System

Abstract

An object of a system according to an embodiment is to identify a potential need of a user and propose a solution based on the identified potential need.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects behavior data or feedback of a user. The analysis unit analyzes the data collected by the collection unit and identifies a potential need of the user. The proposal unit proposes a solution based on the potential need identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently identify users' latent needs and propose appropriate solutions based on those needs, so there is room for improvement.

[0005] The system according to the embodiment aims to identify the potential needs of users and propose solutions based on those needs. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects user behavioral data or feedback. The analysis unit analyzes the data collected by the collection unit and identifies potential needs of the user. The proposal unit proposes solutions based on the potential needs identified by the analysis unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A latent needs analysis system according to an embodiment of the present invention is a system that collects and analyzes user behavioral data and feedback and proposes solutions based on the latent needs. The latent needs analysis system collects user behavioral data and feedback, analyzes them using AI, and identifies the user's latent needs. Furthermore, the system proposes solutions based on the identified latent needs. For example, the latent needs analysis system collects user behavioral data and feedback. For example, on an e-commerce site, data such as which products users viewed and which products they purchased is collected. The latent needs analysis system then analyzes the collected data using AI. The AI ​​analyzes the collected data and identifies the user's latent needs. For example, the system can identify users who are highly interested in a particular product and propose appropriate solutions to those users. Furthermore, the latent needs analysis system proposes solutions based on the identified latent needs. For example, the system can provide promotional information related to a particular product to users who are highly interested in that product. This allows the latent needs analysis system to propose solutions that meet the user's latent needs. This allows the system to collect and analyze user behavioral data and feedback and propose solutions based on the latent needs. For example, understanding user behavior patterns and providing personalized solutions can improve user satisfaction.

[0029] A potential needs analysis system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects user behavioral data or feedback. Examples of user behavioral data include, but are not limited to, website browsing history, purchase history, and click data. The collection unit collects, for example, data on which products users browsed and which products they purchased. The collection unit can also collect user feedback such as reviews, survey results, and evaluation comments. For example, the collection unit collects reviews provided by users and uses the collected data as data for analysis. The analysis unit analyzes the data collected by the collection unit to identify potential needs of users. For example, the analysis unit analyzes the collected data using AI to identify users who are highly interested in a particular product. For example, the analysis unit uses AI to analyze the behavioral data and evaluate the user's level of interest. The analysis unit can also identify potential needs of users using sentiment analysis or trend analysis. For example, the analysis unit uses AI to analyze user emotions and identify potential needs. The proposal unit proposes solutions based on the potential needs identified by the analysis unit. The suggestion unit provides, for example, promotion information related to a specific product. For example, the suggestion unit provides discount information related to a specific product to a user who has a high interest in that product. The suggestion unit can also make service suggestions and product recommendations. For example, the suggestion unit suggests related services based on the user's interests. This allows the latent needs analysis system according to the embodiment to collect and analyze user behavioral data and feedback and propose solutions based on latent needs. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose solutions using an AI model that receives the latent needs identified by the analysis unit as input and outputs solutions.

[0030] The collection unit can collect data on which products a user viewed and which products they purchased. The collection unit, for example, collects data on which products a user viewed and which products they purchased. For example, the collection unit collects website browsing history to determine which products a user is interested in. The collection unit can also collect purchase history to determine which products a user purchased. For example, the collection unit collects the categories and purchase amounts of products purchased by users. The collection unit can also collect click data to determine which links a user clicked. For example, the collection unit records the detail pages of products clicked by users. This enables more accurate analysis by collecting user behavior data in detail. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user behavior data into AI and have the AI ​​collect the data.

[0031] The analysis unit can analyze the collected data and identify users who are highly interested in a specific product. For example, the analysis unit can analyze the collected data and identify users who are highly interested in a specific product. For example, the analysis unit can use AI to analyze the collected data and evaluate the user's level of interest. The analysis unit can also evaluate the user's level of interest using indicators such as the number of views, the number of clicks, and the length of time spent on the product. For example, the analysis unit can determine that a user has a high level of interest in a specific product if the user spends a long time on the page of that product. The analysis unit can also evaluate the user's level of interest using sentiment analysis. For example, the analysis unit can use AI to analyze the user's emotions and evaluate the user's level of interest in a specific product. This allows the user's level of interest to be identified, enabling more personalized suggestions. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify the user's level of interest.

