System

Abstract

An object of the system according to the embodiment is to effectively collect and accumulate personal usage data and utilize the data for a marketing strategy.SOLUTION: A system includes a collection unit, an accumulation unit, an analysis unit, and a planning unit. The collection unit collects a CookieID from each service. The storage part stores the use data of the individual for a fixed period based on the CookieID collected by the collection part. The analysis unit analyzes the data accumulated by the accumulation unit and grasps personal hobbies and preferences or usage tendencies. The formulation unit formulates a marketing strategy based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to effectively collect and store personal usage data and fully utilize it in marketing strategies.

[0005] The system according to the embodiment aims to effectively collect and store personal usage data and utilize it in marketing strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a storage unit, an analysis unit, and a formulation unit. The collection unit collects cookie IDs from each service. The storage unit stores personal usage data for a certain period of time based on the cookie IDs collected by the collection unit. The analysis unit analyzes the data stored by the storage unit to understand personal hobbies, preferences, or usage trends. The formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively collect and store personal usage data and utilize it for marketing strategies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data management system according to an embodiment of the present invention accumulates personal usage data over the long term by aggregating cookie IDs and collects mass data on the usage status and hobbies and preferences of various services. The data management system collects cookie IDs from each service and manages them centrally. Next, based on the collected cookie IDs, it accumulates personal usage data over the long term. This data includes the usage status of each service and personal hobbies and preferences. For example, the data management system can collect cookie IDs from various services, such as e-commerce sites and social networking sites. This allows for centralized management of personal usage data. Next, based on the collected cookie IDs, it accumulates personal usage data over the long term. This data includes the usage status of each service and personal hobbies and preferences. For example, this data includes purchase history on e-commerce sites and content posted on social networking sites. This allows for the long-term accumulation of personal usage data and detailed data analysis. Furthermore, the data management system analyzes personal hobbies and preferences and usage trends based on the accumulated data. For example, it can analyze the frequency of purchases of specific products and the content posted in a specific genre. This allows for an understanding of personal hobbies, preferences, and usage trends, which can be used to formulate marketing strategies and improve services. This allows the data management system to centrally manage personal usage data and perform long-term data analysis. For example, it can promote related products to users who frequently purchase a particular product. It can also provide related content to users who frequently post content in a particular genre.

[0029] A data management system according to an embodiment includes a collection unit, a storage unit, an analysis unit, and a formulation unit. The collection unit collects cookie IDs from each service. The collection unit can collect cookie IDs from various services, such as e-commerce sites and social networking sites. The collection unit can also build a system for centrally managing cookie IDs issued by each service. For example, the collection unit can automatically collect cookie IDs from each service using an API. The storage unit stores personal usage data over the long term based on the cookie IDs collected by the collection unit. The storage unit can store data including, for example, purchase history on e-commerce sites and content posted on social networking sites. The storage unit can also encrypt and store the collected data. For example, the storage unit encrypts the data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). The storage unit can also anonymize the collected data. For example, the storage unit anonymizes the data using techniques such as data masking and pseudo-anonymization. The analysis unit analyzes the data accumulated by the accumulation unit to understand individual hobbies, preferences, and usage trends. The analysis unit can analyze the data using AI technologies such as machine learning and deep learning. For example, the analysis unit can analyze the purchase frequency of a specific product and the content of posts in a specific genre. The formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. The formulation unit can formulate a marketing strategy including, for example, a target market and promotion methods. This enables the data management system according to the embodiment to centrally manage individual usage data and perform long-term data analysis. For example, it is possible to promote related products to users who frequently purchase a specific product. It is also possible to provide related content to users who frequently post content in a specific genre.

[0030] The collection unit may automatically collect cookie IDs from each service. For example, the collection unit may automatically collect cookie IDs from each service using an API. For example, the collection unit may automatically collect cookie IDs from each service using a script. The collection unit may also include a process for obtaining permission for data collection from each service. For example, the collection unit may include a process for complying with laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) to ensure legal compliance with data collection from each service. This allows for efficient collection of cookie IDs from each service. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may automate the collection process using an AI model for collecting cookie IDs from each service.

[0031] The storage unit can securely encrypt and store personal usage data based on the collected cookie ID. The storage unit can encrypt data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). For example, the storage unit can encrypt data using AES and securely store the encrypted data. The storage unit can also encrypt data using RSA and securely store the encrypted data. Furthermore, the storage unit can encrypt data using hybrid encryption technology. For example, the storage unit can encrypt data using hybrid encryption technology that combines AES and RSA and securely store the encrypted data. This allows for secure storage of personal usage data. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can automate the data encryption process using an AI model.

[0032] The storage unit can anonymize the collected data in a specific manner. For example, the storage unit can anonymize the data using data masking technology. For example, the storage unit can use data masking technology to conceal personally identifiable information and anonymize the data. The storage unit can also anonymize the data using pseudo-anonymization technology. For example, the storage unit can use pseudo-anonymization technology to replace personally identifiable information and anonymize the data. The storage unit can also anonymize the data using k-anonymization technology. For example, the storage unit can use k-anonymization technology to group personally identifiable information and anonymize the data. This enhances the protection of personal information. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can automate the data anonymization process using an AI model.

[0033] The collection unit may include a process for ensuring legal compliance regarding data collection from each service. The collection unit may include a process for complying with laws and regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). For example, the collection unit may include a process for obtaining user consent regarding data collection under the GDPR. The collection unit may also include a process for protecting user rights regarding data collection under the CCPA. Furthermore, the collection unit may include a process for obtaining permission regarding data collection from each service. For example, the collection unit may include a process for obtaining permission regarding data collection from each service and ensuring legal compliance. This ensures the lawfulness of data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may automate the process using an AI model to ensure legal compliance regarding data collection.

[0034] The analysis unit can use AI to analyze individual hobbies, preferences, and usage trends based on the accumulated data. The analysis unit can analyze the data using AI technologies such as machine learning and deep learning. For example, the analysis unit can use a machine learning algorithm to analyze individual hobbies, preferences, and usage trends. The analysis unit can also use a deep learning model to analyze individual hobbies, preferences, and usage trends. Furthermore, the analysis unit can use natural language processing technology to analyze content posted on SNS and understand individual hobbies, preferences, and usage trends. For example, the analysis unit can use natural language processing technology to extract content posted in a specific genre from the content posted on SNS and analyze the individual hobbies, preferences, and usage trends. This allows for a highly accurate understanding of individual hobbies, preferences, and usage trends. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can automate the data analysis process using an AI model.

[0035] The formulation unit can formulate a marketing strategy based on the analysis results. The formulation unit can formulate a marketing strategy that includes, for example, a target market and a promotion method. For example, the formulation unit can formulate a marketing strategy that promotes related products to users who frequently purchase a specific product. The formulation unit can also formulate a marketing strategy that provides related content to users who frequently post content in a specific genre. Furthermore, the formulation unit can include a process that evaluates the effectiveness of the marketing strategy based on the analysis results and improves the strategy. For example, the formulation unit includes a process that evaluates the effectiveness of the marketing strategy and improves the strategy if the effectiveness is low. This allows for the formulation of an effective marketing strategy. Some or all of the above-mentioned processing in the formulation unit can be performed, for example, using AI, or can be performed without using AI. For example, the formulation unit can automate the marketing strategy formulation process using an AI model.

[0036] When collecting cookie IDs from each service, the collection unit can select an appropriate collection method based on the user's past usage history. The collection unit can, for example, analyze the user's past usage history and select the most efficient collection method. For example, the collection unit prioritizes collecting cookie IDs from services the user frequently uses. The collection unit can also collect cookie IDs from services used during a specific time period based on the user's past usage history. Furthermore, the collection unit can collect the most relevant cookie IDs based on the user's past usage history. For example, the collection unit collects cookie IDs for a specific time period from services used by the user during that time period. This enables efficient collection of cookie IDs taking the user's past usage history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can select the optimal collection method using an AI model for analyzing the user's past usage history.

