System

Abstract

An object of a system according to an embodiment is to generate and provide product data and a proposal based on needs of a user.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates product data or a plan based on the needs specified by the analysis unit. The provision unit provides the product data or the plan generated by the generation 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 sufficient in efficiently generating and providing product data and proposals based on user needs, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and provide product data and proposals based on the needs of users. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates product data or a proposal based on the needs identified by the analysis unit. The provision unit provides the product data or the proposal generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide product data and proposals based on the needs of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An e-commerce system according to an embodiment of the present invention generates product data and proposals from user reviews and search logs and provides them to stores and business users. The e-commerce system collects reviews and search logs, analyzes them using AI, and identifies user needs. Next, it generates product data and proposals based on the identified needs and makes them available to stores and business users. For example, the e-commerce system collects data on keywords searched by users and reviews posted by users. Next, it analyzes the collected data using AI to identify user needs. For example, the AI ​​can analyze whether a specific product is in high demand or whether specific features are desired. Next, the e-commerce system generates product data and proposals based on the identified needs. For example, it can generate a proposal for a new product with the features desired by the user. Next, the e-commerce system makes the generated product data and proposals available to stores and business users. This enables the provision of products and services that meet user needs, which is expected to increase sales for stores and business users. Furthermore, users can more easily find the products and services they truly desire, improving their satisfaction. This allows the e-commerce system to provide products and services that meet users' needs, which is expected to increase sales for stores and business users. It also makes it easier for users to find the products and services they really want, which increases their satisfaction.

[0029] An e-commerce system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on keywords searched by users and reviews posted by users. For example, the collection unit can collect data such as users' evaluations of specific products and the keywords they use to search for them. The collection unit can also collect user behavioral data, purchase data, and feedback data. The analysis unit analyzes the data collected by the collection unit to identify user needs. For example, the analysis unit can use statistical analysis or machine learning algorithms to analyze whether a specific product is in high demand or whether a specific function is desired. The analysis unit can also identify user needs based on the results of an analysis of survey results and behavioral data. The generation unit generates product data and a proposal based on the needs identified by the analysis unit. For example, the generation unit can generate a proposal for a new product that has the functions desired by the user. The generation unit can also generate specific product data such as product names, prices, and specifications. The provision unit publishes the product data and proposal generated by the generation unit to stores and business users. For example, the providing unit can publish the generated product data and proposals through a website or an application. The providing unit can also provide the generated product data and proposals to stores and business users via email or notifications. This allows the e-commerce system according to the embodiment to generate and provide product data and proposals based on user needs.

[0030] The collection unit can analyze the user's past search history and review posting history and select the optimal collection method. For example, the collection unit prioritizes collection of keywords frequently searched by the user. The collection unit can also focus on collecting reviews of products that the user has given high ratings. If the user is interested in a particular category, the collection unit can also prioritize collection of data for that category. This makes it possible to select the optimal data collection method based on the user's past behavior. 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 the user's search history data into a generation AI and have the generation AI select the optimal collection method.

[0031] When collecting data, the collection unit can perform filtering based on the user's current purchasing behavior and areas of interest. For example, the collection unit collects data related to products recently purchased by the user. The collection unit can also prioritize collecting product data in categories in which the user is interested. The collection unit can also focus on collecting reviews of products viewed by the user. This makes it possible to collect highly relevant data based on the user's current behavior. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's purchasing behavior data into a generation AI and have the generation AI perform filtering.

[0032] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects voice data. Furthermore, if the user uses text input, the collection unit can also collect text data. Furthermore, if the user uploads an image, the collection unit can also collect image data. This makes it possible to select the optimal data 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 AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data to a generation AI and have the generation AI collect the voice data.

[0033] When collecting data, the collection unit can prioritize 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 also collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data around the user's home. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0034] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data on products shared by the user on social media. The collection unit can also collect data on brands the user follows on social media. The collection unit can also collect data on products the user has "liked" on social media. This makes it possible to collect related data based on the user's social media activities. 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 social media activity data into the generation AI and cause the generation AI to collect related data.