[0032] The suggestion unit can provide promotional information related to a specific product based on the identified latent needs. The suggestion unit provides promotional information related to a specific product based on, for example, the identified latent needs. For example, the suggestion unit provides discount information related to a specific product to a user who is highly interested in the product. The suggestion unit can also provide campaign information and benefit information. For example, the suggestion unit notifies the user of campaign information related to the specific product. The suggestion unit can also suggest related services. For example, the suggestion unit suggests related services based on the user's interests. This can improve user satisfaction by providing promotional information based on the user's latent needs. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the identified latent needs into AI and cause the AI ​​to provide promotional information.

[0033] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit, for example, analyzes the user's past behavioral data and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices the user frequently used in the past. The collection unit can also select the optimal collection timing based on the user's past behavioral patterns. For example, the collection unit selects a collection method for a specific time period from the user's past behavioral data. The collection unit can also select a specific data collection method based on the user's past behavioral data. For example, the collection unit selects the optimal collection method based on the device the user used in the past and the user's behavioral patterns. This enables more effective data collection by analyzing the past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data into AI and have the AI ​​select the optimal collection method.

[0034] The collection unit can filter the behavioral data based on the user's current living situation and areas of interest when collecting the behavioral data. For example, the collection unit filters the behavioral data based on the user's current living situation and areas of interest when collecting the behavioral data. For example, the collection unit prioritizes collecting data related to areas in which the user is currently interested. The collection unit can also collect appropriate data depending on the user's living situation (at work, on vacation, etc.). For example, when the user is at work, the collection unit collects work-related data. The collection unit can also filter and collect relevant data based on the user's current activity (sports, reading, etc.). For example, when the user is playing sports, the collection unit collects sports-related data. This allows for more relevant data to be collected by filtering the data based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI and have the AI ​​perform the filtering.

[0035] The collection unit can select the optimal collection means depending on the user's input method when collecting behavioral data. For example, when collecting behavioral data, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, when the user uses voice input, the collection unit prioritizes collecting voice data. Also, when the user uses text input, the collection unit can prioritize collecting text data. For example, when the user uses text input, the collection unit collects text data and uses it as data for analysis. Also, when the user uses image input, the collection unit can prioritize collecting image data. For example, when the user uses image input, the collection unit collects image data and uses it as data for analysis. This enables more efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into AI and have the AI ​​select the optimal collection means.

[0036] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting behavioral data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. For example, when the user is traveling, the collection unit collects data related to the travel destination and uses the data for analysis. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the area around the user's home. For example, when the user is at home, the collection unit collects data related to the area around the user's home and uses the data for analysis. In this way, by taking the user's geographical location information into account, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data of the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0037] The collection unit can analyze the user's social media activities and collect related data when collecting behavioral data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting behavioral data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the content posted by the user's friends on social media and collects related data. The collection unit can also analyze the content posted by the user on social media and collect related data. For example, the collection unit analyzes the content posted by the user on social media and collects related data. In this way, more relevant data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into AI and cause the AI ​​to collect related data.

[0038] The collection unit can customize the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize a specific data collection method based on the user's past feedback. For example, the collection unit selects a specific data collection method based on the user's past feedback. The collection unit can also customize the collection timing and means by reflecting the user's feedback. For example, the collection unit adjusts the collection timing and means based on the user's feedback. This enables more effective data collection by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit adjusts the analysis schedule based on the importance of the data. In this way, adjusting the level of detail of the analysis based on the importance of the data enables more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit applies an image recognition algorithm to image data to analyze the content of the image. The analysis unit can also apply a voice recognition algorithm to audio data. For example, the analysis unit applies a voice recognition algorithm to audio data to analyze the content of the audio. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the data category into AI and have the AI ​​apply the analysis algorithm.

[0041] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. For example, the analysis unit can extract specific patterns based on the user's past analysis results to improve the analysis accuracy. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. For example, the analysis unit can determine the priority of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0042] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit adjusts the analysis schedule based on the time of submission. This enables more efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission into AI and have the AI ​​determine the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. For example, the analysis unit adjusts the analysis schedule based on the relevance of the data. In this way, adjusting the order of analysis based on the relevance of the data enables more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0044] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The analysis unit can also provide analysis results that are explained in simple language to a user with low level of expertise. For example, the analysis unit can provide analysis results that are explained in simple language to a user with low level of expertise. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0045] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a simplified proposal for a solution with low importance. For example, the proposal unit makes a simplified proposal for a solution with low importance. The proposal unit can also determine the priority of the proposal according to the importance of the solution. For example, the proposal unit adjusts the schedule of the proposal based on the importance of the solution. In this way, adjusting the level of detail of the proposal based on the importance of the solution enables more effective proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit may input data on the importance of the solution to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0046] The suggestion unit can apply different suggestion algorithms depending on the category of the solution when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the solution when making a suggestion. For example, the suggestion unit applies a recommendation algorithm to product-related suggestions. The suggestion unit can also apply a service matching algorithm to service-related suggestions. For example, the suggestion unit applies a service matching algorithm to service-related suggestions to suggest optimal services. The suggestion unit can also apply a content filtering algorithm to content-related suggestions. For example, the suggestion unit applies a content filtering algorithm to content-related suggestions to suggest optimal content. In this way, applying different suggestion algorithms depending on the category of the solution enables more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the category of the solution into AI and cause the AI ​​to apply the suggestion algorithm.