[0037] When collecting cookie IDs, the collection unit can perform filtering based on the user's current usage and areas of interest. The collection unit can, for example, analyze the user's current usage and collect the most relevant cookie IDs. For example, the collection unit can prioritize collecting cookie IDs from services currently used by the user. The collection unit can also collect cookie IDs from services related to the user's areas of interest. Furthermore, the collection unit can collect the most relevant cookie IDs based on the user's current usage. For example, the collection unit can analyze the user's current usage and collect the most relevant cookie IDs. This enables effective collection of cookie IDs based on the user's current usage and areas of interest. Some or all of the above-described 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 perform filtering using an AI model for analyzing the user's current usage and areas of interest.

[0038] When collecting cookie IDs, the collection unit can select an appropriate collection method depending on the user's input method. For example, if the user uses voice input, the collection unit can collect cookie IDs from voice data. For example, the collection unit can collect cookie IDs from voice data using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect cookie IDs from text data. For example, the collection unit can collect cookie IDs from text data using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect cookie IDs from image data. For example, the collection unit can collect cookie IDs from image data using image recognition technology. This enables optimal collection of cookie IDs depending on the user's input method. 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 automate the collection process using an AI model for selecting the optimal collection method depending on the user's input method.

[0039] When collecting cookie IDs, the collection unit can prioritize collection of relevant cookie IDs taking into account the user's geographical location information. The collection unit can, for example, collect the most relevant cookie IDs based on the user's geographical location information. For example, the collection unit collects cookie IDs from services related to the user's current location. The collection unit can also analyze the user's past location information and collect highly relevant cookie IDs. Furthermore, the collection unit can collect the most relevant cookie IDs based on the user's current geographical location information. For example, the collection unit collects the most relevant cookie IDs based on the user's current geographical location information. This enables effective collection of cookie IDs based on the user's geographical location information. 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 select the optimal collection method using an AI model for analyzing the user's geographical location information.

[0040] When collecting cookie IDs, the collection unit can analyze the user's social media activities and collect related cookie IDs. For example, the collection unit can analyze the user's social media activities and collect the most relevant cookie IDs. For example, the collection unit can collect cookie IDs related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related cookie IDs. Furthermore, the collection unit can collect related cookie IDs by referring to the activities of the user's friends on social media. For example, the collection unit can collect related cookie IDs by referring to the activities of the user's friends on social media. This enables effective collection of cookie IDs based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the optimal collection method using an AI model for analyzing the user's social media activities.

[0041] When collecting cookie IDs, the collection unit can customize an appropriate collection method by reflecting the user's past feedback. The collection unit can select the optimal collection method based on the user's past feedback, for example. For example, the collection unit analyzes the user's past feedback and customizes the collection method. The collection unit can also adjust the collection method by reflecting the user's feedback in real time. Furthermore, the collection unit can optimize the collection method based on the user's feedback. For example, the collection unit optimizes the collection method based on the user's feedback. This enables optimal cookie ID collection based on the user's past feedback. 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 customize the collection method using an AI model for analyzing user feedback.

[0042] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit can evaluate the importance of the data and store data of high importance in detail. For example, the storage unit stores data of high importance in detail and stores data of low importance in a simplified form. The storage unit can also adjust the frequency of storage based on the importance of the data. For example, the storage unit stores data of high importance in real time and stores data of low importance in batch processing. Furthermore, the storage unit can adjust the level of detail of storage based on the importance of the data. For example, the storage unit stores data of high importance in detail and stores data of low importance in a simplified form. This enables optimal data storage based on the importance of the data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can adjust the level of detail of storage using an AI model for evaluating the importance of data.

[0043] The storage unit can apply different storage algorithms depending on the data category during storage. For example, the storage unit can apply a storage algorithm dedicated to text to text data. For example, the storage unit applies a storage algorithm dedicated to text to text data, thereby efficiently storing the data. The storage unit can also apply a storage algorithm dedicated to images to image data. For example, the storage unit applies a storage algorithm dedicated to images to image data, thereby efficiently storing the data. The storage unit can also apply a storage algorithm dedicated to audio to audio data. For example, the storage unit applies a storage algorithm dedicated to audio to audio data, thereby efficiently storing the data. This enables optimal data storage depending on the data category. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can automate the storage process using an AI model for applying an optimal storage algorithm depending on the data category.

[0044] During data storage, the storage unit can improve the accuracy of storage by referring to the user's past storage results. The storage unit can, for example, analyze the user's past storage results and optimize the storage algorithm. For example, the storage unit can analyze the user's past storage results and optimize the storage algorithm. The storage unit can also adjust the level of detail of storage based on the user's past storage results. For example, the storage unit adjusts the level of detail of storage based on the user's past storage results. Furthermore, the storage unit can also adjust the frequency of storage by referring to the user's past storage results. For example, the storage unit adjusts the frequency of storage by referring to the user's past storage results. This enables optimal data storage by referring to the user's past storage results. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can improve the accuracy of storage by using an AI model for analyzing the user's past storage results.

[0045] The storage unit can determine the storage priority based on the time of data submission when storing data. The storage unit can, for example, preferentially store the most relevant data based on the time of data submission. For example, the storage unit preferentially stores the most recent data. The storage unit can also store data that was submitted earlier later. Furthermore, the storage unit can adjust the data storage order based on the time of submission. For example, the storage unit adjusts the data storage order based on the time of submission. This enables optimal data storage based on the time of data submission. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can determine the storage priority using an AI model for evaluating the time of data submission.

[0046] The storage unit can adjust the order of storage based on the relevance of the data during storage. The storage unit can, for example, evaluate the relevance of the data and store highly relevant data preferentially. For example, the storage unit stores highly relevant data preferentially and stores less relevant data later. The storage unit can also adjust the order of storage based on the relevance of the data. For example, the storage unit adjusts the order of storage based on the relevance of the data. This enables optimal data storage based on the relevance of the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can adjust the order of storage using an AI model for evaluating the relevance of the data.

[0047] The storage unit can adjust the data anonymization method according to the user's level of expertise during storage. For example, the storage unit can evaluate the user's level of expertise and apply a detailed anonymization method to a user with high expertise. For example, the storage unit can apply a detailed anonymization method to a user with high expertise and a simplified anonymization method to a user with low expertise. The storage unit can also adjust the anonymization method based on the user's level of expertise. For example, the storage unit adjusts the anonymization method based on the user's level of expertise. This enables optimal data anonymization according to the user's level of expertise. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can adjust the anonymization method using an AI model for evaluating the user's level of expertise.

[0048] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit can, for example, analyze the interrelationships between data and prioritize the analysis of highly related data. For example, the analysis unit analyzes the interrelationships between data and prioritizes the analysis of highly related data. The analysis unit can also optimize the analysis algorithm by taking into account the interrelationships between data. For example, the analysis unit optimizes the analysis algorithm by taking into account the interrelationships between data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the interrelationships between data. For example, the analysis unit adjusts the level of detail of the analysis based on the interrelationships between data. This enables highly accurate analysis that takes into account the interrelationships between 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 improve the accuracy of the analysis by using an AI model for analyzing the interrelationships between data.

[0049] The analysis unit can perform the analysis taking into account attribute information of the data submitter during the analysis. The analysis unit can perform the analysis taking into account, for example, the age and gender of the data submitter. For example, the analysis unit can perform the analysis taking into account the age and gender of the data submitter. The analysis unit can also perform the analysis taking into account the occupation and hobbies of the data submitter. For example, the analysis unit can perform the analysis taking into account the occupation and hobbies of the data submitter. Furthermore, the analysis unit can perform the analysis taking into account regional information of the data submitter. For example, the analysis unit can perform the analysis taking into account regional information of the data submitter. This enables optimal analysis taking into account the attribute information of the data submitter. Some or all of the above-described 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 perform the analysis using an AI model for analyzing attribute information of the data submitter.