[0035] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting data on products that the user has previously rated highly. The collection unit can also postpone collecting data on products that the user has previously rated poorly. The collection unit can also adjust the collection method based on feedback the user has previously provided. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. The analysis unit can also perform a standard analysis on data of medium importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a sentiment analysis algorithm to product reviews. The analysis unit can also apply a trend analysis algorithm to search logs. The analysis unit can also apply a purchasing pattern analysis algorithm to purchase histories. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also adjust the level of analysis detail based on the user's past analysis results. The analysis unit can also determine the priority of the analysis based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on 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 the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terms for users with high levels of expertise. The analysis unit can also avoid technical terms for users with low levels of expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms.

[0042] The generation unit can adjust the level of detail of the content to be generated based on the importance of the identified need at the time of generation. For example, the generation unit generates detailed product data and a proposal for a need of high importance. The generation unit can also generate concise product data and a proposal for a need of low importance. The generation unit can also generate standard product data and a proposal for a need of medium importance. This makes it possible to adjust the level of detail of the content to be generated depending on the importance of the identified need. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the importance of the need to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0043] During generation, the generation unit can apply different generation algorithms depending on the identified category of needs. For example, the generation unit applies a sentiment analysis algorithm to product reviews. The generation unit can also apply a trend analysis algorithm to search logs. The generation unit can also apply a purchase pattern analysis algorithm to purchase histories. This makes it possible to apply the optimal generation algorithm depending on the identified category of needs. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the category of needs into the generation AI and cause the generation AI to apply the generation algorithm.

[0044] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of generation based on the user's past generation results. The generation unit can also determine the priority of generation based on the user's past generation results. This makes it possible to improve the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] At the time of generation, the generation unit can determine the priority of content to be generated based on the time when the identified needs were collected. The generation unit, for example, prioritizes creating content to be generated based on the most recent needs. The generation unit can also postpone content to be generated based on older needs. The generation unit can also adjust the priority of content to be generated depending on the time when the needs were collected. This makes it possible to determine the priority of content to be generated based on the time when the identified needs were collected. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the time when the needs were collected into the generation AI and cause the generation AI to determine the priority of content to be generated.

[0046] The generation unit can adjust the order of content to be generated based on the relevance of the identified needs during generation. For example, the generation unit prioritizes creating content to be generated based on highly relevant needs. The generation unit can also postpone creating content to be generated based on less relevant needs. The generation unit can also adjust the order of content to be generated according to the relevance of the needs. This makes it possible to adjust the order of content to be generated based on the relevance of the identified needs. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the relevance of needs into the generation AI and cause the generation AI to adjust the order of the content to be generated.

[0047] During generation, the generation unit can adjust the use of technical terminology in the content to be generated according to the user's level of expertise. For example, the generation unit uses a lot of technical terminology for a user with high level of expertise. The generation unit can also avoid technical terminology for a user with low level of expertise. The generation unit can also adjust the use of technical terminology according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the content to be generated according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0048] At the time of provision, the providing unit can select the optimal provision method by referring to the user's past usage history. For example, the providing unit preferentially selects a provision method that the user has used in the past. The providing unit can also suggest the optimal provision method based on the user's past usage history. The providing unit can also analyze the user's past usage history and select the most effective provision method. This makes it possible to select the optimal provision method based on the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's usage history data into a generation AI and cause the generation AI to select the optimal provision method.

[0049] The providing unit can customize the content to be provided based on the user's current business situation at the time of providing. The providing unit, for example, analyzes the user's current business situation and provides optimal product data and proposals. The providing unit can also customize the content to be provided according to the user's business situation. The providing unit can also determine the priority of the content to be provided based on the user's business situation. This makes it possible to customize the content to be provided based on the user's current business situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's business situation data into a generation AI and cause the generation AI to customize the content to be provided.

[0050] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit, for example, adjusts the providing method based on user feedback. The providing unit can also improve the provided content by reflecting user feedback. The providing unit can also determine the priority of the providing method based on user feedback. This makes it possible to improve the providing method based on user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0051] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the data. For example, if the user is in a specific area, the providing unit can provide product data and proposals related to that area. Furthermore, if the user is traveling, the providing unit can also provide product data and proposals related to the travel destination. Furthermore, if the user is at home, the providing unit can preferentially provide data around the user's home. This makes it possible to select the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0052] At the time of provision, the providing unit can analyze the user's social media activity and suggest content to be offered. For example, the providing unit can provide data on products shared by the user on social media. The providing unit can also provide data on brands the user follows on social media. The providing unit can also provide data on products the user has "liked" on social media. This makes it possible to suggest content to be offered based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest content to be offered.