[0047] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract a specific pattern from the user's past suggestion results to improve the suggestion accuracy. For example, the suggestion unit can extract a specific pattern based on the user's past suggestion results to improve the suggestion accuracy. The suggestion unit can also determine the priority of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can determine the priority of the suggestion by referring to the user's past suggestion results. As a result, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data of the user's past suggestion results into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0048] The proposal unit can determine the priority of proposals based on the submission time of solutions when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission time of solutions when making proposals. For example, the proposal unit prioritizes solutions with high urgency. The proposal unit can also postpone proposing solutions that were submitted earlier. For example, the proposal unit postpones proposing solutions that were submitted earlier. The proposal unit can also adjust the schedule of proposals based on the submission time. For example, the proposal unit adjusts the schedule of proposals based on the submission time. This enables more efficient proposals by determining the priority of proposals based on the submission time of solutions. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission time of solutions into AI and have the AI ​​determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the solutions when making proposals. The proposal unit, for example, adjusts the order of proposals based on the relevance of the solutions when making proposals. For example, the proposal unit prioritizes proposing highly relevant solutions. The proposal unit can also postpone proposing less relevant solutions. For example, the proposal unit postpones proposing less relevant solutions. The proposal unit can also adjust the schedule of proposals based on the relevance of the solutions. For example, the proposal unit adjusts the schedule of proposals based on the relevance of the solutions. In this way, adjusting the order of proposals based on the relevance of the solutions enables more effective proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the relevance of the solutions to AI and cause the AI ​​to adjust the order of proposals.

[0050] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a user with high level of expertise. The suggestion unit can also provide a proposal explained in simple language to a user with low level of expertise. For example, the suggestion unit can provide a proposal explained in simple language to a user with low level of expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. For example, the suggestion unit adjusts the way the proposal is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easier to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's level of expertise into AI and cause the AI ​​to adjust the use of technical terminology.

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

[0052] The analysis unit can also estimate the user's health condition based on the user's behavioral data and determine the priority of analysis based on the estimated health condition. For example, the analysis unit can analyze the user's exercise amount and sleep patterns from the user's behavioral data to estimate the health condition. The analysis unit can also analyze the user's dietary data and evaluate nutritional balance. Furthermore, the analysis unit can estimate the user's stress level and make suggestions for stress reduction. This allows for more appropriate suggestions to be made by determining the priority of analysis based on the user's health condition.

[0053] The suggestion unit can also estimate the user's hobbies and interests based on the user's behavioral data and customize the suggestions based on the estimated hobbies and interests. For example, the suggestion unit can analyze data on websites and apps frequently visited by the user to identify the user's hobbies and interests. The suggestion unit can also make related suggestions based on data on events the user has attended or products purchased by the user. Furthermore, the suggestion unit can analyze the user's social media activity and make suggestions based on topics of interest to the user. This allows the suggestion unit to customize the suggestions based on the user's hobbies and interests, enabling more personalized suggestions.

[0054] When collecting user behavioral data, the collection unit can optimize the data collection method by taking into account the user's device usage status. For example, if the user frequently uses a smartphone, the collection unit can prioritize data collection from the smartphone. Also, if the user uses a personal computer, the collection unit can collect data from the personal computer. Furthermore, if the user uses a wearable device, the collection unit can collect data from the device. This allows for more accurate data collection by optimizing the data collection method based on the user's device usage status.

[0055] The analysis unit can also estimate the user's lifestyle based on the user's behavioral data and adjust the analysis method based on the estimated lifestyle. For example, the analysis unit can analyze the user's exercise habits and eating patterns to estimate a healthy lifestyle. The analysis unit can also analyze the user's work schedule and vacation patterns to evaluate the lifestyle. Furthermore, the analysis unit can analyze the user's hobbies and leisure activities to make suggestions based on the user's lifestyle. This allows for more appropriate suggestions to be made by adjusting the analysis method based on the user's lifestyle.

