System

Abstract

An object of the system according to the embodiment is to analyze purchase data of a user and provide the analyzed purchase data to a business operator by integrating the analyzed purchase data with other customer data.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, an integration unit, and a provision unit. The collection unit collects purchase data of a user. The analysis unit analyzes the purchase data collected by the collection unit. The integration unit integrates the data analyzed by the analysis unit with other customer data. The provision unit provides the data integrated by the integration unit to the business operator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have room for improvement in terms of effectively utilizing user purchasing data to enhance services for businesses.

[0005] The system according to the embodiment aims to analyze user purchase data, integrate it with other customer data, and provide it to businesses. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an integration unit, and a provision unit. The collection unit collects user purchase data. The analysis unit analyzes the purchase data collected by the collection unit. The integration unit integrates the data analyzed by the analysis unit with other customer data. The provision unit provides the data integrated by the integration unit to a business operator. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user purchase data, integrate it with other customer data, and provide it to businesses. [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 system according to an embodiment of the present invention utilizes a household accounting app with a plug-in generation AI to digitize a user's purchasing data. In this system, users input their daily purchasing information, and the generation AI analyzes and digitizes the data. For example, when a user takes a photo of a receipt, the generation AI reads the contents and automatically records the purchased items and amounts in the household accounting book. The collected purchasing data is then integrated with other customer data. For example, by integrating with customer data held by other companies, the customer data services previously provided to businesses can be enhanced. This allows businesses to further strengthen their customer data infrastructure. For example, it enables digital marketing initiatives such as one-to-one marketing, which were previously difficult to implement. Specifically, it enables businesses to provide optimal promotions and advertisements for each individual customer. Furthermore, the generation AI automates the digitization of user purchasing data, significantly reducing the user's effort. For example, simply taking a photo of a receipt automatically records the purchased items and amounts in the household accounting book, eliminating the need for manual input. By digitizing the user's purchasing data and integrating it with other customer data, the system enhances customer data services for businesses and enables digital marketing initiatives such as one-to-one marketing.

[0029] A household accounting application system according to an embodiment includes a collection unit, an analysis unit, an integration unit, and a provision unit. The collection unit collects purchase data of a user. The user's purchase data includes, but is not limited to, items purchased, purchase dates and times, and purchase amounts. The collection unit collects the purchase data by, for example, having the user take photos of receipts. The collection unit can also collect purchase data manually entered by the user. The collection unit can also use sensors or devices to automatically collect the user's purchase data. For example, the collection unit collects purchase data by having the user take photos of receipts. The analysis unit analyzes the purchase data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these. For example, the analysis unit reads the contents of receipts and automatically records the items purchased and the amounts in a household accounting book. The analysis unit can also analyze trends and patterns in the purchase data. The analysis unit can also predict the user's purchasing behavior based on the purchase data. For example, the analysis unit reads the contents of a receipt and automatically records the purchased items and amounts in a household ledger. The integration unit integrates the data analyzed by the analysis unit with other customer data. Integration can be performed, for example, by database merging, data cleansing, data matching, or other methods, but is not limited to these examples. For example, the integration unit integrates with customer data held by other companies. The integration unit can also integrate data of different formats by converting them into a unified format. Furthermore, the integration unit can eliminate data duplication and create a consistent data set. For example, the integration unit integrates with customer data held by other companies. The provision unit provides the data integrated by the integration unit to a business operator. The provision can be performed, for example, via an API, in report format, or in real time, but is not limited to these examples. For example, the provision unit provides optimal promotions and advertisements to individual customers based on the integrated data. The provision unit can also analyze customer purchasing behavior and develop marketing strategies based on the integrated data.Furthermore, the providing unit can understand customer needs and preferences based on the integrated data and use this information to improve products and services. For example, the providing unit can provide optimal promotions and advertisements to individual customers based on the integrated data. This enables the household accounting application system according to the embodiment to efficiently collect, analyze, integrate, and provide user purchase data.

[0030] The collection unit can collect purchase data by having a user take a photo of a receipt. The photo of the receipt may include, but is not limited to, the resolution, format, and range of required information. The collection unit can collect purchase data by, for example, having a user take a photo of the receipt. The collection unit can also have a user take a photo of the receipt using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The collection unit can also collect purchase data by having a user take a photo of the receipt and upload the image data to the cloud. For example, the image data can be analyzed on the cloud to extract purchase data. This allows purchase data to be collected simply by a user taking a photo of the receipt. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input a photo of the receipt taken by the user into the generation AI and have the generation AI generate text data from the image data.

[0031] The analysis unit can read the contents of a receipt and automatically record the purchased items and amounts in a household ledger. The household ledger includes, for example, income and expenditure items, recording methods, and display formats, but is not limited to these examples. For example, the analysis unit reads the contents of a receipt and automatically records the purchased items and amounts in the household ledger. The analysis unit can also analyze the contents of a receipt and categorize the purchased items and amounts and record them in the household ledger. For example, it can categorize the purchased items and amounts into categories such as food, clothing, and daily necessities. The analysis unit can also analyze the contents of a receipt and display the purchased items and amounts as graphs or charts. For example, it can display monthly expenditure trends in a graph. This automatically records the contents of a receipt in the household ledger, eliminating the need for manual input. Some or all of the above-described processing by the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the contents of a receipt into the generation AI and have the generation AI analyze the purchased items and amounts.

[0032] The integration unit can integrate customer data held by other companies. Examples of customer data held by other companies include, but are not limited to, customer attribute information, purchase history, and behavioral data. For example, the integration unit integrates customer data held by other companies. The integration unit can also convert data of different formats into a unified format for integration. For example, it can convert CSV format data into JSON format. The integration unit can also eliminate data duplication and create a consistent data set. For example, it can integrate duplicate data related to the same customer. By integrating this data with customer data held by other companies, a more accurate customer profile can be created. Some or all of the above-described processing in the integration unit can be performed using, or without, a generation AI. For example, the integration unit can input customer data held by other companies into the generation AI and have the generation AI integrate the data.