[0053] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit preferentially selects delivery methods that the user has previously rated highly. The providing unit can also postpone delivery methods that the user has previously rated poorly. The providing unit can also adjust the delivery method based on the user's past feedback. This makes it possible to customize the delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the delivery method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A brief analysis can also be performed on data with low importance. Furthermore, a standard analysis can be performed on data with medium importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the data. 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 the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0056] When collecting data, the collection unit can perform filtering based on the user's current purchasing behavior and areas of interest. For example, it can collect data related to products recently purchased by the user. It can also prioritize collecting product data in categories in which the user is interested. It can also focus on collecting reviews of products viewed by the user. This makes it possible to collect highly relevant data based on the user's current behavior. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's purchasing behavior data into a generation AI and have the generation AI perform filtering.

[0057] At the time of provision, the provision unit can select the optimal provision method by referring to the user's past usage history. For example, the provision unit can prioritize the selection of a provision method that the user has used in the past. The optimal provision method can also be suggested based on the user's past usage history. Furthermore, the user's past usage history can be analyzed to select the most effective provision method. This makes it possible to select the optimal provision method based on the user's past usage history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's usage history data into a generation AI and have the generation AI select the optimal provision method.

[0058] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a sentiment analysis algorithm can be applied to product reviews. A trend analysis algorithm can also be applied to search logs. Furthermore, a purchasing pattern analysis algorithm can be applied to purchase histories. This makes it possible to apply the optimal analysis algorithm depending on the data category. 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 the data category into the generation AI and have the generation AI apply the analysis algorithm.

[0059] The generation unit can adjust the level of detail of the content to be generated based on the importance of the identified need at the time of generation. For example, detailed product data and a proposal can be generated for a need of high importance. Concise product data and a proposal can also be generated for a need of low importance. Furthermore, standard product data and a proposal can also be generated for a need of medium importance. This makes it possible to adjust the level of detail of the content to be generated depending on the importance of the identified need. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the need to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects data on keywords searched by users and reviews posted by users. For example, the collection unit can collect data such as how users rate specific products and what keywords they use to search. The collection unit can also collect user behavior data, purchase data, feedback data, and so on. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies user needs. For example, the analysis unit can use statistical analysis or machine learning algorithms to analyze whether there is high demand for a particular product or whether a particular function is desired. The analysis unit can also identify user needs based on the results of questionnaire surveys and behavioral data analysis. Step 3: The generation unit generates product data and proposals based on the needs identified by the analysis unit. For example, the generation unit can generate a proposal for a new product with the features desired by the user. The generation unit can also generate specific product data such as product name, price, and specifications. Step 4: The providing unit publishes the product data and proposal generated by the generating unit to the store or business user. For example, the providing unit can publish the generated product data and proposal through a website or application. The providing unit can also provide the generated product data and proposal to the store or business user via email or notification.

[0062] (Example 2) An e-commerce system according to an embodiment of the present invention generates product data and proposals from user reviews and search logs and provides them to stores and business users. The e-commerce system collects reviews and search logs, analyzes them using AI, and identifies user needs. Next, it generates product data and proposals based on the identified needs and makes them available to stores and business users. For example, the e-commerce system collects data on keywords searched by users and reviews posted by users. Next, it analyzes the collected data using AI to identify user needs. For example, the AI ​​can analyze whether a specific product is in high demand or whether specific features are desired. Next, the e-commerce system generates product data and proposals based on the identified needs. For example, it can generate a proposal for a new product with the features desired by the user. Next, the e-commerce system makes the generated product data and proposals available to stores and business users. This enables the provision of products and services that meet user needs, which is expected to increase sales for stores and business users. Furthermore, users can more easily find the products and services they truly desire, improving their satisfaction. This allows the e-commerce system to provide products and services that meet users' needs, which is expected to increase sales for stores and business users. It also makes it easier for users to find the products and services they really want, which increases their satisfaction.