[0056] The suggestion unit can also estimate the user's purchasing tendencies based on the user's behavioral data and adjust the content of suggestions based on the estimated purchasing tendencies. For example, the suggestion unit can analyze the user's past purchase history to identify purchasing tendencies. The suggestion unit can also make related suggestions based on data on products frequently viewed by the user. Furthermore, the suggestion unit can analyze the user's purchasing patterns and make suggestions based on products that the user tends to purchase at specific times. This allows for more effective suggestions by adjusting the content of suggestions based on the user's purchasing tendencies.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects user behavioral data or feedback. User behavioral data includes website browsing history, purchase history, click data, etc. The collection unit collects data on which products users viewed and which products they purchased. The collection unit can also collect feedback such as user reviews, survey results, and evaluation comments. For example, the collection unit collects reviews provided by users and uses them as data for analysis. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the user's latent needs. The analysis unit analyzes the collected data using AI and identifies users who have a high level of interest in a particular product. For example, the analysis unit uses AI to analyze behavioral data and evaluate the user's level of interest. The analysis unit can also identify the user's latent needs using sentiment analysis and trend analysis. For example, the analysis unit uses AI to analyze the user's emotions and identify the latent needs. Step 3: The suggestion unit proposes solutions based on the potential needs identified by the analysis unit. The suggestion unit provides promotional information related to a specific product. For example, the suggestion unit provides discount information related to a specific product to a user who has a high interest in that product. The suggestion unit can also make service suggestions and product recommendations. For example, the suggestion unit proposes related services based on the user's interests. Some or all of the processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can propose solutions using an AI model that takes the potential needs identified by the analysis unit as input and outputs solutions.

[0059] (Example 2) A latent needs analysis system according to an embodiment of the present invention is a system that collects and analyzes user behavioral data and feedback and proposes solutions based on the latent needs. The latent needs analysis system collects user behavioral data and feedback, analyzes them using AI, and identifies the user's latent needs. Furthermore, the system proposes solutions based on the identified latent needs. For example, the latent needs analysis system collects user behavioral data and feedback. For example, on an e-commerce site, data such as which products users viewed and which products they purchased is collected. The latent needs analysis system then analyzes the collected data using AI. The AI ​​analyzes the collected data and identifies the user's latent needs. For example, the system can identify users who are highly interested in a particular product and propose appropriate solutions to those users. Furthermore, the latent needs analysis system proposes solutions based on the identified latent needs. For example, the system can provide promotional information related to a particular product to users who are highly interested in that product. This allows the latent needs analysis system to propose solutions that meet the user's latent needs. This allows the system to collect and analyze user behavioral data and feedback and propose solutions based on the latent needs. For example, understanding user behavior patterns and providing personalized solutions can improve user satisfaction.

[0060] A potential needs analysis system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects user behavioral data or feedback. Examples of user behavioral data include, but are not limited to, website browsing history, purchase history, and click data. The collection unit collects, for example, data on which products users browsed and which products they purchased. The collection unit can also collect user feedback such as reviews, survey results, and evaluation comments. For example, the collection unit collects reviews provided by users and uses the collected data as data for analysis. The analysis unit analyzes the data collected by the collection unit to identify potential needs of users. For example, the analysis unit analyzes the collected data using AI to identify users who are highly interested in a particular product. For example, the analysis unit uses AI to analyze the behavioral data and evaluate the user's level of interest. The analysis unit can also identify potential needs of users using sentiment analysis or trend analysis. For example, the analysis unit uses AI to analyze user emotions and identify potential needs. The proposal unit proposes solutions based on the potential needs identified by the analysis unit. The suggestion unit provides, for example, promotion information related to a specific product. For example, the suggestion unit provides discount information related to a specific product to a user who has a high interest in that product. The suggestion unit can also make service suggestions and product recommendations. For example, the suggestion unit suggests related services based on the user's interests. This allows the latent needs analysis system according to the embodiment to collect and analyze user behavioral data and feedback and propose solutions based on latent needs. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose solutions using an AI model that receives the latent needs identified by the analysis unit as input and outputs solutions.