[0033] The provision unit can provide optimal promotions and advertisements to individual customers based on the integrated data. Optimal promotions and advertisements include, but are not limited to, targeting criteria, advertisement formats, and promotion content. For example, the provision unit can provide optimal promotions and advertisements to individual customers based on the integrated data. The provision unit can also analyze a customer's purchasing history and behavioral patterns to provide optimal promotions and advertisements. For example, the provision unit can provide promotions related to previously purchased products. The provision unit can also provide personalized advertisements based on customer attribute information and preferences. For example, the provision unit can provide advertisements tailored to specific age groups or genders. This enables one-to-one marketing by providing optimal promotions and advertisements to individual customers. Some or all of the above-described processing in the provision unit can be performed using, or without, a generation AI. For example, the provision unit can input the integrated data into a generation AI and have the generation AI generate optimal promotions and advertisements.

[0034] The providing unit automates the digitization of a user's purchase data, thereby reducing the user's workload. Digitization includes, but is not limited to, data format, conversion method, and storage method. For example, the providing unit automates the digitization of a user's purchase data, thereby reducing the user's workload. The providing unit can also digitize purchase data simply by the user taking a photo of a receipt. For example, the providing unit can automatically read the contents of the receipt and convert it into digital data. The providing unit can also automatically digitize purchase data manually entered by the user. For example, the providing unit can automatically organize manually entered data and convert it into a digital format. This automates the digitization of the user's purchase data, thereby significantly reducing the user's workload. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input a photo of a receipt taken by the user into the generation AI and have the generation AI convert it into digital data.

[0035] The collection unit can analyze the user's past purchase history and select the optimal collection method. For example, the collection unit prioritizes collecting receipts from stores frequently visited by the user. The collection unit can also collect related purchase data based on product categories purchased by the user in the past. Furthermore, the collection unit can analyze the user's purchasing patterns and select the optimal collection timing. For example, the collection unit analyzes the user's past purchase history and selects the optimal collection method. In this way, the optimal collection method can be selected by analyzing the user's past purchase history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past purchase history data into the generation AI and have the generation AI select the optimal collection method.

[0036] When collecting purchasing data, the collection unit can filter the purchasing data based on the user's current living situation and areas of interest. For example, if the user is health-conscious, the collection unit can prioritize collecting purchasing data for health foods. Furthermore, if the user is raising a child, the collection unit can prioritize collecting purchasing data for childcare products. Furthermore, if the user is traveling, the collection unit can prioritize collecting purchasing data related to travel. For example, the collection unit filters the purchasing data based on the user's current living situation and areas of interest. This allows more relevant data to be collected by filtering the data based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0037] When collecting purchase data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit collects the purchase data by reading out the receipt contents aloud. If the user prefers text input, the collection unit can also collect the purchase data by inputting the receipt contents in text. If the user prefers image input, the collection unit can also collect the purchase data by taking a photo of the receipt. For example, the collection unit selects the optimal collection means depending on the user's input method. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0038] When collecting purchase data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting purchase data from stores in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting purchase data from travel destinations. Furthermore, when the user is at home, the collection unit can prioritize collecting purchase data from nearby stores. For example, when collecting purchase data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting purchase data. For example, the collection unit collects purchase data for stores 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 purchase data. Furthermore, the collection unit can collect related purchase data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and collects related data when collecting purchase data. In this way, related data can be efficiently collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting purchase data. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also avoid collection methods that the user has been dissatisfied with in the past and adopt methods that provide high satisfaction. Furthermore, the collection unit can analyze the user's past feedback and suggest the optimal collection method. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting purchase data. This allows the collection method to be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the purchasing data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with medium importance. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the purchasing data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the purchasing data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the importance of the purchasing data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of purchasing data. For example, the analysis unit can apply a nutritional value analysis algorithm to data in the food category. The analysis unit can also apply a fashion trend analysis algorithm to data in the clothing category. The analysis unit can also apply a technical specification analysis algorithm to data in the electronic device category. For example, the analysis unit applies different analysis algorithms depending on the category of purchasing data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the category of purchasing data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data of a category of purchasing data into the generation AI and have the generation AI apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also correct errors and improve accuracy based on the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis method. For example, during analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the purchase data. For example, the analysis unit prioritizes analysis of the most recent purchase data. The analysis unit can also determine the analysis priority based on the time of submission by referring to past purchase data. Furthermore, the analysis unit can prioritize analysis of data that was submitted in a concentrated period of time. For example, the analysis unit determines the analysis priority based on the time of submission of the purchase data during analysis. This enables efficient analysis by determining the analysis priority based on the time of submission of the purchase data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the purchase data to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the purchasing data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can analyze data with moderate relevance in an appropriate order. For example, the analysis unit adjusts the order of analysis based on the relevance of the purchasing data during analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the purchasing data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the relevance of the purchasing data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results explained in simple language. Furthermore, the analysis unit can provide analysis results that use appropriate technical terminology according to the user's level of expertise. For example, during analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0047] The integration unit can improve the accuracy of the integration by taking into account the interrelationships of the purchasing data during integration. For example, the integration unit analyzes the interrelationships of the purchasing data and prioritizes integration of highly related data. The integration unit can also improve the accuracy of the integration by taking into account the interrelationships of the purchasing data. Furthermore, the integration unit can select the optimal integration method based on the interrelationships of the purchasing data. For example, the integration unit improves the accuracy of the integration by taking into account the interrelationships of the purchasing data during integration. In this way, the accuracy of the integration is improved by taking into account the interrelationships of the purchasing data. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data on the interrelationships of the purchasing data into the generation AI and have the generation AI improve the accuracy of the integration.

[0048] The integration unit can perform the integration while taking into account attribute information of customer data held by other companies. For example, the integration unit selects an optimal integration method based on the attribute information of customer data held by other companies. The integration unit can also improve the accuracy of the integration by taking into account the attribute information of customer data held by other companies. Furthermore, the integration unit can analyze the attribute information of customer data held by other companies and prioritize the integration of highly related data. For example, the integration unit can perform the integration while taking into account the attribute information of customer data held by other companies. In this way, by taking into account the attribute information of customer data held by other companies, the accuracy of the integration is improved. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input attribute information data of customer data held by other companies into the generation AI and have the generation AI perform the integration.

