. The generative AI, for example, analyzes the user's input and provides information to propose actions that match the user's purpose. The proposal unit proposes actions that match the user's purpose based on the information analyzed by the analysis unit. The proposal unit can, for example, use a generative AI to propose actions that match the user's purpose. For example, if the user answers "I want to clean easily," the proposal unit can provide information such as "You have to tidy up the floor yourself" and further propose "How about a housekeeping service?" This allows the suggestion system to avoid unnecessary purchases by receiving and analyzing user input and suggesting actions that align with the user's objectives. Some or all of the above-described processes in the reception, analysis, and suggestion units may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit inputs the user's input into the generative AI, and the analysis unit allows the suggestion unit to make suggestions based on the information analyzed by the generative AI.

[0061] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, in the case of text input, it receives strings of characters entered by the user using a keyboard or touchscreen. In the case of voice input, it receives the user's speech as audio data via a microphone and converts it to text using speech recognition technology. In the case of image input, it receives image data provided by the user using a camera or scanner. This input data is centrally managed by the reception unit and transmitted to the analysis unit. Furthermore, when a user searches for a product, the reception unit can ask about the user's purpose through a hearing tab. For example, if a user searches for "I want a Roomba," the reception unit can ask "Why do you want one?" In this way, the reception unit can understand the user's specific needs and purposes in detail. The reception unit can also input the user's input content into the generation AI and perform preprocessing so that the generation AI can analyze the user's intent. For example, it can perform grammatical checks on the input text, noise reduction on the voice data, and preprocessing on the image data to enable the analysis unit to perform analysis efficiently. This allows the reception desk to handle a variety of user input formats and collect information accurately and quickly.

[0062] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit can use generative AI to understand the user's purpose and propose appropriate actions. Generative AI, for example, analyzes the user's input and provides information to propose actions that match the user's purpose. Specifically, generative AI uses natural language processing technology to analyze text data and understand the user's intentions and emotions. For example, if a user inputs "I want a Roomba," the generative AI analyzes the user's underlying needs and identifies the purpose of "I want to clean easily." In the case of voice input, it analyzes the data converted to text using speech recognition technology and similarly understands the user's intentions. In the case of image input, it analyzes image data using image recognition technology to identify the object or scene indicated by the user. Based on these analysis results, the analysis unit generates information to propose actions that match the user's purpose. Furthermore, the analysis unit can refer to past user data and behavioral history to perform more accurate analysis. For example, it can analyze the behavioral patterns of users who have performed similar searches in the past and make optimal suggestions for the current user. In addition, the analysis unit can perform flexible analysis based on the user's situation and environment, using data that is updated in real time. This allows the analysis unit to respond to the diverse needs of users and provide accurate action suggestions.

[0063] The Proposal Department proposes actions that align with the user's objectives based on information analyzed by the Analysis Department. For example, the Proposal Department can use generative AI to propose actions that match the user's objectives. Specifically, the generative AI proposes the optimal action to the user based on the information provided by the Analysis Department. For example, if the user responds, "I want to clean easily," the Proposal Department can provide information such as, "You have to tidy up the floor yourself," and further suggest, "How about a housekeeping service?" The Proposal Department presents multiple options according to the user's needs, helping the user make the best choice. For example, if a user is undecided between buying a cleaning robot or using a housekeeping service, the Proposal Department compares the advantages and disadvantages of each option and proposes the best choice for the user. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can receive feedback on the results of actions taken by users following the proposals and reflect them in future proposals. Furthermore, the Proposal Department can provide flexible proposals tailored to the user's situation and environment. For example, it can offer time-saving suggestions when the user is busy, and provide more detailed information when the user has more free time. This allows the proposal department to respond to the diverse needs of users and provide accurate and effective action suggestions.

[0064] The suggestion department can provide support other than purchasing products. For example, the suggestion department can suggest actions that match the user's objectives and provide support other than purchasing products. For example, if a user responds, "I want to clean easily," the suggestion department can provide information such as, "You have to clean the floor yourself," and further suggest, "How about a housekeeping service?" In this way, the suggestion department promotes actions that match the user's original objectives by suggesting actions that match the user's objectives and providing support other than purchasing products. Some or all of the above processing in the suggestion department may be performed using, for example, generative AI, or not using generative AI. For example, the suggestion department can make suggestions based on information analyzed by generative AI.