[0063] An e-commerce system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on keywords searched by users and reviews posted by users. For example, the collection unit can collect data such as users' evaluations of specific products and the keywords they use to search for them. The collection unit can also collect user behavioral data, purchase data, and feedback data. The analysis unit analyzes the data collected by the collection unit to identify user needs. For example, the analysis unit can use statistical analysis or machine learning algorithms to analyze whether a specific product is in high demand or whether a specific function is desired. The analysis unit can also identify user needs based on the results of an analysis of survey results and behavioral data. The generation unit generates product data and a proposal based on the needs identified by the analysis unit. For example, the generation unit can generate a proposal for a new product that has the functions desired by the user. The generation unit can also generate specific product data such as product names, prices, and specifications. The provision unit publishes the product data and proposal generated by the generation unit to stores and business users. For example, the providing unit can publish the generated product data and proposals through a website or an application. The providing unit can also provide the generated product data and proposals to stores and business users via email or notifications. This allows the e-commerce system according to the embodiment to generate and provide product data and proposals based on user needs.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user expresses positive emotions, the collection unit collects data immediately after posting a review. Alternatively, if the user expresses negative emotions, the collection unit can collect data after a certain period of time has elapsed. Alternatively, if the user expresses neutral emotions, the collection unit can collect data at random times. This allows data to be collected at the optimal timing depending on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0065] The collection unit can analyze the user's past search history and review posting history and select the optimal collection method. For example, the collection unit prioritizes collection of keywords frequently searched by the user. The collection unit can also focus on collecting reviews of products that the user has given high ratings. If the user is interested in a particular category, the collection unit can also prioritize collection of data for that category. This makes it possible to select the optimal data collection method based on the user's past behavior. 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 the user's search history data into a generation AI and have the generation AI select the optimal collection method.

[0066] When collecting data, the collection unit can perform filtering based on the user's current purchasing behavior and areas of interest. For example, the collection unit collects data related to products recently purchased by the user. The collection unit can also prioritize collecting product data in categories in which the user is interested. The collection unit can also focus on collecting reviews of products viewed by the user. This makes it possible to collect highly relevant data based on the user's current behavior. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's purchasing behavior data into a generation AI and have the generation AI perform filtering.

[0067] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects voice data. Furthermore, if the user uses text input, the collection unit can also collect text data. Furthermore, if the user uploads an image, the collection unit can also collect image data. This makes it possible to select the optimal data 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 AI, for example, or may be performed without using AI. For example, the collection unit can input the user's voice data to a generation AI and have the generation AI collect the voice data.

[0068] The collection unit can estimate a user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if a user is expressing positive emotions, the collection unit prioritizes collecting the user's data. Furthermore, if a user is expressing negative emotions, the collection unit can also postpone collecting the user's data. Furthermore, if a user is expressing neutral emotions, the collection unit can treat the user's data equally with other data. This allows data prioritization to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] When collecting data, the collection unit can prioritize 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 also collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data around the user's home. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0070] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data on products shared by the user on social media. The collection unit can also collect data on brands the user follows on social media. The collection unit can also collect data on products the user has "liked" on social media. This makes it possible to collect related data based on the user's social media activities. 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 social media activity data into the generation AI and cause the generation AI to collect related data.

[0071] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit prioritizes collecting data on products that the user has previously rated highly. The collection unit can also postpone collecting data on products that the user has previously rated poorly. The collection unit can also adjust the collection method based on feedback the user has previously provided. This makes it possible to customize the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and have the generation AI customize the collection method.