[0061] The collection unit can collect data on which products a user viewed and which products they purchased. The collection unit, for example, collects data on which products a user viewed and which products they purchased. For example, the collection unit collects website browsing history to determine which products a user is interested in. The collection unit can also collect purchase history to determine which products a user purchased. For example, the collection unit collects the categories and purchase amounts of products purchased by users. The collection unit can also collect click data to determine which links a user clicked. For example, the collection unit records the detail pages of products clicked by users. This enables more accurate analysis by collecting user behavior data in detail. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user behavior data into AI and have the AI ​​collect the data.

[0062] The analysis unit can analyze the collected data and identify users who are highly interested in a specific product. For example, the analysis unit can analyze the collected data and identify users who are highly interested in a specific product. For example, the analysis unit can use AI to analyze the collected data and evaluate the user's level of interest. The analysis unit can also evaluate the user's level of interest using indicators such as the number of views, the number of clicks, and the length of time spent on the product. For example, the analysis unit can determine that a user has a high level of interest in a specific product if the user spends a long time on the page of that product. The analysis unit can also evaluate the user's level of interest using sentiment analysis. For example, the analysis unit can use AI to analyze the user's emotions and evaluate the user's level of interest in a specific product. This allows the user's level of interest to be identified, enabling more personalized suggestions. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​identify the user's level of interest.

[0063] The suggestion unit can provide promotional information related to a specific product based on the identified latent needs. The suggestion unit provides promotional information related to a specific product based on, for example, the identified latent needs. For example, the suggestion unit provides discount information related to a specific product to a user who is highly interested in the product. The suggestion unit can also provide campaign information and benefit information. For example, the suggestion unit notifies the user of campaign information related to the specific product. The suggestion unit can also suggest related services. For example, the suggestion unit suggests related services based on the user's interests. This can improve user satisfaction by providing promotional information based on the user's latent needs. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the identified latent needs into AI and cause the AI ​​to provide promotional information.

[0064] The collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of behavioral data collection based on the estimated user emotions. For example, if the user is stressed, the collection unit delays the collection timing and collects data when the user is relaxed. Furthermore, if the user is excited, the collection unit can immediately collect data and understand the behavioral pattern in real time. For example, the collection unit collects data when the user is excited and analyzes the behavioral pattern in real time. Furthermore, if the user is tired, the collection unit can adjust the collection timing and collect data after the user has rested. For example, the collection unit collects data after the user has rested and understands the behavioral pattern. This enables more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and have the AI ​​adjust the collection timing.

[0065] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit, for example, analyzes the user's past behavioral data and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices the user frequently used in the past. The collection unit can also select the optimal collection timing based on the user's past behavioral patterns. For example, the collection unit selects a collection method for a specific time period from the user's past behavioral data. The collection unit can also select a specific data collection method based on the user's past behavioral data. For example, the collection unit selects the optimal collection method based on the device the user used in the past and the user's behavioral patterns. This enables more effective data collection by analyzing the past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data into AI and have the AI ​​select the optimal collection method.

[0066] The collection unit can filter the behavioral data based on the user's current living situation and areas of interest when collecting the behavioral data. For example, the collection unit filters the behavioral data based on the user's current living situation and areas of interest when collecting the behavioral data. For example, the collection unit prioritizes collecting data related to areas in which the user is currently interested. The collection unit can also collect appropriate data depending on the user's living situation (at work, on vacation, etc.). For example, when the user is at work, the collection unit collects work-related data. The collection unit can also filter and collect relevant data based on the user's current activity (sports, reading, etc.). For example, when the user is playing sports, the collection unit collects sports-related data. This allows for more relevant data to be collected by filtering the data based on the user's living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's living situation and areas of interest into AI and have the AI ​​perform the filtering.

[0067] The collection unit can select the optimal collection means depending on the user's input method when collecting behavioral data. For example, when collecting behavioral data, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, when the user uses voice input, the collection unit prioritizes collecting voice data. Also, when the user uses text input, the collection unit can prioritize collecting text data. For example, when the user uses text input, the collection unit collects text data and uses it as data for analysis. Also, when the user uses image input, the collection unit can prioritize collecting image data. For example, when the user uses image input, the collection unit collects image data and uses it as data for analysis. This enables more efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into AI and have the AI ​​select the optimal collection means.

[0068] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting data related to relaxation. For example, the collection unit collects data when the user is relaxed and understands behavioral patterns. Furthermore, if the user is excited, the collection unit can also prioritize collecting data related to excitement. For example, the collection unit collects data when the user is excited and analyzes behavioral patterns in real time. Thus, by determining the priority of data based on the user's emotions, more important data can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotional data into the AI ​​and have the AI ​​determine the priority of the data.