[0065] The analysis unit can understand the user's objective and propose appropriate actions. For example, the analysis unit can use generative AI to understand the user's objective and propose appropriate actions. For example, the generative AI analyzes the user's input and provides information to propose actions that match the user's objective. In this way, the analysis unit promotes actions that match the user's original objective by understanding the user's objective and proposing appropriate actions. Some or all of the above processing in the analysis unit may be performed using generative AI, for example, or without using generative AI. For example, the analysis unit may have a proposal unit make suggestions based on the information analyzed by the generative AI.

[0066] The reception desk can ask about the user's purpose through a hearing tab when the user searches for a product. For example, if the user searches for "I want a Roomba," the reception desk can ask, "Why do you want one?" By asking about the user's purpose when they search for a product, the reception desk can suggest actions that align with the user's true purpose. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's input into a generative AI, and the suggestion desk can make suggestions based on the information analyzed by the generative AI.

[0067] The learning unit can learn from data on e-commerce sites and make suggestions that are best suited to the user's purpose. For example, the learning unit can learn from data on e-commerce sites using generative AI and make suggestions that are best suited to the user's purpose. For example, the generative AI learns from data such as purchase history, browsing history, and user ratings on e-commerce sites and provides information to make suggestions that are best suited to the user's purpose. As a result, the learning unit can make suggestions that are best suited to the user's purpose by learning from data on e-commerce sites. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can have the suggestion unit make suggestions based on the information learned by the generative AI.

[0068] The reception desk can estimate the user's emotions and adjust the interview questions based on the estimated emotions. For example, if the user is stressed, the reception desk can ask simple and intuitive questions to reduce their burden. If the user is relaxed, the reception desk can ask detailed questions to aim for a deeper understanding. Furthermore, if the user is in a hurry, the reception desk can ask short questions to quickly identify their purpose. In this way, the reception desk can ask the most appropriate questions for the user by adjusting the interview questions based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's emotion data into a generative AI and adjust the questions based on the information analyzed by the generative AI.

[0069] The reception desk can analyze the user's past search history and select the most appropriate interview method. For example, the reception desk can ask relevant questions based on keywords the user has frequently searched for in the past. Furthermore, the reception desk can identify specific patterns from the user's past search history and customize questions based on them. In addition, the reception desk can ask relevant questions by referring to product categories the user has searched for in the past. This allows the reception desk to select the most appropriate interview method by analyzing the user's past search history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's past search history into a generative AI and adjust the questions based on the information analyzed by the generative AI.

[0070] The reception desk can customize interview questions based on the user's current living situation and areas of interest. For example, if the user has recently moved, the reception desk can ask questions related to their new living environment. It can also ask questions related to a user's hobby if that hobby is present. Furthermore, if the user has experienced a specific life event (such as marriage or childbirth), the reception desk can ask questions related to that event. This allows the reception desk to ask more appropriate questions by customizing them based on the user's current living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input the user's current living situation and areas of interest into a generative AI and adjust the questions based on the information analyzed by the generative AI.

[0071] The reception desk can estimate the user's emotions and determine the priority of the interview based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize important questions. If the user is relaxed, the reception desk can postpone detailed questions and start with basic questions. Furthermore, if the user is in a hurry, the reception desk can ask important questions first to quickly identify the purpose. In this way, the reception desk can determine the priority of the interview based on the user's emotions and ask questions in the optimal order for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input the user's emotion data into a generative AI and determine the priority of questions based on the information analyzed by the generative AI.

[0072] The reception desk can prioritize asking highly relevant questions by considering the user's geographical location. For example, if the user is in a specific region, the reception desk can ask questions related to that region. If the user is traveling, the reception desk can ask questions related to their travel destination. Furthermore, if the user is near a specific store, the reception desk can ask questions related to the store's products. In this way, the reception desk can prioritize asking highly relevant questions by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and adjust the question content based on the information analyzed by the generative AI.