[0072] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is expressing a positive emotion. The analysis unit can also provide concise analysis results when the user is expressing a negative emotion. The analysis unit can also provide standard analysis results when the user is expressing a neutral emotion. This allows the way the analysis is presented to be adjusted according to the user's emotion. The 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. The analysis unit can also perform a standard analysis on data of medium importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a sentiment analysis algorithm to product reviews. The analysis unit can also apply a trend analysis algorithm to search logs. The analysis unit can also apply a purchasing pattern analysis algorithm to purchase histories. This makes it possible to apply the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also adjust the level of analysis detail based on the user's past analysis results. The analysis unit can also determine the priority of the analysis based on the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on 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 the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. For example, the analysis unit can provide a detailed analysis result when the user is expressing a positive emotion. The analysis unit can also provide a concise analysis result when the user is expressing a negative emotion. The analysis unit can also provide a standard analysis result when the user is expressing a neutral emotion. This allows the length of the analysis to be adjusted according to the user's emotion. The 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terms for users with high levels of expertise. The analysis unit can also avoid technical terms for users with low levels of expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms.

[0080] The generation unit can estimate the user's emotions and adjust the expression method of the generated product data and proposal based on the estimated user emotions. For example, when the user is expressing positive emotions, the generation unit generates detailed product data and proposals. Furthermore, when the user is expressing negative emotions, the generation unit can generate concise product data and proposals. Furthermore, when the user is expressing neutral emotions, the generation unit can generate standard product data and proposals. This allows the expression method of the generated product data and proposals to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The generation unit can adjust the level of detail of the content to be generated based on the importance of the identified need at the time of generation. For example, the generation unit generates detailed product data and a proposal for a need of high importance. The generation unit can also generate concise product data and a proposal for a need of low importance. The generation unit can also generate standard product data and a proposal for a need of medium importance. This makes it possible to adjust the level of detail of the content to be generated depending on the importance of the identified need. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the importance of the need to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0082] During generation, the generation unit can apply different generation algorithms depending on the identified category of needs. For example, the generation unit applies a sentiment analysis algorithm to product reviews. The generation unit can also apply a trend analysis algorithm to search logs. The generation unit can also apply a purchase pattern analysis algorithm to purchase histories. This makes it possible to apply the optimal generation algorithm depending on the identified category of needs. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the category of needs into the generation AI and cause the generation AI to apply the generation algorithm.

[0083] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on feedback provided by the user in the past. The generation unit can also adjust the level of detail of generation based on the user's past generation results. The generation unit can also determine the priority of generation based on the user's past generation results. This makes it possible to improve the accuracy of generation based on the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0084] The generation unit can estimate the user's emotions and adjust the length of the content to be generated based on the estimated user emotions. For example, the generation unit generates detailed product data or a proposal when the user is expressing positive emotions. The generation unit can also generate concise product data or a proposal when the user is expressing negative emotions. The generation unit can also generate standard product data or a proposal when the user is expressing neutral emotions. This allows the length of the content to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with 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 generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] At the time of generation, the generation unit can determine the priority of content to be generated based on the time when the identified needs were collected. The generation unit, for example, prioritizes creating content to be generated based on the most recent needs. The generation unit can also postpone content to be generated based on older needs. The generation unit can also adjust the priority of content to be generated depending on the time when the needs were collected. This makes it possible to determine the priority of content to be generated based on the time when the identified needs were collected. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the time when the needs were collected into the generation AI and cause the generation AI to determine the priority of content to be generated.

[0086] The generation unit can adjust the order of content to be generated based on the relevance of the identified needs during generation. For example, the generation unit prioritizes creating content to be generated based on highly relevant needs. The generation unit can also postpone creating content to be generated based on less relevant needs. The generation unit can also adjust the order of content to be generated according to the relevance of the needs. This makes it possible to adjust the order of content to be generated based on the relevance of the identified needs. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the relevance of needs into the generation AI and cause the generation AI to adjust the order of the content to be generated.

[0087] During generation, the generation unit can adjust the use of technical terminology in the content to be generated according to the user's level of expertise. For example, the generation unit uses a lot of technical terminology for a user with high level of expertise. The generation unit can also avoid technical terminology for a user with low level of expertise. The generation unit can also adjust the use of technical terminology according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the content to be generated according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0088] The providing unit can estimate the user's emotions and adjust the display method of the product data and proposals to be provided based on the estimated user emotions. For example, when the user is expressing positive emotions, the providing unit can display detailed product data and proposals. When the user is expressing negative emotions, the providing unit can also display concise product data and proposals. When the user is expressing neutral emotions, the providing unit can also display standard product data and proposals. This makes it possible to adjust the display method of the product data and proposals to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] At the time of provision, the providing unit can select the optimal provision method by referring to the user's past usage history. For example, the providing unit preferentially selects a provision method that the user has used in the past. The providing unit can also suggest the optimal provision method based on the user's past usage history. The providing unit can also analyze the user's past usage history and select the most effective provision method. This makes it possible to select the optimal provision method based on the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's usage history data into a generation AI and cause the generation AI to select the optimal provision method.