[0049] The integrating unit can weight the integration based on the frequency of submission of the purchasing data when integrating. For example, the integrating unit prioritizes integration of data with a high submission frequency. The integrating unit can also integrate data with a low submission frequency later. Furthermore, the integrating unit can appropriately weight and integrate data with a medium submission frequency. For example, the integrating unit weights the integration based on the frequency of submission of the purchasing data when integrating. This enables efficient integration by weighting the integration based on the frequency of submission of the purchasing data. Some or all of the above-mentioned processing in the integrating unit may be performed using or without the generation AI. For example, the integrating unit can input data on the frequency of submission of the purchasing data into the generation AI and have the generation AI perform the integration weighting.

[0050] The integration unit can perform integration taking into account the geographical distribution of the purchase data. For example, the integration unit prioritizes integration of geographically close data. The integration unit can also postpone integration of geographically distant data. Furthermore, the integration unit can select the optimal integration method taking into account the geographical distribution. For example, the integration unit performs integration taking into account the geographical distribution of the purchase data. This enables efficient integration by taking into account the geographical distribution of the purchase data. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data on the geographical distribution of the purchase data into the generation AI and have the generation AI perform the integration.

[0051] The integration unit can improve the accuracy of the integration by referring to literature related to the purchasing data during integration. For example, the integration unit can improve the accuracy of the integration by referring to literature related to the purchasing data. The integration unit can also select the optimal integration method based on the literature related to the purchasing data. Furthermore, the integration unit can analyze literature related to the purchasing data and prioritize the integration of highly relevant data. For example, the integration unit can improve the accuracy of the integration by referring to literature related to the purchasing data during integration. In this way, by referring to literature related to the purchasing data, the accuracy of the integration is improved. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data from literature related to the purchasing data into the generation AI and have the generation AI perform the integration.

[0052] The integration unit can perform integration taking into account the market value of the purchasing data. For example, the integration unit prioritizes integration of data with high market value. The integration unit can also integrate data with low market value later. Furthermore, the integration unit can appropriately weight and integrate data with medium market value. For example, the integration unit performs integration taking into account the market value of the purchasing data. This enables efficient integration by taking the market value of the purchasing data into consideration. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data on the market value of the purchasing data into the generation AI and have the generation AI perform the integration.

[0053] The providing unit can improve the accuracy of the provision by taking into account the interrelationships of the purchasing data when providing the data. For example, the providing unit analyzes the interrelationships of the purchasing data and provides highly relevant data preferentially. The providing unit can also improve the accuracy of the provision by taking into account the interrelationships of the purchasing data. Furthermore, the providing unit can select the optimal provision method based on the interrelationships of the purchasing data. For example, the providing unit improves the accuracy of the provision by taking into account the interrelationships of the purchasing data when providing the data. In this way, the accuracy of the provision is improved by taking into account the interrelationships of the purchasing data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input data on the interrelationships of the purchasing data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0054] The providing unit can provide the purchasing data while taking into consideration the attribute information of the person who submitted the data. For example, the providing unit selects the optimal provision method based on the attribute information of the person who submitted the data. The providing unit can also improve the accuracy of the provision by taking into consideration the attribute information of the person who submitted the data. Furthermore, the providing unit can analyze the attribute information of the person who submitted the data and provide highly relevant data preferentially. For example, the providing unit provides the purchasing data while taking into consideration the attribute information of the person who submitted the data. This improves the accuracy of the provision by taking into consideration the attribute information of the person who submitted the purchasing data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the attribute information of the person who submitted the data into the generation AI and have the generation AI execute the provision.

[0055] The providing unit can weight the provision of the purchase data based on the frequency of submission of the purchase data when providing the data. For example, the providing unit can prioritize providing data with a high submission frequency. The providing unit can also provide data with a low submission frequency later. Furthermore, the providing unit can appropriately weight data with a medium submission frequency when providing the data. For example, the providing unit weights the provision of the purchase data based on the frequency of submission of the purchase data when providing the data. This enables efficient provision by weighting the provision of the purchase data based on the frequency of submission of the purchase data. Some or all of the above-mentioned processing in the providing unit may be performed using or without using the generation AI. For example, the providing unit can input data on the frequency of submission of the purchase data into the generation AI and have the generation AI perform the weighting of the provision.

[0056] The providing unit can provide the purchase data taking into consideration the geographical distribution of the purchase data when providing the data. For example, the providing unit can provide geographically close data with priority. The providing unit can also provide geographically distant data later. Furthermore, the providing unit can select the optimal providing method by taking into consideration the geographical distribution. For example, the providing unit provides the purchase data taking into consideration the geographical distribution of the purchase data when providing the data. This enables efficient provision by taking into consideration the geographical distribution of the purchase data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input data on the geographical distribution of the purchase data into the generation AI and have the generation AI execute the provision.

[0057] The providing unit can improve the accuracy of the provision by referring to literature related to the purchasing data when providing the data. For example, the providing unit can improve the accuracy of the provision by referring to literature related to the purchasing data. The providing unit can also select the optimal provision method based on the literature related to the purchasing data. Furthermore, the providing unit can analyze literature related to the purchasing data and provide highly relevant data preferentially. For example, the providing unit can improve the accuracy of the provision by referring to literature related to the purchasing data when providing the data. In this way, by referring to literature related to the purchasing data, the accuracy of the provision is improved. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input data of literature related to the purchasing data into the generation AI and have the generation AI perform the provision.

[0058] The providing unit can provide the purchasing data taking into consideration the market value of the purchasing data when providing it. For example, the providing unit can provide data with a high market value preferentially. The providing unit can also provide data with a low market value later. Furthermore, the providing unit can provide data with an appropriate weighting on data with a medium market value. For example, the providing unit provides the purchasing data taking into consideration the market value of the purchasing data when providing it. This enables efficient provision by taking the market value of the purchasing data into consideration. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input data on the market value of the purchasing data into the generation AI and have the generation AI perform the provision.

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

[0060] When collecting the user's purchasing data, the collection unit can provide personalized reminders based on the user's purchasing history. For example, if the user regularly purchases a particular product, the collection unit can send a reminder when the user needs that product again. The collection unit can also provide the user with information about new products or promotions related to products previously purchased by the user. Furthermore, the collection unit can analyze the user's purchasing history and suggest products related to specific seasons or events. This helps the user remember to purchase the products they need, improving their purchasing experience.