[0073] The reception desk can analyze a user's social media activity and ask relevant questions. For example, it can ask questions related to topics the user has recently mentioned on social media. It can also customize questions based on the activity of accounts the user follows. Furthermore, it can ask questions related to topics in online communities the user participates in. In this way, the reception desk can ask relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not. For example, the reception desk can input the user's social media activity into generative AI and adjust the question content based on the information analyzed by the generative AI.

[0074] 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 stressed, the analysis unit can use a simple algorithm. If the user is relaxed, the analysis unit can use a more complex algorithm for a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can use a high-speed algorithm to produce results quickly. In this way, the analysis unit can perform the optimal analysis for the user 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 generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and adjust the algorithm based on the information analyzed by the generative AI.

[0075] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the user's past search history. Furthermore, the analysis unit can customize the analysis results by referring to the user's past purchase history. In addition, the analysis unit can analyze the user's past browsing history and provide highly relevant information. This allows the analysis unit to improve the accuracy of its analysis by referring to the user's past behavioral data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past behavioral data into a generative AI and improve the accuracy of its analysis based on the information analyzed by the generative AI.

[0076] The analysis unit can customize its analysis methods based on the user's current living situation during analysis. For example, if the user has recently moved, the analysis unit can prioritize analyzing information related to the new living environment. Furthermore, if the user has experienced a specific life event (such as marriage or childbirth), the analysis unit can analyze information related to that event. Additionally, if the user has a specific hobby, the analysis unit can prioritize analyzing information related to that hobby. This allows the analysis unit to perform more appropriate analysis by customizing its methods based on the user's current living situation. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without one. For example, the analysis unit can input the user's current living situation into a generative AI and customize its analysis methods based on the information analyzed by the generative AI.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can provide the optimal display method for the user by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and adjust the display method based on the information analyzed by the generative AI.

[0078] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit can prioritize analyzing information related to that region. Also, if the user is traveling, the analysis unit can analyze information related to the travel destination. Furthermore, if the user is near a specific store, the analysis unit can prioritize analyzing information related to the store's products. In this way, the analysis unit can perform more relevant analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and perform analysis based on the information analyzed by the generative AI.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on relevant literature that the user has read in the past. Furthermore, the analysis unit can customize the analysis results by referring to the opinions of experts that the user follows. In addition, the analysis unit can analyze topics in online communities that the user participates in and provide highly relevant information. This allows the analysis unit to improve the accuracy of its analysis by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's relevant literature into a generative AI and improve the accuracy of its analysis based on the information analyzed by the generative AI.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, it can present suggestions that include detailed information. Furthermore, if the user is in a hurry, it can present suggestions that get straight to the point. In this way, the suggestion unit can provide the most suitable suggestions for the user by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and adjust the way it presents suggestions based on the information analyzed by the generative AI.

[0081] The proposal unit can adjust the level of detail of its proposals based on the user's objectives. For example, if the user has a specific objective, the proposal unit can provide detailed suggestions. If the user has a vague objective, the proposal unit can provide simple suggestions. Furthermore, if the user has multiple objectives, the proposal unit can provide suggestions tailored to each objective. In this way, the proposal unit can provide the most suitable suggestions for the user by adjusting the level of detail based on the user's objectives. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the user's objectives into a generative AI and adjust the level of detail of its suggestions based on the information analyzed by the generative AI.

[0082] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, if the user is looking for home appliances, the suggestion unit can use a suggestion algorithm specialized for home appliances. If the user is looking for fashion items, the suggestion unit can use a suggestion algorithm specialized for fashion. Furthermore, if the user is looking for books, the suggestion unit can use a suggestion algorithm specialized for books. In this way, the suggestion unit can make the best suggestions for the user by applying the optimal suggestion algorithm according to the user's category. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's category into a generative AI and apply a suggestion algorithm based on the information analyzed by the generative AI.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. In this way, the suggestion unit can provide the most suitable suggestions for the user by adjusting the length of the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI or not. For example, the suggestion unit can input user emotion data into a generative AI and adjust the length of the suggestions based on the information analyzed by the generative AI.

[0084] The proposal department can prioritize proposals based on the user's submission timing. For example, if the user is in a hurry, the proposal department can prioritize proposals that can be addressed quickly. Furthermore, if the user has a specific deadline, the proposal department can provide proposals tailored to that deadline. Additionally, if the user has a long-term plan, the proposal department can provide phased proposals. This allows the proposal department to provide the best possible proposal for the user by prioritizing proposals based on their submission timing. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without one. For instance, the proposal department can input the user's submission timing into a generative AI and determine the proposal priority based on the information analyzed by the generative AI.