[0050] During analysis, the analysis unit can weight the analysis based on the frequency of data submission. The analysis unit can, for example, prioritize the analysis of the most relevant data based on the frequency of data submission. For example, the analysis unit prioritizes analysis of data with a high submission frequency. The analysis unit can also postpone analysis of data with a low submission frequency. Furthermore, the analysis unit can adjust the weighting of the data based on the submission frequency. For example, the analysis unit adjusts the weighting of the data based on the submission frequency. This enables optimal analysis based on the frequency of data submission. Some or all of the above-described 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 weight the analysis using an AI model for evaluating the frequency of data submission.

[0051] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit can perform an analysis for each region based on the geographical distribution of the data. For example, the analysis unit performs an analysis for each region based on the geographical distribution of the data. The analysis unit can also optimize the analysis algorithm taking into account the geographical distribution. For example, the analysis unit optimizes the analysis algorithm taking into account the geographical distribution. Furthermore, the analysis unit can display the analysis results for each region based on the geographical distribution. For example, the analysis unit displays the analysis results for each region based on the geographical distribution. This enables optimal analysis taking into account the geographical distribution of the data. Some or all of the above-described 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 perform the analysis using an AI model for evaluating the geographical distribution of the data.

[0052] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. The analysis unit can, for example, optimize the analysis algorithm by referring to literature related to the data. For example, the analysis unit optimizes the analysis algorithm by referring to the literature. The analysis unit can also interpret the data based on the literature. For example, the analysis unit interprets the data based on the literature. Furthermore, the analysis unit can also improve the reliability of the analysis results by referring to the literature. For example, the analysis unit improves the reliability of the analysis results by referring to the literature. This enables highly accurate analysis by referring to literature related to the data. Some or all of the above-described 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 improve the accuracy of the analysis by using an AI model for referring to the literature.

[0053] The analysis unit can perform the analysis taking into account the market value of the data. For example, the analysis unit can prioritize analysis of data with high importance based on the market value of the data. For example, the analysis unit prioritizes analysis of data with high importance based on the market value of the data. The analysis unit can also optimize the analysis algorithm taking into account the market value. For example, the analysis unit optimizes the analysis algorithm taking into account the market value. Furthermore, the analysis unit can also display the analysis results based on the market value. For example, the analysis unit displays the analysis results based on the market value. This enables optimal analysis taking into account the market value of the data. Some or all of the above-described 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 perform the analysis using an AI model for evaluating the market value of the data.

[0054] The formulation unit can appropriately optimize the current strategy by referring to past marketing strategy data during formulation. The formulation unit can, for example, analyze past marketing strategy data and formulate an optimal strategy. For example, the formulation unit analyzes past marketing strategy data and formulates an optimal strategy. The formulation unit can also optimize the current strategy based on past success cases. For example, the formulation unit optimizes the current strategy based on past success cases. Furthermore, the formulation unit can also improve the current strategy by referring to past failure cases. For example, the formulation unit improves the current strategy by referring to past failure cases. This makes it possible to formulate an optimal strategy by referring to past marketing strategy data. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can optimize the current strategy by using an AI model for analyzing past marketing strategy data.

[0055] The formulation unit can update the marketing strategy by reflecting user feedback during formulation. The formulation unit can, for example, adjust the marketing strategy based on user feedback. For example, the formulation unit adjusts the marketing strategy based on user feedback. The formulation unit can also analyze user feedback and identify areas for improvement in the strategy. For example, the formulation unit analyzes user feedback and identifies areas for improvement in the strategy. Furthermore, the formulation unit can also reflect user feedback in real time and update the strategy. For example, the formulation unit reflects user feedback in real time and updates the strategy. This makes it possible to update an optimal marketing strategy that reflects user feedback. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can update the marketing strategy using an AI model for analyzing user feedback.

[0056] The formulation unit can customize an appropriate strategy based on the user's current market situation during formulation. The formulation unit can, for example, analyze the current market situation and formulate an optimal marketing strategy. For example, the formulation unit analyzes the current market situation and formulates an optimal marketing strategy. The formulation unit can also customize the strategy taking market trends into consideration. For example, the formulation unit customizes the strategy taking market trends into consideration. Furthermore, the formulation unit can also optimize the strategy based on the competitive situation in the market. For example, the formulation unit optimizes the strategy based on the competitive situation in the market. This makes it possible to formulate an optimal strategy based on the user's current market situation. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can customize the strategy using an AI model for analyzing the current market situation.

[0057] During formulation, the formulation unit can weight appropriate strategies based on the time of data submission. The formulation unit can, for example, prioritize analysis of the most relevant data based on the time of data submission. For example, the formulation unit weights strategies based on the most recent data. The formulation unit can also lower the weight of data submitted earlier. Furthermore, the formulation unit can determine the priority of strategies based on the time of submission. For example, the formulation unit determines the priority of strategies based on the time of submission. This enables optimal weighting of strategies based on the time of data submission. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can weight strategies using an AI model for evaluating the time of data submission.

[0058] During formulation, the formulation unit can integrate information from different data sources to enhance the strategy. For example, the formulation unit can integrate information from different data sources to formulate a comprehensive strategy. For example, the formulation unit integrates information from different data sources to formulate a comprehensive strategy. The formulation unit can also optimize the strategy by taking into account the characteristics of each data source. For example, the formulation unit optimizes the strategy by taking into account the characteristics of each data source. Furthermore, the formulation unit can adjust the level of detail of the strategy based on information from different data sources. For example, the formulation unit adjusts the level of detail of the strategy based on information from different data sources. This makes it possible to formulate an optimal strategy that integrates information from different data sources. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can enhance the strategy by using an AI model for integrating information from different data sources.

[0059] The formulation unit can customize an appropriate strategy by reflecting the user's past feedback during formulation. The formulation unit can, for example, adjust the strategy based on the user's past feedback. For example, the formulation unit adjusts the strategy based on the user's past feedback. The formulation unit can also analyze the user's past feedback and identify areas for improvement in the strategy. For example, the formulation unit analyzes the user's past feedback and identifies areas for improvement in the strategy. Furthermore, the formulation unit can reflect the user's past feedback in real time and update the strategy. For example, the formulation unit reflects the user's past feedback in real time and updates the strategy. This makes it possible to formulate an optimal strategy that reflects the user's past feedback. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can customize the strategy using an AI model for analyzing the user's past feedback.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit may adjust the collection method of the cookie ID based on the type of device used by the user. For example, the collection unit may use a mobile-optimized API to collect data from a smartphone and a higher-bandwidth API to collect data from a desktop. The collection unit may also select the optimal collection method based on the type of browser used by the user. For example, the collection unit may use a particular extension to collect data from a Chrome browser and a different extension to collect data from a Safari browser. The collection unit may also adjust the frequency and timing of data collection based on the user's network connection status. For example, the collection unit may prioritize data collection when the user is connected to Wi-Fi and refrain from data collection when the user is connected to mobile data. This allows for optimal data collection based on the user's device and connection status.

[0062] The storage unit can select a data storage location based on the user's geographic location. For example, if the user is in Europe, the storage unit can store the data on a server in Europe, and if the user is in Asia, the storage unit can store the data on a server in Asia. The storage unit can also select a data storage location based on the user's privacy settings. For example, if the user desires high privacy protection, the storage unit can store the data in encrypted cloud storage, and if the user desires low privacy protection, the storage unit can store the data on a local server. The storage unit can also select a data storage location based on the user's data usage purpose. For example, the storage unit can store data intended for data analysis in high-speed accessible storage and data intended for backup in low-cost storage. This enables optimal data storage according to the user's geographic location, privacy settings, and data usage purpose.