[0069] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting behavioral data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. For example, when the user is traveling, the collection unit collects data related to the travel destination and uses the data for analysis. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the area around the user's home. For example, when the user is at home, the collection unit collects data related to the area around the user's home and uses the data for analysis. In this way, by taking the user's geographical location information into account, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data of the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0070] The collection unit can analyze the user's social media activities and collect related data when collecting behavioral data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting behavioral data. For example, the collection unit collects related data based on information shared by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the content posted by the user's friends on social media and collects related data. The collection unit can also analyze the content posted by the user on social media and collect related data. For example, the collection unit analyzes the content posted by the user on social media and collects related data. In this way, more relevant data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activities into AI and cause the AI ​​to collect related data.

[0071] The collection unit can customize the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize a specific data collection method based on the user's past feedback. For example, the collection unit selects a specific data collection method based on the user's past feedback. The collection unit can also customize the collection timing and means by reflecting the user's feedback. For example, the collection unit adjusts the collection timing and means based on the user's feedback. This enables more effective data collection by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, when the user is nervous, the analysis unit provides a simple, highly visible analysis result. Furthermore, the analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit provides a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, the analysis unit provides a visually stimulating analysis result when the user is excited. This allows for adjusting the presentation method of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the analysis is expressed.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit adjusts the analysis schedule based on the importance of the data. In this way, adjusting the level of detail of the analysis based on the importance of the data enables more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to AI and have the AI ​​adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit applies an image recognition algorithm to image data to analyze the content of the image. The analysis unit can also apply a voice recognition algorithm to audio data. For example, the analysis unit applies a voice recognition algorithm to audio data to analyze the content of the audio. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the data category into AI and have the AI ​​apply the analysis algorithm.

[0075] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the analysis accuracy. For example, the analysis unit can extract specific patterns based on the user's past analysis results to improve the analysis accuracy. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. For example, the analysis unit can determine the priority of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, the analysis unit can provide a visually stimulating analysis result when the user is excited. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the length of the analysis.

[0077] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit adjusts the analysis schedule based on the time of submission. This enables more efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission into AI and have the AI ​​determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. For example, the analysis unit adjusts the analysis schedule based on the relevance of the data. In this way, adjusting the order of analysis based on the relevance of the data enables more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0079] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The analysis unit can also provide analysis results that are explained in simple language to a user with low level of expertise. For example, the analysis unit can provide analysis results that are explained in simple language to a user with low level of expertise. The analysis unit can also adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0080] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, the suggestion unit estimates the user's emotions and adjusts the way in which suggestions are expressed based on the estimated user's emotions. For example, when the user is nervous, the suggestion unit provides simple, highly visible suggestions. The suggestion unit can also provide detailed suggestions when the user is relaxed. For example, the suggestion unit provides detailed suggestions when the user is relaxed. The suggestion unit can also provide visually stimulating suggestions when the user is excited. For example, the suggestion unit provides visually stimulating suggestions when the user is excited. This allows for adjusting the way in which suggestions are expressed based on the user's emotions, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the suggestion is expressed.

[0081] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the solution when making the proposal. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a simplified proposal for a solution with low importance. For example, the proposal unit makes a simplified proposal for a solution with low importance. The proposal unit can also determine the priority of the proposal according to the importance of the solution. For example, the proposal unit adjusts the schedule of the proposal based on the importance of the solution. In this way, adjusting the level of detail of the proposal based on the importance of the solution enables more effective proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit may input data on the importance of the solution to AI and cause the AI ​​to adjust the level of detail of the proposal.

[0082] The suggestion unit can apply different suggestion algorithms depending on the category of the solution when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the solution when making a suggestion. For example, the suggestion unit applies a recommendation algorithm to product-related suggestions. The suggestion unit can also apply a service matching algorithm to service-related suggestions. For example, the suggestion unit applies a service matching algorithm to service-related suggestions to suggest optimal services. The suggestion unit can also apply a content filtering algorithm to content-related suggestions. For example, the suggestion unit applies a content filtering algorithm to content-related suggestions to suggest optimal content. In this way, applying different suggestion algorithms depending on the category of the solution enables more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the category of the solution into AI and cause the AI ​​to apply the suggestion algorithm.