[0061] When analyzing a user's purchasing data, the analysis unit can provide analysis results that take the user's health condition into consideration. For example, if the user is health-conscious, the analysis unit can analyze the nutritional value of purchased foods and suggest healthy ingredients. If the user has a specific allergy, the analysis unit can also warn the user about products that contain allergens. Furthermore, the analysis unit can evaluate the calories and nutritional balance of purchased products based on the user's health goals and suggest areas for improvement. This allows the user to make healthy purchasing choices.

[0062] When integrating data with customer data held by other companies, the Integration Department can evaluate the reliability of the data and prioritize integrating highly reliable data. For example, it evaluates reliability based on the data source and collection method, and excludes data with low reliability. The Integration Department can also check the consistency and accuracy of the data and correct any inconsistencies. Furthermore, the Integration Department can consider the recency of the data and prioritize integrating the most recent data. This improves the quality of the integrated data and enables the creation of more accurate customer profiles.

[0063] The provision unit can predict the user's purchasing behavior based on the integrated data and provide promotions that meet future purchasing needs. For example, the provision unit can analyze patterns of products the user has purchased in the past and predict the products the user is likely to purchase next. The provision unit can also suggest products the user needs based on seasons or events. Furthermore, the provision unit can send reminders based on the user's purchasing history if a specific product is in short supply. This allows the user to purchase the products they need in a timely manner, improving their purchasing experience.

[0064] The providing unit can strengthen data security when automating the digitization of user purchasing data. For example, the data can be encrypted to protect it from unauthorized access. The providing unit can also periodically back up the data to prevent data loss. Furthermore, the providing unit can anonymize the data to protect the user's privacy. This ensures that the user's purchasing data is managed safely and can be used with peace of mind.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects the user's purchase data. The user's purchase data includes the purchased items, purchase date and time, and purchase amount. The collection unit collects the purchase data by having the user take a photo of the receipt. Purchase data can also be manually entered by the user or automatically collected using sensors or devices. Step 2: The analysis unit analyzes the purchasing data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can read the contents of a receipt and automatically record the items purchased and the amount in a household ledger. It can also analyze trends and patterns in the purchasing data to predict user purchasing behavior. Step 3: The integration department integrates the data analyzed by the analysis department with other customer data. Integration is performed by methods such as database merging, data cleansing, and data matching. For example, it integrates with customer data held by other companies, converting data in different formats into a unified format, and integrating it. It also eliminates data duplication and creates a consistent data set. Step 4: The provision unit provides the data integrated by the integration unit to the business operator. This can be done through an API, in report format, or in real time. For example, the integrated data can be used to provide optimal promotions and advertisements to individual customers. It can also analyze customer purchasing behavior and develop marketing strategies. It can also understand customer needs and preferences and use this information to improve products and services.

[0067] (Example 2) A system according to an embodiment of the present invention utilizes a household accounting app with a plug-in generation AI to digitize a user's purchasing data. In this system, users input their daily purchasing information, and the generation AI analyzes and digitizes the data. For example, when a user takes a photo of a receipt, the generation AI reads the contents and automatically records the purchased items and amounts in the household accounting book. The collected purchasing data is then integrated with other customer data. For example, by integrating with customer data held by other companies, the customer data services previously provided to businesses can be enhanced. This allows businesses to further strengthen their customer data infrastructure. For example, it enables digital marketing initiatives such as one-to-one marketing, which were previously difficult to implement. Specifically, it enables businesses to provide optimal promotions and advertisements for each individual customer. Furthermore, the generation AI automates the digitization of user purchasing data, significantly reducing the user's effort. For example, simply taking a photo of a receipt automatically records the purchased items and amounts in the household accounting book, eliminating the need for manual input. By digitizing the user's purchasing data and integrating it with other customer data, the system enhances customer data services for businesses and enables digital marketing initiatives such as one-to-one marketing.

[0068] A household accounting application system according to an embodiment includes a collection unit, an analysis unit, an integration unit, and a provision unit. The collection unit collects purchase data of a user. The user's purchase data includes, but is not limited to, items purchased, purchase dates and times, and purchase amounts. The collection unit collects the purchase data by, for example, having the user take photos of receipts. The collection unit can also collect purchase data manually entered by the user. The collection unit can also use sensors or devices to automatically collect the user's purchase data. For example, the collection unit collects purchase data by having the user take photos of receipts. The analysis unit analyzes the purchase data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these. For example, the analysis unit reads the contents of receipts and automatically records the items purchased and the amounts in a household accounting book. The analysis unit can also analyze trends and patterns in the purchase data. The analysis unit can also predict the user's purchasing behavior based on the purchase data. For example, the analysis unit reads the contents of a receipt and automatically records the purchased items and amounts in a household ledger. The integration unit integrates the data analyzed by the analysis unit with other customer data. Integration can be performed, for example, by database merging, data cleansing, data matching, or other methods, but is not limited to these examples. For example, the integration unit integrates with customer data held by other companies. The integration unit can also integrate data of different formats by converting them into a unified format. Furthermore, the integration unit can eliminate data duplication and create a consistent data set. For example, the integration unit integrates with customer data held by other companies. The provision unit provides the data integrated by the integration unit to a business operator. The provision can be performed, for example, via an API, in report format, or in real time, but is not limited to these examples. For example, the provision unit provides optimal promotions and advertisements to individual customers based on the integrated data. The provision unit can also analyze customer purchasing behavior and develop marketing strategies based on the integrated data.Furthermore, the providing unit can understand customer needs and preferences based on the integrated data and use this information to improve products and services. For example, the providing unit can provide optimal promotions and advertisements to individual customers based on the integrated data. This enables the household accounting application system according to the embodiment to efficiently collect, analyze, integrate, and provide user purchase data.