[0063] The collection unit can adjust the frequency of data collection based on the remaining battery level of the user's device. For example, the collection unit can increase the frequency of data collection when the remaining battery level of the user's device is high, and decrease the frequency of data collection when the remaining battery level is low. The collection unit can also prioritize data collection when the user's device is charging, and refrain from data collection when the device is not charging. Furthermore, the collection unit can adjust the method of data collection based on the remaining battery level of the user's device. For example, detailed data can be collected when the remaining battery level is high, and simplified data can be collected when the remaining battery level is low. This enables optimal data collection according to the remaining battery level of the user's device.

[0064] The storage unit can adjust the data storage period based on the user's contract plan. For example, the storage unit can store data for a long period of time for users with a premium plan and for a short period of time for users with a basic plan. The storage unit can also adjust the data storage capacity based on the user's contract plan. For example, it can provide large-capacity storage for users with a premium plan and small-capacity storage for users with a basic plan. Furthermore, the storage unit can adjust the data backup frequency based on the user's contract plan. For example, it can back up data more frequently for users with a premium plan and back up data less frequently for users with a basic plan. This enables optimal data storage based on the user's contract plan.

[0065] The analysis unit can apply different analysis algorithms depending on the type of data. For example, it can apply a natural language processing algorithm to text data and an image recognition algorithm to image data. The analysis unit can also apply a voice analysis algorithm to audio data and a video analysis algorithm to video data. Furthermore, the analysis unit can also apply a time series analysis algorithm to sensor data. For example, it can apply a time series analysis algorithm to sensor data to analyze the fluctuation patterns of the data. This enables optimal analysis depending on the type of data.

[0066] The planning unit can customize a marketing strategy based on a user's purchasing history. For example, the planning unit can analyze trends in products purchased by the user in the past and promote related products. The planning unit can also adjust the timing of promotions based on the user's purchasing frequency. For example, promotions can be run periodically for users who purchase frequently, and promotions can be run at specific event times for users who purchase infrequently. Furthermore, the planning unit can adjust the content of promotions based on the user's purchase amount. For example, special discounts can be offered to users who purchase high-priced items, and point rewards can be offered to users who purchase low-priced items. This makes it possible to formulate an optimal marketing strategy based on the user's purchasing history.

[0067] The planning department can adjust the marketing strategy based on the user's influence on social media. For example, the planning department can conduct influencer marketing for users with a large number of followers and general promotions for users with a small number of followers. The planning department can also adjust the content of promotions based on the user's engagement rate on social media. For example, it can provide special campaigns to users with a high engagement rate and general campaigns to users with a low engagement rate. Furthermore, the planning department can narrow down the target of promotions based on the content of the user's posts on social media. For example, it can promote related products to users who post a lot about a specific genre. This makes it possible to develop an optimal marketing strategy according to the user's influence on social media.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects cookie IDs from each service. The collection unit can collect cookie IDs from various services, such as e-commerce sites and social networking sites. The collection unit can also build a system for centrally managing cookie IDs issued by each service. For example, the collection unit can automatically collect cookie IDs from each service using an API. Step 2: The storage unit stores personal usage data over the long term based on the cookie ID collected by the collection unit. The storage unit can store data including, for example, purchase history on e-commerce sites and content posted on social media. The storage unit can also encrypt and store the collected data. For example, the storage unit encrypts the data using encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The storage unit can also anonymize the collected data. For example, the storage unit anonymizes the data using techniques such as data masking and pseudo-anonymization. Step 3: The analysis unit analyzes the data accumulated by the accumulation unit to understand individual hobbies, preferences, and usage trends. The analysis unit can analyze the data using AI technologies such as machine learning and deep learning. For example, the analysis unit can analyze the purchase frequency of a particular product or the content of posts in a particular genre. Step 4: The formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. The formulation unit can formulate a marketing strategy that includes, for example, a target market and promotion methods. This allows the data management system according to the embodiment to centrally manage personal usage data and enable long-term data analysis. For example, related products can be promoted to users who frequently purchase a particular product. Also, related content can be provided to users who frequently post content in a particular genre.

[0070] (Example 2) A data management system according to an embodiment of the present invention accumulates personal usage data over the long term by aggregating cookie IDs and collects mass data on the usage status and hobbies and preferences of various services. The data management system collects cookie IDs from each service and manages them centrally. Next, based on the collected cookie IDs, it accumulates personal usage data over the long term. This data includes the usage status of each service and personal hobbies and preferences. For example, the data management system can collect cookie IDs from various services, such as e-commerce sites and social networking sites. This allows for centralized management of personal usage data. Next, based on the collected cookie IDs, it accumulates personal usage data over the long term. This data includes the usage status of each service and personal hobbies and preferences. For example, this data includes purchase history on e-commerce sites and content posted on social networking sites. This allows for the long-term accumulation of personal usage data and detailed data analysis. Furthermore, the data management system analyzes personal hobbies and preferences and usage trends based on the accumulated data. For example, it can analyze the frequency of purchases of specific products and the content posted in a specific genre. This allows for an understanding of personal hobbies, preferences, and usage trends, which can be used to formulate marketing strategies and improve services. This allows the data management system to centrally manage personal usage data and perform long-term data analysis. For example, it can promote related products to users who frequently purchase a particular product. It can also provide related content to users who frequently post content in a particular genre.

[0071] A data management system according to an embodiment includes a collection unit, a storage unit, an analysis unit, and a formulation unit. The collection unit collects cookie IDs from each service. The collection unit can collect cookie IDs from various services, such as e-commerce sites and social networking sites. The collection unit can also build a system for centrally managing cookie IDs issued by each service. For example, the collection unit can automatically collect cookie IDs from each service using an API. The storage unit stores personal usage data over the long term based on the cookie IDs collected by the collection unit. The storage unit can store data including, for example, purchase history on e-commerce sites and content posted on social networking sites. The storage unit can also encrypt and store the collected data. For example, the storage unit encrypts the data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). The storage unit can also anonymize the collected data. For example, the storage unit anonymizes the data using techniques such as data masking and pseudo-anonymization. The analysis unit analyzes the data accumulated by the accumulation unit to understand individual hobbies, preferences, and usage trends. The analysis unit can analyze the data using AI technologies such as machine learning and deep learning. For example, the analysis unit can analyze the purchase frequency of a specific product and the content of posts in a specific genre. The formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. The formulation unit can formulate a marketing strategy including, for example, a target market and promotion methods. This enables the data management system according to the embodiment to centrally manage individual usage data and perform long-term data analysis. For example, it is possible to promote related products to users who frequently purchase a specific product. It is also possible to provide related content to users who frequently post content in a specific genre.

[0072] The collection unit may automatically collect cookie IDs from each service. For example, the collection unit may automatically collect cookie IDs from each service using an API. For example, the collection unit may automatically collect cookie IDs from each service using a script. The collection unit may also include a process for obtaining permission for data collection from each service. For example, the collection unit may include a process for complying with laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) to ensure legal compliance with data collection from each service. This allows for efficient collection of cookie IDs from each service. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may automate the collection process using an AI model for collecting cookie IDs from each service.

[0073] The storage unit can securely encrypt and store personal usage data based on the collected cookie ID. The storage unit can encrypt data using an encryption algorithm such as AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman). For example, the storage unit can encrypt data using AES and securely store the encrypted data. The storage unit can also encrypt data using RSA and securely store the encrypted data. Furthermore, the storage unit can encrypt data using hybrid encryption technology. For example, the storage unit can encrypt data using hybrid encryption technology that combines AES and RSA and securely store the encrypted data. This allows for secure storage of personal usage data. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can automate the data encryption process using an AI model.