[0083] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also extract a specific pattern from the user's past suggestion results to improve the suggestion accuracy. For example, the suggestion unit can extract a specific pattern based on the user's past suggestion results to improve the suggestion accuracy. The suggestion unit can also determine the priority of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can determine the priority of the suggestion by referring to the user's past suggestion results. As a result, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data of the user's past suggestion results into AI and cause the AI ​​to improve the accuracy of the suggestion.

[0084] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. The suggestion unit can also provide a detailed suggestion if the user is relaxed. For example, the suggestion unit can provide a detailed suggestion when the user is relaxed. The suggestion unit can also provide a visually stimulating suggestion when the user is excited. For example, the suggestion unit can provide a visually stimulating suggestion when the user is excited. This allows for adjusting the length of the suggestion based on the user's emotion to provide more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the length of the suggestions.

[0085] The proposal unit can determine the priority of proposals based on the submission time of solutions when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission time of solutions when making proposals. For example, the proposal unit prioritizes solutions with high urgency. The proposal unit can also postpone proposing solutions that were submitted earlier. For example, the proposal unit postpones proposing solutions that were submitted earlier. The proposal unit can also adjust the schedule of proposals based on the submission time. For example, the proposal unit adjusts the schedule of proposals based on the submission time. This enables more efficient proposals by determining the priority of proposals based on the submission time of solutions. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission time of solutions into AI and have the AI ​​determine the priority of proposals.

[0086] The proposal unit can adjust the order of proposals based on the relevance of the solutions when making proposals. The proposal unit, for example, adjusts the order of proposals based on the relevance of the solutions when making proposals. For example, the proposal unit prioritizes proposing highly relevant solutions. The proposal unit can also postpone proposing less relevant solutions. For example, the proposal unit postpones proposing less relevant solutions. The proposal unit can also adjust the schedule of proposals based on the relevance of the solutions. For example, the proposal unit adjusts the schedule of proposals based on the relevance of the solutions. In this way, adjusting the order of proposals based on the relevance of the solutions enables more effective proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on the relevance of the solutions to AI and cause the AI ​​to adjust the order of proposals.

[0087] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making the proposal. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to a user with high level of expertise. The suggestion unit can also provide a proposal explained in simple language to a user with low level of expertise. For example, the suggestion unit can provide a proposal explained in simple language to a user with low level of expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. For example, the suggestion unit adjusts the way the proposal is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easier to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's level of expertise into AI and cause the AI ​​to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user behavior data and feedback using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify the user's potential needs. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a solution based on the identified potential needs. The proposal unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user behavior data and feedback using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify the user's potential needs. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a solution based on the identified potential needs. The suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user behavior data and feedback using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify the user's latent needs. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a solution based on the identified latent needs. The proposal unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user behavior data and feedback using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to identify the user's latent needs. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a solution based on the identified latent needs. The proposal unit may be realized, for example, by the control unit 46A of the robot 414.

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

[0089] The analysis unit can also estimate the user's health condition based on the user's behavioral data and determine the priority of analysis based on the estimated health condition. For example, the analysis unit can analyze the user's exercise amount and sleep patterns from the user's behavioral data to estimate the health condition. The analysis unit can also analyze the user's dietary data and evaluate nutritional balance. Furthermore, the analysis unit can estimate the user's stress level and make suggestions for stress reduction. This allows for more appropriate suggestions to be made by determining the priority of analysis based on the user's health condition.

[0090] The suggestion unit can also estimate the user's hobbies and interests based on the user's behavioral data and customize the suggestions based on the estimated hobbies and interests. For example, the suggestion unit can analyze data on websites and apps frequently visited by the user to identify the user's hobbies and interests. The suggestion unit can also make related suggestions based on data on events the user has attended or products purchased by the user. Furthermore, the suggestion unit can analyze the user's social media activity and make suggestions based on topics of interest to the user. This allows the suggestion unit to customize the suggestions based on the user's hobbies and interests, enabling more personalized suggestions.

[0091] When collecting user behavioral data, the collection unit can optimize the data collection method by taking into account the user's device usage status. For example, if the user frequently uses a smartphone, the collection unit can prioritize data collection from the smartphone. Also, if the user uses a personal computer, the collection unit can collect data from the personal computer. Furthermore, if the user uses a wearable device, the collection unit can collect data from the device. This allows for more accurate data collection by optimizing the data collection method based on the user's device usage status.