[0069] The collection unit can collect purchase data by having a user take a photo of a receipt. The photo of the receipt may include, but is not limited to, the resolution, format, and range of required information. The collection unit can collect purchase data by, for example, having a user take a photo of the receipt. The collection unit can also have a user take a photo of the receipt using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The collection unit can also collect purchase data by having a user take a photo of the receipt and upload the image data to the cloud. For example, the image data can be analyzed on the cloud to extract purchase data. This allows purchase data to be collected simply by a user taking a photo of the receipt. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input a photo of the receipt taken by the user into the generation AI and have the generation AI generate text data from the image data.

[0070] The analysis unit can read the contents of a receipt and automatically record the purchased items and amounts in a household ledger. The household ledger includes, for example, income and expenditure items, recording methods, and display formats, but is not limited to these examples. For example, the analysis unit reads the contents of a receipt and automatically records the purchased items and amounts in the household ledger. The analysis unit can also analyze the contents of a receipt and categorize the purchased items and amounts and record them in the household ledger. For example, it can categorize the purchased items and amounts into categories such as food, clothing, and daily necessities. The analysis unit can also analyze the contents of a receipt and display the purchased items and amounts as graphs or charts. For example, it can display monthly expenditure trends in a graph. This automatically records the contents of a receipt in the household ledger, eliminating the need for manual input. Some or all of the above-described processing by the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the contents of a receipt into the generation AI and have the generation AI analyze the purchased items and amounts.

[0071] The integration unit can integrate customer data held by other companies. Examples of customer data held by other companies include, but are not limited to, customer attribute information, purchase history, and behavioral data. For example, the integration unit integrates customer data held by other companies. The integration unit can also convert data of different formats into a unified format for integration. For example, it can convert CSV format data into JSON format. The integration unit can also eliminate data duplication and create a consistent data set. For example, it can integrate duplicate data related to the same customer. By integrating this data with customer data held by other companies, a more accurate customer profile can be created. Some or all of the above-described processing in the integration unit can be performed using, or without, a generation AI. For example, the integration unit can input customer data held by other companies into the generation AI and have the generation AI integrate the data.

[0072] The provision unit can provide optimal promotions and advertisements to individual customers based on the integrated data. Optimal promotions and advertisements include, but are not limited to, targeting criteria, advertisement formats, and promotion content. For example, the provision unit can provide optimal promotions and advertisements to individual customers based on the integrated data. The provision unit can also analyze a customer's purchasing history and behavioral patterns to provide optimal promotions and advertisements. For example, the provision unit can provide promotions related to previously purchased products. The provision unit can also provide personalized advertisements based on customer attribute information and preferences. For example, the provision unit can provide advertisements tailored to specific age groups or genders. This enables one-to-one marketing by providing optimal promotions and advertisements to individual customers. Some or all of the above-described processing in the provision unit can be performed using, or without, a generation AI. For example, the provision unit can input the integrated data into a generation AI and have the generation AI generate optimal promotions and advertisements.

[0073] The providing unit automates the digitization of a user's purchase data, thereby reducing the user's workload. Digitization includes, but is not limited to, data format, conversion method, and storage method. For example, the providing unit automates the digitization of a user's purchase data, thereby reducing the user's workload. The providing unit can also digitize purchase data simply by the user taking a photo of a receipt. For example, the providing unit can automatically read the contents of the receipt and convert it into digital data. The providing unit can also automatically digitize purchase data manually entered by the user. For example, the providing unit can automatically organize manually entered data and convert it into a digital format. This automates the digitization of the user's purchase data, thereby significantly reducing the user's workload. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input a photo of a receipt taken by the user into the generation AI and have the generation AI convert it into digital data.

[0074] The collection unit can estimate the user's emotions and adjust the timing of collecting purchase data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit refrains from collecting purchase data and collects it when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can actively collect purchase data to obtain detailed data. Furthermore, if the user is in a hurry, the collection unit can quickly collect purchase data using a simplified method. For example, the collection unit estimates the user's emotions and adjusts the timing of collecting purchase data based on the estimated user emotions. This allows data to be collected at a more appropriate time by adjusting the timing of collecting purchase data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using the generation AI, or without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] The collection unit can analyze the user's past purchase history and select the optimal collection method. For example, the collection unit prioritizes collecting receipts from stores frequently visited by the user. The collection unit can also collect related purchase data based on product categories purchased by the user in the past. Furthermore, the collection unit can analyze the user's purchasing patterns and select the optimal collection timing. For example, the collection unit analyzes the user's past purchase history and selects the optimal collection method. In this way, the optimal collection method can be selected by analyzing the user's past purchase history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past purchase history data into the generation AI and have the generation AI select the optimal collection method.

[0076] When collecting purchasing data, the collection unit can filter the purchasing data based on the user's current living situation and areas of interest. For example, if the user is health-conscious, the collection unit can prioritize collecting purchasing data for health foods. Furthermore, if the user is raising a child, the collection unit can prioritize collecting purchasing data for childcare products. Furthermore, if the user is traveling, the collection unit can prioritize collecting purchasing data related to travel. For example, the collection unit filters the purchasing data based on the user's current living situation and areas of interest. This allows more relevant data to be collected by filtering the data based on the user's living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0077] When collecting purchase data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit collects the purchase data by reading out the receipt contents aloud. If the user prefers text input, the collection unit can also collect the purchase data by inputting the receipt contents in text. If the user prefers image input, the collection unit can also collect the purchase data by taking a photo of the receipt. For example, the collection unit selects the optimal collection means depending on the user's input method. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's input method into the generation AI and have the generation AI select the optimal collection means.