[0074] The storage unit can anonymize the collected data in a specific manner. For example, the storage unit can anonymize the data using data masking technology. For example, the storage unit can use data masking technology to conceal personally identifiable information and anonymize the data. The storage unit can also anonymize the data using pseudo-anonymization technology. For example, the storage unit can use pseudo-anonymization technology to replace personally identifiable information and anonymize the data. The storage unit can also anonymize the data using k-anonymization technology. For example, the storage unit can use k-anonymization technology to group personally identifiable information and anonymize the data. This enhances the protection of personal information. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can automate the data anonymization process using an AI model.

[0075] The collection unit may include a process for ensuring legal compliance regarding data collection from each service. The collection unit may include a process for complying with laws and regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). For example, the collection unit may include a process for obtaining user consent regarding data collection under the GDPR. The collection unit may also include a process for protecting user rights regarding data collection under the CCPA. Furthermore, the collection unit may include a process for obtaining permission regarding data collection from each service. For example, the collection unit may include a process for obtaining permission regarding data collection from each service and ensuring legal compliance. This ensures the lawfulness of data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may automate the process using an AI model to ensure legal compliance regarding data collection.

[0076] The analysis unit can use AI to analyze individual hobbies, preferences, and usage trends based on the accumulated data. The analysis unit can analyze the data using AI technologies such as machine learning and deep learning. For example, the analysis unit can use a machine learning algorithm to analyze individual hobbies, preferences, and usage trends. The analysis unit can also use a deep learning model to analyze individual hobbies, preferences, and usage trends. Furthermore, the analysis unit can use natural language processing technology to analyze content posted on SNS and understand individual hobbies, preferences, and usage trends. For example, the analysis unit can use natural language processing technology to extract content posted in a specific genre from the content posted on SNS and analyze the individual hobbies, preferences, and usage trends. This allows for a highly accurate understanding of individual hobbies, preferences, and usage trends. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can automate the data analysis process using an AI model.

[0077] The formulation unit can formulate a marketing strategy based on the analysis results. The formulation unit can formulate a marketing strategy that includes, for example, a target market and a promotion method. For example, the formulation unit can formulate a marketing strategy that promotes related products to users who frequently purchase a specific product. The formulation unit can also formulate a marketing strategy that provides related content to users who frequently post content in a specific genre. Furthermore, the formulation unit can include a process that evaluates the effectiveness of the marketing strategy based on the analysis results and improves the strategy. For example, the formulation unit includes a process that evaluates the effectiveness of the marketing strategy and improves the strategy if the effectiveness is low. This allows for the formulation of an effective marketing strategy. Some or all of the above-mentioned processing in the formulation unit can be performed, for example, using AI, or can be performed without using AI. For example, the formulation unit can automate the marketing strategy formulation process using an AI model.

[0078] The collection unit can identify the user's emotion and adjust the timing of collecting cookie IDs based on the identified user emotion. The collection unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the collection unit can analyze the user's facial expression captured by a camera to estimate the emotion. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The collection unit can also collect biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotion. For example, the collection unit can estimate the emotion based on heart rate fluctuations. This allows the collection of cookie IDs at the optimal timing according to the user's emotion. Emotion estimation is achieved 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-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can adjust collection timing using an AI model for estimating a user's emotions.

[0079] When collecting cookie IDs from each service, the collection unit can select an appropriate collection method based on the user's past usage history. The collection unit can, for example, analyze the user's past usage history and select the most efficient collection method. For example, the collection unit prioritizes collecting cookie IDs from services the user frequently uses. The collection unit can also collect cookie IDs from services used during a specific time period based on the user's past usage history. Furthermore, the collection unit can collect the most relevant cookie IDs based on the user's past usage history. For example, the collection unit collects cookie IDs for a specific time period from services used by the user during that time period. This enables efficient collection of cookie IDs taking the user's past usage history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can select the optimal collection method using an AI model for analyzing the user's past usage history.

[0080] When collecting cookie IDs, the collection unit can perform filtering based on the user's current usage and areas of interest. The collection unit can, for example, analyze the user's current usage and collect the most relevant cookie IDs. For example, the collection unit can prioritize collecting cookie IDs from services currently used by the user. The collection unit can also collect cookie IDs from services related to the user's areas of interest. Furthermore, the collection unit can collect the most relevant cookie IDs based on the user's current usage. For example, the collection unit can analyze the user's current usage and collect the most relevant cookie IDs. This enables effective collection of cookie IDs based on the user's current usage and areas of interest. Some or all of the above-described 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 perform filtering using an AI model for analyzing the user's current usage and areas of interest.

[0081] When collecting cookie IDs, the collection unit can select an appropriate collection method depending on the user's input method. For example, if the user uses voice input, the collection unit can collect cookie IDs from voice data. For example, the collection unit can collect cookie IDs from voice data using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect cookie IDs from text data. For example, the collection unit can collect cookie IDs from text data using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect cookie IDs from image data. For example, the collection unit can collect cookie IDs from image data using image recognition technology. This enables optimal collection of cookie IDs depending on the user's input method. 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 automate the collection process using an AI model for selecting the optimal collection method depending on the user's input method.

[0082] The collection unit can identify the user's emotion and determine the priority of cookie IDs to be collected based on the identified user's emotion. The collection unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the collection unit can analyze the user's facial expression captured by a camera to estimate the emotion. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The collection unit can also collect biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotion. For example, the collection unit can estimate the emotion based on heart rate fluctuations. This allows cookie IDs to be collected in an optimal priority order according to the user's emotion. Emotion estimation is achieved 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-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can use an AI model for estimating user emotions to determine the priority of cookie IDs to collect.

[0083] When collecting cookie IDs, the collection unit can prioritize collection of relevant cookie IDs taking into account the user's geographical location information. The collection unit can, for example, collect the most relevant cookie IDs based on the user's geographical location information. For example, the collection unit collects cookie IDs from services related to the user's current location. The collection unit can also analyze the user's past location information and collect highly relevant cookie IDs. Furthermore, the collection unit can collect the most relevant cookie IDs based on the user's current geographical location information. For example, the collection unit collects the most relevant cookie IDs based on the user's current geographical location information. This enables effective collection of cookie IDs based on the user's geographical location information. 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 select the optimal collection method using an AI model for analyzing the user's geographical location information.

[0084] When collecting cookie IDs, the collection unit can analyze the user's social media activities and collect related cookie IDs. For example, the collection unit can analyze the user's social media activities and collect the most relevant cookie IDs. For example, the collection unit can collect cookie IDs related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related cookie IDs. Furthermore, the collection unit can collect related cookie IDs by referring to the activities of the user's friends on social media. For example, the collection unit can collect related cookie IDs by referring to the activities of the user's friends on social media. This enables effective collection of cookie IDs based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select the optimal collection method using an AI model for analyzing the user's social media activities.

[0085] When collecting cookie IDs, the collection unit can customize an appropriate collection method by reflecting the user's past feedback. The collection unit can select the optimal collection method based on the user's past feedback, for example. For example, the collection unit analyzes the user's past feedback and customizes the collection method. The collection unit can also adjust the collection method by reflecting the user's feedback in real time. Furthermore, the collection unit can optimize the collection method based on the user's feedback. For example, the collection unit optimizes the collection method based on the user's feedback. This enables optimal cookie ID collection based on the user's past feedback. 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 customize the collection method using an AI model for analyzing user feedback.

[0086] The storage unit can identify the user's emotion and adjust the data storage method based on the identified user's emotion. The storage unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the storage unit can analyze the user's facial expression captured by a camera and estimate the emotion. The storage unit can also estimate the user's emotion using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice and estimate the emotion. Furthermore, the storage unit can collect biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion. For example, the storage unit can estimate the emotion based on heart rate fluctuations. This enables optimal data storage according to the user's emotion. Emotion estimation is achieved 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 storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can use an AI model to estimate a user's emotions to adjust how the data is stored.