[0092] The analysis unit can also estimate the user's lifestyle based on the user's behavioral data and adjust the analysis method based on the estimated lifestyle. For example, the analysis unit can analyze the user's exercise habits and eating patterns to estimate a healthy lifestyle. The analysis unit can also analyze the user's work schedule and vacation patterns to evaluate the lifestyle. Furthermore, the analysis unit can analyze the user's hobbies and leisure activities to make suggestions based on the user's lifestyle. This allows for more appropriate suggestions to be made by adjusting the analysis method based on the user's lifestyle.

[0093] The suggestion unit can also estimate the user's purchasing tendencies based on the user's behavioral data and adjust the content of suggestions based on the estimated purchasing tendencies. For example, the suggestion unit can analyze the user's past purchase history to identify purchasing tendencies. The suggestion unit can also make related suggestions based on data on products frequently viewed by the user. Furthermore, the suggestion unit can analyze the user's purchasing patterns and make suggestions based on products that the user tends to purchase at specific times. This allows for more effective suggestions by adjusting the content of suggestions based on the user's purchasing tendencies.

[0094] The analysis unit can also estimate the user's emotions and adjust the analysis feedback method based on the estimated user's emotions. For example, the analysis unit can provide positive feedback when the user is feeling stressed. The analysis unit can also provide detailed feedback when the user is relaxed. Furthermore, the analysis unit can provide visually appealing feedback when the user is excited. This allows for more appropriate feedback by adjusting the analysis feedback method based on the user's emotions.

[0095] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, the suggestion unit delays the timing of suggestions when the user is feeling stressed. The suggestion unit can also make suggestions immediately when the user is relaxed. Furthermore, the suggestion unit can also make suggestions in real time when the user is excited. This allows for more effective suggestions by adjusting the timing of suggestions based on the user's emotions.

[0096] The collection unit can also estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, the collection unit can reduce the frequency of data collection when the user is feeling stressed. The collection unit can also increase the frequency of data collection when the user is relaxed. Furthermore, the collection unit can collect data in real time when the user is excited. This allows for more appropriate data collection by adjusting the frequency of data collection based on the user's emotions.

[0097] The analysis unit can also estimate the user's emotions and adjust the visual representation of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible visual representation. If the user is relaxed, the analysis unit can also provide a detailed visual representation. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating representation. In this way, by adjusting the visual representation of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0098] The suggestion unit can also estimate the user's emotions and adjust the content of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest products and services that will help the user relax. Also, if the user is relaxed, the suggestion unit can suggest products and services that will pique the user's interest. Furthermore, if the user is excited, the suggestion unit can suggest products and services that are active. This allows for more effective suggestions by adjusting the content of suggestions based on the user's emotions.

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

[0100] Step 1: The collection unit collects user behavioral data or feedback. User behavioral data includes website browsing history, purchase history, click data, etc. The collection unit collects data on which products users viewed and which products they purchased. The collection unit can also collect feedback such as user reviews, survey results, and evaluation comments. For example, the collection unit collects reviews provided by users and uses them as data for analysis. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the user's latent needs. The analysis unit analyzes the collected data using AI and identifies users who have a high level of interest in a particular product. For example, the analysis unit uses AI to analyze behavioral data and evaluate the user's level of interest. The analysis unit can also identify the user's latent needs using sentiment analysis and trend analysis. For example, the analysis unit uses AI to analyze the user's emotions and identify the latent needs. Step 3: The suggestion unit proposes solutions based on the potential needs identified by the analysis unit. The suggestion unit provides promotional information related to a specific product. For example, the suggestion unit provides discount information related to a specific product to a user who has a high interest in that product. The suggestion unit can also make service suggestions and product recommendations. For example, the suggestion unit proposes related services based on the user's interests. Some or all of the processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can propose solutions using an AI model that takes the potential needs identified by the analysis unit as input and outputs solutions.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

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

Claims

1. a collection unit for collecting user behavior data or feedback; an analysis unit that analyzes the data collected by the collection unit and identifies potential needs of users; a proposal unit that proposes solutions based on the potential needs identified by the analysis unit; A system comprising:

2. The collecting unit Collect data on which products users view and purchase 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify users who are highly interested in specific products 2. The system of claim 1.

4. The proposal unit Providing promotional information related to specific products based on identified potential needs 2. The system of claim 1.

5. The collecting unit The system estimates user emotions and adjusts the timing of behavioral data collection based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Analyze users' past behavioral data and select the optimal collection method 2. The system of claim 1.

7. The collecting unit As behavioral data is collected, it is filtered based on the user's current life situation and areas of interest.

2. The system of claim 1.

8. The collecting unit When collecting behavioral data, select the optimal collection method depending on the user's input method.

2. The system of claim 1.

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