[0085] The suggestion unit can adjust the order of suggestions based on user relevance. For example, it can prioritize suggestions related to products the user has previously purchased. It can also prioritize suggestions related to a specific category if the user is interested in that category. Furthermore, it can prioritize suggestions related to a specific brand if the user is interested in that brand. This allows the suggestion unit to provide the most suitable suggestions for the user by adjusting the order of suggestions based on user relevance. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without one. For example, the suggestion unit can input user relevance into a generative AI and adjust the order of suggestions based on the information analyzed by the generative AI.

[0086] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit can prioritize learning simple and intuitive data. If the user is relaxed, the learning unit can prioritize learning detailed data. Furthermore, if the user is in a hurry, the learning unit can prioritize learning data that will produce results quickly. In this way, the learning unit can perform optimal learning for the user by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI and select training data based on the information analyzed by the generative AI.

[0087] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can adjust the optimal learning parameters based on past learning data. Furthermore, the learning unit can utilize insights gained from past learning data to streamline the learning of new data. In addition, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. Thus, by referring to past learning data, the learning unit can optimize the learning algorithm and improve the accuracy of learning. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI and optimize its learning algorithm based on the information analyzed by the generative AI.

[0088] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the burden. Conversely, if the user is relaxed, the learning unit can increase the learning frequency to obtain more detailed data. Furthermore, if the user is in a hurry, the learning unit can adjust the learning frequency to produce results quickly. In this way, the learning unit can perform optimal learning for the user by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI and adjust the learning frequency based on the information analyzed by the generative AI.

[0089] The learning unit can weight the training data based on the user's submission timing during training. For example, if the user is in a hurry, the learning unit can weight the most recent data during training. If the user has a long-term plan, the learning unit can also weight past data during training. Furthermore, if the user has a specific deadline, the learning unit can weight the data relevant to that deadline during training. In this way, the learning unit can perform optimal training for the user by weighting the training data based on the user's submission timing. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's submission timing into a generative AI and weight the training data based on the information analyzed by the generative AI.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The proposed system can further include a health management unit that monitors the user's health status. The health management unit collects the user's health data (e.g., heart rate, blood pressure, sleep patterns, etc.) and provides it to the analysis unit. The analysis unit evaluates the user's health status based on this data and provides information to the proposal unit. Based on the user's health status, the proposal unit can suggest, for example, relaxation products to reduce stress or fitness equipment to maintain health. This allows the proposal system to make optimal suggestions tailored to the user's health status.

[0092] The suggestion system can further estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion system can offer suggestions during a time when the user can relax. If the user is relaxed, the suggestion system can offer more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion system can offer suggestions quickly. In this way, the suggestion system can offer suggestions at the optimal time based on the user's emotions.

[0093] The suggestion system can also include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes the user's past purchase history to understand the user's preferences and trends. Based on the information provided by the purchase history analysis unit, the analysis unit can make suggestions that match the user's preferences. For example, it can suggest products similar to those the user has purchased in the past. In this way, the suggestion system can make optimal suggestions based on the user's purchase history.

[0094] The suggestion system can further estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is stressed, the suggestion system can suggest products or services that promote relaxation. If the user is relaxed, the suggestion system can suggest products or services that encourage new challenges. Furthermore, if the user is excited, the suggestion system can suggest highly entertaining products or services. In this way, the suggestion system can provide optimal suggestions based on the user's emotions.

[0095] The suggestion system can further consider the user's geographical location when making recommendations. For example, if the user is in a specific region, it can suggest local specialties and tourist attractions. If the user is traveling, it can suggest recommended spots and restaurants in their destination. Furthermore, if the user is near a specific store, it can suggest special offers and new products from that store. In this way, the suggestion system can provide optimal recommendations based on the user's geographical location.