[0087] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit can evaluate the importance of the data and store data of high importance in detail. For example, the storage unit stores data of high importance in detail and stores data of low importance in a simplified form. The storage unit can also adjust the frequency of storage based on the importance of the data. For example, the storage unit stores data of high importance in real time and stores data of low importance in batch processing. Furthermore, the storage unit can adjust the level of detail of storage based on the importance of the data. For example, the storage unit stores data of high importance in detail and stores data of low importance in a simplified form. This enables optimal data storage based on the importance of the data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can adjust the level of detail of storage using an AI model for evaluating the importance of data.

[0088] The storage unit can apply different storage algorithms depending on the data category during storage. For example, the storage unit can apply a storage algorithm dedicated to text to text data. For example, the storage unit applies a storage algorithm dedicated to text to text data, thereby efficiently storing the data. The storage unit can also apply a storage algorithm dedicated to images to image data. For example, the storage unit applies a storage algorithm dedicated to images to image data, thereby efficiently storing the data. The storage unit can also apply a storage algorithm dedicated to audio to audio data. For example, the storage unit applies a storage algorithm dedicated to audio to audio data, thereby efficiently storing the data. This enables optimal data storage depending on the data category. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can automate the storage process using an AI model for applying an optimal storage algorithm depending on the data category.

[0089] During data storage, the storage unit can improve the accuracy of storage by referring to the user's past storage results. The storage unit can, for example, analyze the user's past storage results and optimize the storage algorithm. For example, the storage unit can analyze the user's past storage results and optimize the storage algorithm. The storage unit can also adjust the level of detail of storage based on the user's past storage results. For example, the storage unit adjusts the level of detail of storage based on the user's past storage results. Furthermore, the storage unit can also adjust the frequency of storage by referring to the user's past storage results. For example, the storage unit adjusts the frequency of storage by referring to the user's past storage results. This enables optimal data storage by referring to the user's past storage results. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can improve the accuracy of storage by using an AI model for analyzing the user's past storage results.

[0090] The storage unit can identify the user's emotion and adjust the data encryption method based on the identified user's emotion. The storage unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the storage unit can analyze the user's facial expression captured by a camera to estimate the emotion. The storage unit can also estimate the user's emotion using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the storage unit can collect biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotion. For example, the storage unit can estimate the emotion based on heart rate fluctuations. This enables optimal data encryption according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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-mentioned processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can use AI models to estimate user emotions and adjust how it encrypts data.

[0091] The storage unit can determine the storage priority based on the time of data submission when storing data. The storage unit can, for example, preferentially store the most relevant data based on the time of data submission. For example, the storage unit preferentially stores the most recent data. The storage unit can also store data that was submitted earlier later. Furthermore, the storage unit can adjust the data storage order based on the time of submission. For example, the storage unit adjusts the data storage order based on the time of submission. This enables optimal data storage based on the time of data submission. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can determine the storage priority using an AI model for evaluating the time of data submission.

[0092] The storage unit can adjust the order of storage based on the relevance of the data during storage. The storage unit can, for example, evaluate the relevance of the data and store highly relevant data preferentially. For example, the storage unit stores highly relevant data preferentially and stores less relevant data later. The storage unit can also adjust the order of storage based on the relevance of the data. For example, the storage unit adjusts the order of storage based on the relevance of the data. This enables optimal data storage based on the relevance of the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can adjust the order of storage using an AI model for evaluating the relevance of the data.

[0093] The storage unit can adjust the data anonymization method according to the user's level of expertise during storage. For example, the storage unit can evaluate the user's level of expertise and apply a detailed anonymization method to a user with high expertise. For example, the storage unit can apply a detailed anonymization method to a user with high expertise and a simplified anonymization method to a user with low expertise. The storage unit can also adjust the anonymization method based on the user's level of expertise. For example, the storage unit adjusts the anonymization method based on the user's level of expertise. This enables optimal data anonymization according to the user's level of expertise. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can adjust the anonymization method using an AI model for evaluating the user's level of expertise.

[0094] The analysis unit can identify the user's emotion and adjust the analysis criteria based on the identified user's emotion. The analysis unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the analysis unit can analyze the user's facial expression captured by a camera to estimate the emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the analysis unit can collect biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotion. For example, the analysis unit can estimate the emotion based on heart rate fluctuations. This allows the application of optimal analysis criteria according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can use AI models to estimate user emotions and adjust the analysis criteria.

[0095] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit can, for example, analyze the interrelationships between data and prioritize the analysis of highly related data. For example, the analysis unit analyzes the interrelationships between data and prioritizes the analysis of highly related data. The analysis unit can also optimize the analysis algorithm by taking into account the interrelationships between data. For example, the analysis unit optimizes the analysis algorithm by taking into account the interrelationships between data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the interrelationships between data. For example, the analysis unit adjusts the level of detail of the analysis based on the interrelationships between data. This enables highly accurate analysis that takes into account the interrelationships between 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 improve the accuracy of the analysis by using an AI model for analyzing the interrelationships between data.

[0096] The analysis unit can perform the analysis taking into account attribute information of the data submitter during the analysis. The analysis unit can perform the analysis taking into account, for example, the age and gender of the data submitter. For example, the analysis unit can perform the analysis taking into account the age and gender of the data submitter. The analysis unit can also perform the analysis taking into account the occupation and hobbies of the data submitter. For example, the analysis unit can perform the analysis taking into account the occupation and hobbies of the data submitter. Furthermore, the analysis unit can perform the analysis taking into account regional information of the data submitter. For example, the analysis unit can perform the analysis taking into account regional information of the data submitter. This enables optimal analysis taking into account the attribute information of the data submitter. Some or all of the above-described 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 perform the analysis using an AI model for analyzing attribute information of the data submitter.

[0097] During analysis, the analysis unit can weight the analysis based on the frequency of data submission. The analysis unit can, for example, prioritize the analysis of the most relevant data based on the frequency of data submission. For example, the analysis unit prioritizes analysis of data with a high submission frequency. The analysis unit can also postpone analysis of data with a low submission frequency. Furthermore, the analysis unit can adjust the weighting of the data based on the submission frequency. For example, the analysis unit adjusts the weighting of the data based on the submission frequency. This enables optimal analysis based on the frequency of data submission. Some or all of the above-described 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 weight the analysis using an AI model for evaluating the frequency of data submission.

[0098] The analysis unit can identify the user's emotion and adjust the display method of the analysis results based on the identified user's emotion. The analysis unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the analysis unit can analyze the user's facial expression captured by a camera to estimate the emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the analysis unit can collect biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotion. For example, the analysis unit can estimate the emotion based on heart rate fluctuations. This enables the display of optimal analysis results according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can use AI models to infer user emotions and adjust how the analysis results are displayed.

[0099] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit can perform an analysis for each region based on the geographical distribution of the data. For example, the analysis unit performs an analysis for each region based on the geographical distribution of the data. The analysis unit can also optimize the analysis algorithm taking into account the geographical distribution. For example, the analysis unit optimizes the analysis algorithm taking into account the geographical distribution. Furthermore, the analysis unit can display the analysis results for each region based on the geographical distribution. For example, the analysis unit displays the analysis results for each region based on the geographical distribution. This enables optimal analysis taking into account the geographical distribution of the data. Some or all of the above-described 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 perform the analysis using an AI model for evaluating the geographical distribution of the data.

[0100] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. The analysis unit can, for example, optimize the analysis algorithm by referring to literature related to the data. For example, the analysis unit optimizes the analysis algorithm by referring to the literature. The analysis unit can also interpret the data based on the literature. For example, the analysis unit interprets the data based on the literature. Furthermore, the analysis unit can also improve the reliability of the analysis results by referring to the literature. For example, the analysis unit improves the reliability of the analysis results by referring to the literature. This enables highly accurate analysis by referring to literature related to the data. Some or all of the above-described 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 improve the accuracy of the analysis by using an AI model for referring to the literature.