[0078] The collection unit can estimate the user's emotions and prioritize the purchase data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit refrains from collecting less important data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed purchase data. Furthermore, if the user is in a hurry, the collection unit can quickly collect more important data. For example, the collection unit can estimate the user's emotions and prioritize the purchase data to be collected based on the estimated user emotions. Thus, by prioritizing data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using the generation AI, or can be performed without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] When collecting purchase data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting purchase data from stores in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting purchase data from travel destinations. Furthermore, when the user is at home, the collection unit can prioritize collecting purchase data from nearby stores. For example, when collecting purchase data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit can analyze the user's social media activities and collect related data when collecting purchase data. For example, the collection unit collects purchase data for stores 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 purchase data. Furthermore, the collection unit can collect related purchase data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and collects related data when collecting purchase data. In this way, related data can be efficiently collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0081] The collection unit can customize the collection method by reflecting the user's past feedback when collecting purchase data. For example, the collection unit improves the collection method based on feedback provided by the user in the past. The collection unit can also avoid collection methods that the user has been dissatisfied with in the past and adopt methods that provide high satisfaction. Furthermore, the collection unit can analyze the user's past feedback and suggest the optimal collection method. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting purchase data. This allows the collection method to be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the purchasing data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data with medium importance. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the purchasing data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the purchasing data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the importance of the purchasing data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of purchasing data. For example, the analysis unit can apply a nutritional value analysis algorithm to data in the food category. The analysis unit can also apply a fashion trend analysis algorithm to data in the clothing category. The analysis unit can also apply a technical specification analysis algorithm to data in the electronic device category. For example, the analysis unit applies different analysis algorithms depending on the category of purchasing data. This improves the accuracy of the analysis by applying different analysis algorithms depending on the category of purchasing data. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data of a category of purchasing data into the generation AI and have the generation AI apply different analysis algorithms.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also correct errors and improve accuracy based on the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis method. For example, during analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data of the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the purchase data. For example, the analysis unit prioritizes analysis of the most recent purchase data. The analysis unit can also determine the analysis priority based on the time of submission by referring to past purchase data. Furthermore, the analysis unit can prioritize analysis of data that was submitted in a concentrated period of time. For example, the analysis unit determines the analysis priority based on the time of submission of the purchase data during analysis. This enables efficient analysis by determining the analysis priority based on the time of submission of the purchase data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the purchase data to the generation AI and have the generation AI determine the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the purchasing data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can analyze data with moderate relevance in an appropriate order. For example, the analysis unit adjusts the order of analysis based on the relevance of the purchasing data during analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of the purchasing data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the relevance of the purchasing data to the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results explained in simple language. Furthermore, the analysis unit can provide analysis results that use appropriate technical terminology according to the user's level of expertise. For example, during analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0090] The integration unit can estimate the user's emotions and adjust the integration criteria based on the estimated user emotions. For example, the integration unit can apply detailed integration criteria when the user is relaxed. The integration unit can also apply simplified integration criteria when the user is in a hurry. Furthermore, the integration unit can apply integration criteria with visually stimulating effects when the user is excited. For example, the integration unit can estimate the user's emotions and adjust the integration criteria based on the estimated user emotions. This allows for more appropriate integration results to be provided by adjusting the integration criteria according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the integration unit can be performed using the generation AI, or can be performed without the generation AI. For example, the integration unit can input the user's emotion data into the generation AI and have the generation AI adjust the integration criteria.

[0091] The integration unit can improve the accuracy of the integration by taking into account the interrelationships of the purchasing data during integration. For example, the integration unit analyzes the interrelationships of the purchasing data and prioritizes integration of highly related data. The integration unit can also improve the accuracy of the integration by taking into account the interrelationships of the purchasing data. Furthermore, the integration unit can select the optimal integration method based on the interrelationships of the purchasing data. For example, the integration unit improves the accuracy of the integration by taking into account the interrelationships of the purchasing data during integration. In this way, the accuracy of the integration is improved by taking into account the interrelationships of the purchasing data. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data on the interrelationships of the purchasing data into the generation AI and have the generation AI improve the accuracy of the integration.

[0092] The integration unit can perform the integration while taking into account attribute information of customer data held by other companies. For example, the integration unit selects an optimal integration method based on the attribute information of customer data held by other companies. The integration unit can also improve the accuracy of the integration by taking into account the attribute information of customer data held by other companies. Furthermore, the integration unit can analyze the attribute information of customer data held by other companies and prioritize the integration of highly related data. For example, the integration unit can perform the integration while taking into account the attribute information of customer data held by other companies. In this way, by taking into account the attribute information of customer data held by other companies, the accuracy of the integration is improved. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input attribute information data of customer data held by other companies into the generation AI and have the generation AI perform the integration.

[0093] The integrating unit can weight the integration based on the frequency of submission of the purchasing data when integrating. For example, the integrating unit prioritizes integration of data with a high submission frequency. The integrating unit can also integrate data with a low submission frequency later. Furthermore, the integrating unit can appropriately weight and integrate data with a medium submission frequency. For example, the integrating unit weights the integration based on the frequency of submission of the purchasing data when integrating. This enables efficient integration by weighting the integration based on the frequency of submission of the purchasing data. Some or all of the above-mentioned processing in the integrating unit may be performed using or without the generation AI. For example, the integrating unit can input data on the frequency of submission of the purchasing data into the generation AI and have the generation AI perform the integration weighting.

[0094] The integration unit can estimate the user's emotions and adjust the order in which the integration results are displayed based on the estimated user emotions. For example, when the user is relaxed, the integration unit can prioritize displaying detailed integration results. Furthermore, when the user is in a hurry, the integration unit can prioritize displaying integration results that emphasize the main points. Furthermore, when the user is excited, the integration unit can prioritize displaying integration results that add visually stimulating effects. For example, the integration unit can estimate the user's emotions and adjust the order in which the integration results are displayed based on the estimated user emotions. This allows for more appropriate display by adjusting the order in which the integration results are displayed according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the integration unit can be performed using the generation AI, or without the generation AI. For example, the integration unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order.

[0095] The integration unit can perform integration taking into account the geographical distribution of the purchase data. For example, the integration unit prioritizes integration of geographically close data. The integration unit can also postpone integration of geographically distant data. Furthermore, the integration unit can select the optimal integration method taking into account the geographical distribution. For example, the integration unit performs integration taking into account the geographical distribution of the purchase data. This enables efficient integration by taking into account the geographical distribution of the purchase data. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data on the geographical distribution of the purchase data into the generation AI and have the generation AI perform the integration.

[0096] The integration unit can improve the accuracy of the integration by referring to literature related to the purchasing data during integration. For example, the integration unit can improve the accuracy of the integration by referring to literature related to the purchasing data. The integration unit can also select the optimal integration method based on the literature related to the purchasing data. Furthermore, the integration unit can analyze literature related to the purchasing data and prioritize the integration of highly relevant data. For example, the integration unit can improve the accuracy of the integration by referring to literature related to the purchasing data during integration. In this way, by referring to literature related to the purchasing data, the accuracy of the integration is improved. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data from literature related to the purchasing data into the generation AI and have the generation AI perform the integration.