[0096] The suggestion system can further estimate the user's emotions and adjust the format of its suggestions based on those emotions. For example, if the user is stressed, the suggestion system can present suggestions in a simple and highly visual format. If the user is relaxed, the suggestion system can present suggestions in a format that includes detailed information. Furthermore, if the user is in a hurry, the suggestion system can present suggestions in a concise and to-the-point format. In this way, the suggestion system can present suggestions in the most optimal format based on the user's emotions.

[0097] The suggestion system can also include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes topics mentioned by the user on social media and the activity of accounts they follow to understand the user's interests. Based on the information provided by the social media analysis unit, the analysis unit can make suggestions tailored to the user's interests. For example, it can suggest products or services related to topics the user has recently become interested in. This allows the suggestion system to make optimal suggestions based on the user's social media activity.

[0098] The suggestion system can further estimate the user's emotions and adjust the order of suggestions based on those emotions. For example, if the user is stressed, important suggestions can be prioritized. If the user is relaxed, detailed suggestions can be postponed and basic suggestions can be introduced first. Furthermore, if the user is in a hurry, important suggestions to quickly identify their objectives can be introduced first. In this way, the suggestion system can deliver suggestions in the optimal order based on the user's emotions.

[0099] The suggestion system can further consider the user's current living situation when making recommendations. For example, if a user has recently moved, it can suggest products and services related to their new living environment. Similarly, if a user has experienced a specific life event (such as marriage or childbirth), it can suggest products and services related to that event. Furthermore, if a user has a particular hobby, it can suggest products and services related to that hobby. In this way, the suggestion system can provide optimal recommendations based on the user's current living situation.

[0100] The suggestion system can further estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling tense, the suggestion system can suggest products or services that promote relaxation. If the user is relaxed, the suggestion system can suggest products or services that encourage new challenges. Furthermore, if the user is excited, the suggestion system can suggest highly entertaining products or services. In this way, the suggestion system can provide optimal suggestions based on the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, when a user searches for a product, the reception desk can ask about the user's purpose through the hearing tab. For example, if a user searches for "I want a Roomba," the reception desk can ask "Why do you want one?" Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses a generation AI to understand the user's objectives and provides information to suggest appropriate actions. Step 3: The proposal department suggests actions that match the user's objectives based on the information analyzed by the analysis department. For example, if the user answers, "I want to clean easily," the proposal department can provide information such as, "You have to clean the floor yourself," and further suggest, "How about a housekeeping service?"

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes actions that suit the user's purpose based on the analyzed information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns data from e-commerce sites and makes optimal suggestions for the user's purpose. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, suggestion unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes actions that suit the user's purpose based on the analyzed information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns data from e-commerce sites and makes suggestions that are optimal for the user's purpose. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes actions that suit the user's purpose based on the analyzed information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns data from e-commerce sites and makes optimal suggestions for the user's purpose. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes actions that suit the user's purpose based on the analyzed information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns data from e-commerce sites and makes optimal suggestions for the user's purpose. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, The system includes a suggestion unit that proposes actions that suit the user's purpose based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Providing support other than purchasing products. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Understand the user's objectives and suggest appropriate actions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When a user searches for a product, the system asks about their purpose through the "Hearing" tab. The system described in Appendix 1, characterized by the features described herein. (Note 5) Learn from e-commerce site data, It features a learning section that provides suggestions best suited to the user's objectives. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the interview questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past search history and select the most suitable interview method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Customize interview questions based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Prioritize asking highly relevant questions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze users' social media activity and ask relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's objectives. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting a proposal, we prioritize the proposals based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the training data is weighted based on when the users submitted their data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, The system includes a suggestion unit that proposes actions that suit the user's purpose based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, Providing support other than purchasing products. The system according to feature 1.

3. The aforementioned analysis unit, Understand the user's objectives and suggest appropriate actions. The system according to feature 1.

4. The aforementioned reception unit is When a user searches for a product, the system asks about their purpose through the "Hearing" tab. The system according to feature 1.

5. Learn from e-commerce site data, It features a learning section that provides suggestions best suited to the user's objectives. The system according to feature 1.

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

7. The aforementioned reception unit is Analyze the user's past search history and select the most suitable interview method. The system according to feature 1.

8. The aforementioned reception unit is Customize interview questions based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of interviews based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is Prioritize asking highly relevant questions by considering the user's geographical location. The system according to feature 1.

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