[0101] The analysis unit can perform the analysis taking into account the market value of the data. For example, the analysis unit can prioritize analysis of data with high importance based on the market value of the data. For example, the analysis unit prioritizes analysis of data with high importance based on the market value of the data. The analysis unit can also optimize the analysis algorithm taking into account the market value. For example, the analysis unit optimizes the analysis algorithm taking into account the market value. Furthermore, the analysis unit can also display the analysis results based on the market value. For example, the analysis unit displays the analysis results based on the market value. This enables optimal analysis taking into account the market value of the data. Some or all of the above-described 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 perform the analysis using an AI model for evaluating the market value of the data.

[0102] The formulation unit can identify the user's emotions and adjust the marketing strategy formulation method based on the identified user emotions. The formulation unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the formulation unit can analyze the user's facial expressions captured with a camera to estimate the emotions. The formulation unit can also estimate the user's emotions using voice analysis technology. For example, the formulation unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the formulation unit can collect biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotions. For example, the formulation unit can estimate the emotions based on heart rate fluctuations. This enables the formulation of an optimal marketing strategy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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-mentioned processing in the formulation unit can be performed using, for example, AI, or without AI. For example, the development team can use AI models to estimate user sentiment to adjust how they develop marketing strategies.

[0103] The formulation unit can appropriately optimize the current strategy by referring to past marketing strategy data during formulation. The formulation unit can, for example, analyze past marketing strategy data and formulate an optimal strategy. For example, the formulation unit analyzes past marketing strategy data and formulates an optimal strategy. The formulation unit can also optimize the current strategy based on past success cases. For example, the formulation unit optimizes the current strategy based on past success cases. Furthermore, the formulation unit can also improve the current strategy by referring to past failure cases. For example, the formulation unit improves the current strategy by referring to past failure cases. This makes it possible to formulate an optimal strategy by referring to past marketing strategy data. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can optimize the current strategy by using an AI model for analyzing past marketing strategy data.

[0104] The formulation unit can update the marketing strategy by reflecting user feedback during formulation. The formulation unit can, for example, adjust the marketing strategy based on user feedback. For example, the formulation unit adjusts the marketing strategy based on user feedback. The formulation unit can also analyze user feedback and identify areas for improvement in the strategy. For example, the formulation unit analyzes user feedback and identifies areas for improvement in the strategy. Furthermore, the formulation unit can also reflect user feedback in real time and update the strategy. For example, the formulation unit reflects user feedback in real time and updates the strategy. This makes it possible to update an optimal marketing strategy that reflects user feedback. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can update the marketing strategy using an AI model for analyzing user feedback.

[0105] The formulation unit can customize an appropriate strategy based on the user's current market situation during formulation. The formulation unit can, for example, analyze the current market situation and formulate an optimal marketing strategy. For example, the formulation unit analyzes the current market situation and formulates an optimal marketing strategy. The formulation unit can also customize the strategy taking market trends into consideration. For example, the formulation unit customizes the strategy taking market trends into consideration. Furthermore, the formulation unit can also optimize the strategy based on the competitive situation in the market. For example, the formulation unit optimizes the strategy based on the competitive situation in the market. This makes it possible to formulate an optimal strategy based on the user's current market situation. Some or all of the above-mentioned processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can customize the strategy using an AI model for analyzing the current market situation.

[0106] The formulation unit can identify the user's emotions and prioritize marketing strategies based on the identified user emotions. The formulation unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the formulation unit can analyze the user's facial expressions captured with a camera to estimate the emotions. The formulation unit can also estimate the user's emotions using voice analysis technology. For example, the formulation unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the formulation unit can collect biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotions. For example, the formulation unit can estimate the emotions based on heart rate fluctuations. This enables optimal prioritization of marketing strategies 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, 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 formulation unit can be performed using, for example, AI, or without AI. For example, the development team can use AI models to estimate user sentiment to prioritize marketing strategies.

[0107] During formulation, the formulation unit can weight appropriate strategies based on the time of data submission. The formulation unit can, for example, prioritize analysis of the most relevant data based on the time of data submission. For example, the formulation unit weights strategies based on the most recent data. The formulation unit can also lower the weight of data submitted earlier. Furthermore, the formulation unit can determine the priority of strategies based on the time of submission. For example, the formulation unit determines the priority of strategies based on the time of submission. This enables optimal weighting of strategies based on the time of data submission. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can weight strategies using an AI model for evaluating the time of data submission.

[0108] During formulation, the formulation unit can integrate information from different data sources to enhance the strategy. For example, the formulation unit can integrate information from different data sources to formulate a comprehensive strategy. For example, the formulation unit integrates information from different data sources to formulate a comprehensive strategy. The formulation unit can also optimize the strategy by taking into account the characteristics of each data source. For example, the formulation unit optimizes the strategy by taking into account the characteristics of each data source. Furthermore, the formulation unit can adjust the level of detail of the strategy based on information from different data sources. For example, the formulation unit adjusts the level of detail of the strategy based on information from different data sources. This makes it possible to formulate an optimal strategy that integrates information from different data sources. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can enhance the strategy by using an AI model for integrating information from different data sources.

[0109] The formulation unit can customize an appropriate strategy by reflecting the user's past feedback during formulation. The formulation unit can, for example, adjust the strategy based on the user's past feedback. For example, the formulation unit adjusts the strategy based on the user's past feedback. The formulation unit can also analyze the user's past feedback and identify areas for improvement in the strategy. For example, the formulation unit analyzes the user's past feedback and identifies areas for improvement in the strategy. Furthermore, the formulation unit can reflect the user's past feedback in real time and update the strategy. For example, the formulation unit reflects the user's past feedback in real time and updates the strategy. This makes it possible to formulate an optimal strategy that reflects the user's past feedback. Some or all of the above-described processing in the formulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the formulation unit can customize the strategy using an AI model for analyzing the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and formulation 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 can collect the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and estimate the emotion using the specific processing unit 290 of the data processing device 12. The storage unit can, for example, encrypt and store the data collected by the specific processing unit 290 of the data processing device 12. The analysis unit can, for example, analyze the data using machine learning or deep learning using the specific processing unit 290 of the data processing device 12. The formulation unit can, for example, formulate a marketing strategy using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and formulation 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 can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214 and estimate the emotion using the specific processing unit 290 of the data processing device 12. The storage unit can, for example, encrypt and store the data collected by the specific processing unit 290 of the data processing device 12. The analysis unit can, for example, analyze the data using machine learning or deep learning using the specific processing unit 290 of the data processing device 12. The formulation unit can, for example, formulate a marketing strategy using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and formulation 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 can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the headset-type terminal 314 and estimate the emotion using the specific processing unit 290 of the data processing device 12. The storage unit can encrypt and store the data collected by the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the data using machine learning or deep learning using the specific processing unit 290 of the data processing device 12. The formulation unit can formulate a marketing strategy using the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and formulation 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 can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414 and estimate the emotion using the specific processing unit 290 of the data processing device 12. The storage unit can, for example, encrypt and store the data collected by the specific processing unit 290 of the data processing device 12. The analysis unit can, for example, analyze the data using machine learning or deep learning using the specific processing unit 290 of the data processing device 12. The formulation unit can, for example, formulate a marketing strategy using the specific processing unit 290 of the data processing device 12.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The collection unit may adjust the collection method of the cookie ID based on the type of device used by the user. For example, the collection unit may use a mobile-optimized API to collect data from a smartphone and a higher-bandwidth API to collect data from a desktop. The collection unit may also select the optimal collection method based on the type of browser used by the user. For example, the collection unit may use a particular extension to collect data from a Chrome browser and a different extension to collect data from a Safari browser. The collection unit may also adjust the frequency and timing of data collection based on the user's network connection status. For example, the collection unit may prioritize data collection when the user is connected to Wi-Fi and refrain from data collection when the user is connected to mobile data. This allows for optimal data collection based on the user's device and connection status.