[0090] The providing unit can customize the content to be provided based on the user's current business situation at the time of providing. The providing unit, for example, analyzes the user's current business situation and provides optimal product data and proposals. The providing unit can also customize the content to be provided according to the user's business situation. The providing unit can also determine the priority of the content to be provided based on the user's business situation. This makes it possible to customize the content to be provided based on the user's current business situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's business situation data into a generation AI and cause the generation AI to customize the content to be provided.

[0091] The providing unit can improve the providing method by reflecting user feedback at the time of providing. The providing unit, for example, adjusts the providing method based on user feedback. The providing unit can also improve the provided content by reflecting user feedback. The providing unit can also determine the priority of the providing method based on user feedback. This makes it possible to improve the providing method based on user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0092] The providing unit can estimate the user's emotions and determine the priority of content to be provided based on the estimated user emotions. For example, if the user is expressing positive emotions, the providing unit can prioritize providing detailed product data or proposals. Furthermore, if the user is expressing negative emotions, the providing unit can prioritize providing concise product data or proposals. Furthermore, if the user is expressing neutral emotions, the providing unit can prioritize providing standard product data or proposals. This allows the priority of content to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing the data. For example, if the user is in a specific area, the providing unit can provide product data and proposals related to that area. Furthermore, if the user is traveling, the providing unit can also provide product data and proposals related to the travel destination. Furthermore, if the user is at home, the providing unit can preferentially provide data around the user's home. This makes it possible to select the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0094] At the time of provision, the providing unit can analyze the user's social media activity and suggest content to be offered. For example, the providing unit can provide data on products shared by the user on social media. The providing unit can also provide data on brands the user follows on social media. The providing unit can also provide data on products the user has "liked" on social media. This makes it possible to suggest content to be offered based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest content to be offered.

[0095] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit preferentially selects delivery methods that the user has previously rated highly. The providing unit can also postpone delivery methods that the user has previously rated poorly. The providing unit can also adjust the delivery method based on the user's past feedback. This makes it possible to customize the delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 reviews and search logs using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI and identifies user needs. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates product data and proposals based on the identified needs. The provision unit, realized, for example, by the control unit 46A of the smart device 14, makes the generated product data and proposals available to stores and business users. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 reviews and search logs using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI and identifies user needs. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates product data and proposals based on the identified needs. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, makes the generated product data and proposals available to stores and business users. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision 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 reviews and search logs using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits them to the data processing device 12 via the control unit 46A. 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 user needs. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates product data and proposals based on the identified needs. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and makes the generated product data and proposals available to stores and business users. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision 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 reviews and search logs using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing device 12 via the control unit 46A. 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 user needs. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates product data and proposals based on the identified needs. The provision unit is realized, for example, by the control unit 46A of the robot 414 and makes the generated product data and proposals available to stores and business users.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is expressing positive emotions, the analysis of that user's data can be prioritized. Also, if the user is expressing negative emotions, the analysis of that user's data can be postponed. Furthermore, if the user is expressing neutral emotions, the analysis of that user's data can be treated equally with other data. This allows the analysis priority to be determined according to the user's emotions. The emotion estimation is realized 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the product data and proposals to be provided based on the estimated user emotions. For example, if the user is expressing positive emotions, detailed product data and proposals can be displayed. Furthermore, if the user is expressing negative emotions, concise product data and proposals can be displayed. Furthermore, if the user is expressing neutral emotions, standard product data and proposals can be displayed. This allows the display method of the product data and proposals to be adjusted 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 providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The collection unit can estimate a user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if a user is expressing positive emotions, the collection of that user's data can be prioritized. Furthermore, if a user is expressing negative emotions, the collection of that user's data can be postponed. Furthermore, if a user is expressing neutral emotions, the data can be treated equally with other data. This allows the priority of data to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] The generation unit can estimate the user's emotions and adjust the expression method of the generated product data and proposal based on the estimated user emotions. For example, if the user is expressing positive emotions, detailed product data and proposals can be generated. Furthermore, if the user is expressing negative emotions, concise product data and proposals can be generated. Furthermore, if the user is expressing neutral emotions, standard product data and proposals can be generated. This allows the expression method of the generated product data and proposals to be adjusted 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0101] The providing unit can estimate the user's emotions and prioritize the content to be provided based on the estimated user emotions. For example, if the user is expressing positive emotions, detailed product data and proposals can be provided preferentially. Furthermore, if the user is expressing negative emotions, concise product data and proposals can be provided preferentially. Furthermore, if the user is expressing neutral emotions, standard product data and proposals can be provided preferentially. This allows the prioritization of content to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0102] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A brief analysis can also be performed on data with low importance. Furthermore, a standard analysis can be performed on data with medium importance. This makes it possible to adjust the level of detail of the analysis depending on the importance of the data. 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 the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0103] When collecting data, the collection unit can perform filtering based on the user's current purchasing behavior and areas of interest. For example, it can collect data related to products recently purchased by the user. It can also prioritize collecting product data in categories in which the user is interested. It can also focus on collecting reviews of products viewed by the user. This makes it possible to collect highly relevant data based on the user's current behavior. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's purchasing behavior data into a generation AI and have the generation AI perform filtering.