[0097] The integration unit can perform integration taking into account the market value of the purchasing data. For example, the integration unit prioritizes integration of data with high market value. The integration unit can also integrate data with low market value later. Furthermore, the integration unit can appropriately weight and integrate data with medium market value. For example, the integration unit performs integration taking into account the market value of the purchasing data. This enables efficient integration by taking the market value of the purchasing data into consideration. Some or all of the above-mentioned processing in the integration unit may be performed using or without the generation AI. For example, the integration unit can input data on the market value of the purchasing data into the generation AI and have the generation AI perform the integration.

[0098] The providing unit can estimate the user's emotions and prioritize the data to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed data. Furthermore, when the user is in a hurry, the providing unit can prioritize providing data that focuses on the main points. Furthermore, when the user is excited, the providing unit can prioritize providing data with visually stimulating effects. For example, the providing unit can estimate the user's emotions and prioritize the data to be provided based on the estimated user's emotions. This allows important data to be provided preferentially by prioritizing data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using the generation AI, or can be performed without the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0099] The providing unit can improve the accuracy of the provision by taking into account the interrelationships of the purchasing data when providing the data. For example, the providing unit analyzes the interrelationships of the purchasing data and provides highly relevant data preferentially. The providing unit can also improve the accuracy of the provision by taking into account the interrelationships of the purchasing data. Furthermore, the providing unit can select the optimal provision method based on the interrelationships of the purchasing data. For example, the providing unit improves the accuracy of the provision by taking into account the interrelationships of the purchasing data when providing the data. In this way, the accuracy of the provision is improved by taking into account the interrelationships of the purchasing data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input data on the interrelationships of the purchasing data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0100] The providing unit can provide the purchasing data while taking into consideration the attribute information of the person who submitted the data. For example, the providing unit selects the optimal provision method based on the attribute information of the person who submitted the data. The providing unit can also improve the accuracy of the provision by taking into consideration the attribute information of the person who submitted the data. Furthermore, the providing unit can analyze the attribute information of the person who submitted the data and provide highly relevant data preferentially. For example, the providing unit provides the purchasing data while taking into consideration the attribute information of the person who submitted the data. This improves the accuracy of the provision by taking into consideration the attribute information of the person who submitted the purchasing data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the attribute information of the person who submitted the data into the generation AI and have the generation AI execute the provision.

[0101] The providing unit can weight the provision of the purchase data based on the frequency of submission of the purchase data when providing the data. For example, the providing unit can prioritize providing data with a high submission frequency. The providing unit can also provide data with a low submission frequency later. Furthermore, the providing unit can appropriately weight data with a medium submission frequency when providing the data. For example, the providing unit weights the provision of the purchase data based on the frequency of submission of the purchase data when providing the data. This enables efficient provision by weighting the provision of the purchase data based on the frequency of submission of the purchase data. Some or all of the above-mentioned processing in the providing unit may be performed using or without using the generation AI. For example, the providing unit can input data on the frequency of submission of the purchase data into the generation AI and have the generation AI perform the weighting of the provision.

[0102] The providing unit can estimate the user's emotions and adjust the display method of the data to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a detailed display method. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. Furthermore, if the user is excited, the providing unit can provide a display method that adds a visually stimulating effect. For example, the providing unit can estimate the user's emotions and adjust the display method of the data to be provided based on the estimated user's emotions. This enables more appropriate display by adjusting the data display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0103] The providing unit can provide the purchase data taking into consideration the geographical distribution of the purchase data when providing the data. For example, the providing unit can provide geographically close data with priority. The providing unit can also provide geographically distant data later. Furthermore, the providing unit can select the optimal providing method by taking into consideration the geographical distribution. For example, the providing unit provides the purchase data taking into consideration the geographical distribution of the purchase data when providing the data. This enables efficient provision by taking into consideration the geographical distribution of the purchase data. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input data on the geographical distribution of the purchase data into the generation AI and have the generation AI execute the provision.

[0104] The providing unit can improve the accuracy of the provision by referring to literature related to the purchasing data when providing the data. For example, the providing unit can improve the accuracy of the provision by referring to literature related to the purchasing data. The providing unit can also select the optimal provision method based on the literature related to the purchasing data. Furthermore, the providing unit can analyze literature related to the purchasing data and provide highly relevant data preferentially. For example, the providing unit can improve the accuracy of the provision by referring to literature related to the purchasing data when providing the data. In this way, by referring to literature related to the purchasing data, the accuracy of the provision is improved. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input data of literature related to the purchasing data into the generation AI and have the generation AI perform the provision.

[0105] The providing unit can provide the purchasing data taking into consideration the market value of the purchasing data when providing it. For example, the providing unit can provide data with a high market value preferentially. The providing unit can also provide data with a low market value later. Furthermore, the providing unit can provide data with an appropriate weighting on data with a medium market value. For example, the providing unit provides the purchasing data taking into consideration the market value of the purchasing data when providing it. This enables efficient provision by taking the market value of the purchasing data into consideration. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input data on the market value of the purchasing data into the generation AI and have the generation AI perform the provision. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, integration unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user purchase data using the camera 42 or a sensor of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected purchase data. The integration unit, realized, for example, by the specific processing unit 290 of the data processing device 12, integrates the analyzed data with other customer data. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides the integrated data to the business operator. For example, the collection unit, estimated by the control unit 46A of the smart device 14, estimates the user's emotions and adjusts the timing of collecting the purchase data based on the emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, integration unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user purchase data using the camera 42 or a sensor of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected purchase data. The integration unit, realized, for example, by the specific processing unit 290 of the data processing device 12, integrates the analyzed data with other customer data. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides the integrated data to the business operator. For example, the collection unit, estimated by the control unit 46A of the smart glasses 214, estimates the user's emotions and adjusts the timing of collecting purchase data based on the emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, integration unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user purchase data using the camera 42 or a sensor of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected purchase data. The integration unit, realized, for example, by the specific processing unit 290 of the data processing device 12, integrates the analyzed data with other customer data. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides the integrated data to the business operator. For example, the collection unit, estimated by the control unit 46A of the headset-type terminal 314, estimates the user's emotions and adjusts the timing of collecting purchase data based on the emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, integration unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user purchase data using the camera 42 or sensors of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected purchase data. The integration unit, realized, for example, by the specific processing unit 290 of the data processing device 12, integrates the analyzed data with other customer data. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides the integrated data to the business operator. For example, the collection unit, estimated by the control unit 46A of the robot 414, estimates the user's emotions and adjusts the timing of collecting purchase data based on the emotions.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When collecting the user's purchasing data, the collection unit can provide personalized reminders based on the user's purchasing history. For example, if the user regularly purchases a particular product, the collection unit can send a reminder when the user needs that product again. The collection unit can also provide the user with information about new products or promotions related to products previously purchased by the user. Furthermore, the collection unit can analyze the user's purchasing history and suggest products related to specific seasons or events. This helps the user remember to purchase the products they need, improving their purchasing experience.