[0112] The storage unit can select a data storage location based on the user's geographic location. For example, if the user is in Europe, the storage unit can store the data on a server in Europe, and if the user is in Asia, the storage unit can store the data on a server in Asia. The storage unit can also select a data storage location based on the user's privacy settings. For example, if the user desires high privacy protection, the storage unit can store the data in encrypted cloud storage, and if the user desires low privacy protection, the storage unit can store the data on a local server. The storage unit can also select a data storage location based on the user's data usage purpose. For example, the storage unit can store data intended for data analysis in high-speed accessible storage and data intended for backup in low-cost storage. This enables optimal data storage according to the user's geographic location, privacy settings, and data usage purpose.

[0113] The analysis unit can identify the user's emotions and adjust the notification method of the analysis results based on the identified user emotions. For example, the analysis unit can immediately notify the user of the analysis results if the user is expressing positive emotions, and delay the notification if the user is expressing negative emotions. The analysis unit can also change the notification format according to the user's emotions. For example, the analysis unit can provide detailed analysis results if the user is relaxed, and provide a concise summary if the user is feeling stressed. Furthermore, the analysis unit can adjust the priority of notifications based on the user's emotions. For example, the analysis unit can prioritize sending important notifications if the user is excited, and refrain from sending notifications if the user is tired. This makes it possible to notify the user of the optimal analysis results according to the user's emotions.

[0114] The planning unit can identify the user's emotions and adjust the content of the marketing strategy based on the identified user's emotions. For example, the planning unit can perform an aggressive promotion when the user is expressing positive emotions, and a more subdued promotion when the user is expressing negative emotions. The planning unit can also change the tone of the marketing message according to the user's emotions. For example, if the user is relaxed, the planning unit can send a message in a casual tone, and if the user is stressed, the planning unit can send a message in a formal tone. Furthermore, the planning unit can adjust the timing of the marketing strategy based on the user's emotions. For example, if the user is excited, the planning unit can send a promotion immediately, and if the user is tired, the planning unit can postpone the promotion. This makes it possible to plan an optimal marketing strategy according to the user's emotions.

[0115] The collection unit can identify the user's emotions and adjust the frequency of data collection based on the identified user's emotions. For example, the collection unit can increase the frequency of data collection when the user is expressing positive emotions, and decrease the frequency of data collection when the user is expressing negative emotions. The collection unit can also change the timing of data collection according to the user's emotions. For example, if the user is relaxed, data collection can be performed immediately, and if the user is feeling stressed, data collection can be delayed. Furthermore, the collection unit can adjust the method of data collection based on the user's emotions. For example, if the user is excited, detailed data can be collected, and if the user is tired, simplified data can be collected. This enables optimal data collection according to the user's emotions.

[0116] The collection unit can adjust the frequency of data collection based on the remaining battery level of the user's device. For example, the collection unit can increase the frequency of data collection when the remaining battery level of the user's device is high, and decrease the frequency of data collection when the remaining battery level is low. The collection unit can also prioritize data collection when the user's device is charging, and refrain from data collection when the device is not charging. Furthermore, the collection unit can adjust the method of data collection based on the remaining battery level of the user's device. For example, detailed data can be collected when the remaining battery level is high, and simplified data can be collected when the remaining battery level is low. This enables optimal data collection according to the remaining battery level of the user's device.

[0117] The storage unit can adjust the data storage period based on the user's contract plan. For example, the storage unit can store data for a long period of time for users with a premium plan and for a short period of time for users with a basic plan. The storage unit can also adjust the data storage capacity based on the user's contract plan. For example, it can provide large-capacity storage for users with a premium plan and small-capacity storage for users with a basic plan. Furthermore, the storage unit can adjust the data backup frequency based on the user's contract plan. For example, it can back up data more frequently for users with a premium plan and back up data less frequently for users with a basic plan. This enables optimal data storage based on the user's contract plan.

[0118] The analysis unit can apply different analysis algorithms depending on the type of data. For example, it can apply a natural language processing algorithm to text data and an image recognition algorithm to image data. The analysis unit can also apply a voice analysis algorithm to audio data and a video analysis algorithm to video data. Furthermore, the analysis unit can also apply a time series analysis algorithm to sensor data. For example, it can apply a time series analysis algorithm to sensor data to analyze the fluctuation patterns of the data. This enables optimal analysis depending on the type of data.

[0119] The planning unit can customize a marketing strategy based on a user's purchasing history. For example, the planning unit can analyze trends in products purchased by the user in the past and promote related products. The planning unit can also adjust the timing of promotions based on the user's purchasing frequency. For example, promotions can be run periodically for users who purchase frequently, and promotions can be run at specific event times for users who purchase infrequently. Furthermore, the planning unit can adjust the content of promotions based on the user's purchase amount. For example, special discounts can be offered to users who purchase high-priced items, and point rewards can be offered to users who purchase low-priced items. This makes it possible to formulate an optimal marketing strategy based on the user's purchasing history.

[0120] The planning department can adjust the marketing strategy based on the user's influence on social media. For example, the planning department can conduct influencer marketing for users with a large number of followers and general promotions for users with a small number of followers. The planning department can also adjust the content of promotions based on the user's engagement rate on social media. For example, it can provide special campaigns to users with a high engagement rate and general campaigns to users with a low engagement rate. Furthermore, the planning department can narrow down the target of promotions based on the content of the user's posts on social media. For example, it can promote related products to users who post a lot about a specific genre. This makes it possible to develop an optimal marketing strategy according to the user's influence on social media.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects cookie IDs from each service. The collection unit can collect cookie IDs from various services, such as e-commerce sites and social networking sites. The collection unit can also build a system for centrally managing cookie IDs issued by each service. For example, the collection unit can automatically collect cookie IDs from each service using an API. Step 2: The storage unit stores personal usage data over the long term based on the cookie ID collected by the collection unit. The storage unit can store data including, for example, purchase history on e-commerce sites and content posted on social media. The storage unit can also encrypt and store the collected data. For example, the storage unit encrypts the data using encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The storage unit can also anonymize the collected data. For example, the storage unit anonymizes the data using techniques such as data masking and pseudo-anonymization. Step 3: The analysis unit analyzes the data accumulated by the accumulation unit to understand individual hobbies, preferences, and usage trends. The analysis unit can analyze the data using AI technologies such as machine learning and deep learning. For example, the analysis unit can analyze the purchase frequency of a particular product or the content of posts in a particular genre. Step 4: The formulation unit formulates a marketing strategy based on the analysis results obtained by the analysis unit. The formulation unit can formulate a marketing strategy that includes, for example, a target market and promotion methods. This allows the data management system according to the embodiment to centrally manage personal usage data and enable long-term data analysis. For example, related products can be promoted to users who frequently purchase a particular product. Also, related content can be provided to users who frequently post content in a particular genre.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 cookie IDs from each service; a storage unit that stores personal usage data for a certain period of time based on the cookie ID collected by the collection unit; an analysis unit that analyzes the data accumulated by the accumulation unit and grasps personal tastes, preferences, or usage trends; a formulation unit that formulates a marketing strategy based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Cookie IDs are automatically collected from each service.

2. The system of claim 1.

3. The storage unit is Personal usage data is securely encrypted and stored based on the collected cookie ID.

2. The system of claim 1.

4. The storage unit is Anonymize the collected data in a specific way 2. The system of claim 1.

5. The collecting unit Includes processes to ensure legal compliance regarding data collection from each service 2. The system of claim 1.

6. The analysis unit Using AI to analyze personal tastes, preferences, and usage trends based on accumulated data 2. The system of claim 1.

7. The formulation unit Formulate marketing strategies based on analysis results 2. The system of claim 1.

8. The collecting unit Identify user emotions and adjust the timing of cookie ID collection based on the identified user emotions 2. The system of claim 1.

Images