[0104] At the time of provision, the provision unit can select the optimal provision method by referring to the user's past usage history. For example, the provision unit can prioritize the selection of a provision method that the user has used in the past. The optimal provision method can also be suggested based on the user's past usage history. Furthermore, the user's past usage history can be analyzed to select the most effective provision method. This makes it possible to select the optimal provision method based on the user's past usage history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's usage history data into a generation AI and have the generation AI select the optimal provision method.

[0105] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a sentiment analysis algorithm can be applied to product reviews. A trend analysis algorithm can also be applied to search logs. Furthermore, a purchasing pattern analysis algorithm can be applied to purchase histories. This makes it possible to apply the optimal analysis algorithm depending on the data category. 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 the data category into the generation AI and have the generation AI apply the analysis algorithm.

[0106] The generation unit can adjust the level of detail of the content to be generated based on the importance of the identified need at the time of generation. For example, detailed product data and a proposal can be generated for a need of high importance. Concise product data and a proposal can also be generated for a need of low importance. Furthermore, standard product data and a proposal can also be generated for a need of medium importance. This makes it possible to adjust the level of detail of the content to be generated depending on the importance of the identified need. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the need to the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects data on keywords searched by users and reviews posted by users. For example, the collection unit can collect data such as how users rate specific products and what keywords they use to search. The collection unit can also collect user behavior data, purchase data, feedback data, and so on. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies user needs. For example, the analysis unit can use statistical analysis or machine learning algorithms to analyze whether there is high demand for a particular product or whether a particular function is desired. The analysis unit can also identify user needs based on the results of questionnaire surveys and behavioral data analysis. Step 3: The generation unit generates product data and proposals based on the needs identified by the analysis unit. For example, the generation unit can generate a proposal for a new product with the features desired by the user. The generation unit can also generate specific product data such as product name, price, and specifications. Step 4: The providing unit publishes the product data and proposal generated by the generating unit to the store or business user. For example, the providing unit can publish the generated product data and proposal through a website or application. The providing unit can also provide the generated product data and proposal to the store or business user via email or notification.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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, to avoid confusion and 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates product data or a business plan based on the needs identified by the analysis unit; a providing unit that provides the product data or the business plan generated by the generating unit. A system characterized by:

2. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze users' past search history or review posting history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit At the time of data collection, filtering based on the user's current purchasing behavior or areas of interest 2. The system of claim 1.

5. The collecting unit When collecting data, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting data, consider the user's geographic location to prioritize collecting the most relevant data.

2. The system of claim 1.

8. The collecting unit When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1.

Images