[0108] When analyzing a user's purchasing data, the analysis unit can provide analysis results that take the user's health condition into consideration. For example, if the user is health-conscious, the analysis unit can analyze the nutritional value of purchased foods and suggest healthy ingredients. If the user has a specific allergy, the analysis unit can also warn the user about products that contain allergens. Furthermore, the analysis unit can evaluate the calories and nutritional balance of purchased products based on the user's health goals and suggest areas for improvement. This allows the user to make healthy purchasing choices.

[0109] When integrating data with customer data held by other companies, the Integration Department can evaluate the reliability of the data and prioritize integrating highly reliable data. For example, it evaluates reliability based on the data source and collection method, and excludes data with low reliability. The Integration Department can also check the consistency and accuracy of the data and correct any inconsistencies. Furthermore, the Integration Department can consider the recency of the data and prioritize integrating the most recent data. This improves the quality of the integrated data and enables the creation of more accurate customer profiles.

[0110] The provision unit can predict the user's purchasing behavior based on the integrated data and provide promotions that meet future purchasing needs. For example, the provision unit can analyze patterns of products the user has purchased in the past and predict the products the user is likely to purchase next. The provision unit can also suggest products the user needs based on seasons or events. Furthermore, the provision unit can send reminders based on the user's purchasing history if a specific product is in short supply. This allows the user to purchase the products they need in a timely manner, improving their purchasing experience.

[0111] The providing unit can strengthen data security when automating the digitization of user purchasing data. For example, the data can be encrypted to protect it from unauthorized access. The providing unit can also periodically back up the data to prevent data loss. Furthermore, the providing unit can anonymize the data to protect the user's privacy. This ensures that the user's purchasing data is managed safely and can be used with peace of mind.

[0112] The collection unit can estimate the user's emotions and customize the purchasing data collection method based on the estimated user emotions. For example, if the user is feeling stressed, a method for collecting data with simple operations is provided. The collection unit can also provide a method for collecting detailed data if the user is relaxed. Furthermore, the collection unit can also collect data in an interactive manner if the user is excited. This makes it possible to provide an optimal collection method according to the user's emotions.

[0113] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. This makes it possible to provide the optimal feedback method according to the user's emotions.

[0114] The integration unit can estimate the user's emotion and adjust the data integration process based on the estimated user's emotion. For example, if the user is relaxed, a detailed integration process is performed. If the user is in a hurry, the integration unit can also perform a simplified integration process. Furthermore, if the user is excited, the integration unit can also perform an integration process with a visually stimulating effect. This makes it possible to provide an optimal integration process according to the user's emotion.

[0115] The providing unit can estimate the user's emotions and adjust the format of the data to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide data in a detailed report format. If the user is in a hurry, the providing unit can also provide data in a summary format that focuses on the main points. Furthermore, if the user is excited, the providing unit can also provide data in a visually stimulating graphic format. In this way, it is possible to provide the optimal data format according to the user's emotions.

[0116] The providing unit can estimate the user's emotions and adjust the timing of providing data based on the estimated user's emotions. For example, if the user is relaxed, detailed data can be provided immediately. If the user is in a hurry, the providing unit can also provide data that focuses on the main points later. Furthermore, if the user is excited, the providing unit can also provide visually stimulating data in real time. This makes it possible to provide the optimal timing of providing data according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects the user's purchase data. The user's purchase data includes the purchased items, purchase date and time, and purchase amount. The collection unit collects the purchase data by having the user take a photo of the receipt. Purchase data can also be manually entered by the user or automatically collected using sensors or devices. Step 2: The analysis unit analyzes the purchasing data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit can read the contents of a receipt and automatically record the items purchased and the amount in a household ledger. It can also analyze trends and patterns in the purchasing data to predict user purchasing behavior. Step 3: The integration department integrates the data analyzed by the analysis department with other customer data. Integration is performed by methods such as database merging, data cleansing, and data matching. For example, it integrates with customer data held by other companies, converting data in different formats into a unified format, and integrating it. It also eliminates data duplication and creates a consistent data set. Step 4: The provision unit provides the data integrated by the integration unit to the business operator. This can be done through an API, in report format, or in real time. For example, the integrated data can be used to provide optimal promotions and advertisements to individual customers. It can also analyze customer purchasing behavior and develop marketing strategies. It can also understand customer needs and preferences and use this information to improve products and services.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 user purchase data; an analysis unit that analyzes the purchase data collected by the collection unit; an integration unit that integrates the data analyzed by the analysis unit with other customer data; a providing unit that provides the data integrated by the integration unit to a business operator. A system characterized by:

2. The collecting unit Collect purchase data by having users take photos of receipts 2. The system of claim 1.

3. The analysis unit Reads receipts and automatically records purchase items and amounts in a household ledger 2. The system of claim 1.

4. The integration unit Integrate customer data held by other companies 2. The system of claim 1.

5. The providing unit Based on integrated data, provide optimal promotions and advertisements to individual customers 2. The system of claim 1.

6. The providing unit Automate the digitization of user purchasing data, reducing user effort 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of purchasing data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past purchase history and select the optimal collection method 2. The system of claim 1.

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