system

Abstract

The system according to this embodiment aims to calculate the optimal timing for purchasing daily necessities, taking into account the frequency of consumption and campaign information, and to facilitate efficient purchasing. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a calculation unit, and a purchase unit. The reception unit receives input from the user regarding the frequency of consumption of daily necessities and brand preferences. The collection unit collects campaign information from shopping sites based on the information entered by the reception unit. The calculation unit calculates the optimal purchase timing considering the campaign information collected by the collection unit and the user's points ecosystem and membership attributes. The purchase unit makes a purchase based on the purchase timing calculated by the calculation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] [[ID=2r3]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to determine the optimal purchase timing of daily necessities, and there are problems in inventory management and cost reduction.

[0005] The system according to the embodiment aims to calculate the optimal purchase timing in consideration of the consumption frequency of daily necessities and campaign information, and purchase efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, a calculation unit, and a purchase unit. The reception unit receives input from the user regarding the frequency of consumption of daily necessities and brand preferences. The collection unit collects campaign information from shopping sites based on the information entered by the reception unit. The calculation unit calculates the optimal purchase timing, taking into account the campaign information collected by the collection unit, as well as the user's points ecosystem and membership attributes. The purchase unit makes a purchase based on the purchase timing calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to this embodiment calculates the optimal purchase timing by considering the frequency of consumption of daily necessities and campaign information, enabling efficient purchasing. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An agent system according to an embodiment of the present invention is a system that uses AI to suggest the optimal timing for purchasing daily necessities. This agent system allows the user to input the frequency of consumption and brand preferences for daily necessities. The AI ​​then collects campaign information from shopping sites and calculates the optimal purchase timing, taking into account the user's points ecosystem and membership attributes. The AI ​​estimates the rate at which consumables are being used based on past purchase history and makes a purchase at the appropriate time using the most suitable campaign. This mechanism allows users to purchase daily necessities at the right time and at a good price without accumulating excess inventory. For example, the user inputs the frequency of consumption and brand preferences for daily necessities. For instance, the user might specify a particular brand for shampoo, but leave laundry detergent and fabric softener unspecified. This information is input into the AI. Next, the AI ​​collects campaign information from shopping sites. The AI ​​visits multiple shopping sites to collect information on sales and campaigns. For example, it collects information on campaigns where a large number of points can be earned on a specific day, or sales where specific products are discounted. The AI ​​calculates the optimal purchase timing, taking into account the collected campaign information and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the AI ​​makes a purchase at the time when the most points can be earned in that ecosystem. Furthermore, the system considers utilizing specific member-only campaigns based on the user's membership attributes. In addition, the AI ​​estimates the rate at which consumables are being used based on past purchase history. For example, it predicts when consumables are likely to run out based on the number of days since the last purchase and usage frequency. This allows users to purchase items at the optimal time using the most relevant campaigns. This ensures users don't end up with excess inventory and can purchase daily necessities at the right time and at a good price. For instance, by purchasing shampoo when it's about to run out using the campaign offering the most points, users can enjoy significant economic benefits. This allows the agent system to ensure users purchase daily necessities at the optimal time.

[0029] The agent system according to this embodiment comprises a reception unit, a collection unit, a calculation unit, and a purchase unit. The reception unit receives input from the user regarding the frequency of consumption and brand specifications of daily necessities. For example, the user can specify a shampoo brand but not laundry detergent or fabric softener. The reception unit transmits the input information to the AI. The collection unit collects campaign information from shopping sites based on the information entered by the reception unit. The collection unit, for example, visits multiple shopping sites to collect information on sales and campaigns. For example, the collection unit collects information on campaigns where a large number of points can be earned on a specific day, or sales where specific products are discounted. The calculation unit calculates the optimal purchase timing by considering the campaign information collected by the collection unit and the user's point ecosystem and membership attributes. For example, if the user has specified a specific point ecosystem, the calculation unit will make a purchase at the time when the most points can be earned in that ecosystem. The calculation unit also considers using specific member-only campaigns depending on the user's membership attributes. The purchase unit makes a purchase based on the purchase timing calculated by the calculation unit. For example, the purchase unit makes a purchase using the optimal campaign. This allows the agent system to enable users to purchase daily necessities at the optimal time.

[0030] The reception desk allows users to input their usage frequency and brand preferences for daily necessities. For example, a user can specify a shampoo brand but not laundry detergent or fabric softener. The reception desk then sends the entered information to the AI. Specifically, users input their usage frequency and brand preferences for daily necessities through a dedicated application or web interface. For example, a user can specify "Company A's shampoo" as the shampoo brand and input "one bottle per month" as the usage frequency. For laundry detergent and fabric softener, it is also possible to input "Leave it to you" without specifying a particular brand. The reception desk sends this input information to the AI ​​in real time to accurately understand the user's needs. Furthermore, the reception desk has a function that learns the user's past purchase history and usage patterns and suggests predictive options for the next input. For example, based on brands and usage frequencies that the user has frequently purchased in the past, it may suggest "Is the same brand as last time okay?" for the next input. This allows users to input information easily without any hassle. The reception desk centrally manages user input information and can collaborate with other departments as needed. For example, the entered information is sent to the data collection and calculation units and used to calculate the optimal purchase timing and collect campaign information. This allows the reception unit to accurately understand user needs and improve the overall efficiency of the system.

[0031] The data collection unit collects campaign information from shopping sites based on the information entered by the reception unit. For example, the data collection unit visits multiple shopping sites to collect information on sales and campaigns. Specifically, the data collection unit automatically visits multiple shopping sites on the internet and collects sales and campaign information from each site. For example, it collects information on campaigns where points are accumulated in large quantities on specific days, or sales where specific products are discounted. The data collection unit uses web scraping technology to analyze the HTML structure of each shopping site and extract the necessary information. Furthermore, the data collection unit can also obtain information directly from shopping sites using APIs. For example, it can use APIs provided by specific shopping sites to obtain the latest sales and campaign information. The data collection unit stores the collected information in a database and updates it as needed. For example, it performs data collection at a fixed time each day, and updates the database when new campaign information is added. This ensures that the data collection unit always has the latest campaign information available for use by the calculation and purchase units. Furthermore, the data collection unit classifies the collected information and provides information tailored to the user's needs. For example, it prioritizes providing campaign information related to brands and products specified by the user. This allows the data collection unit to provide the most useful information to the user and improve the overall efficiency of the system.

[0032] The calculation unit calculates the optimal purchase timing by considering campaign information collected by the collection unit and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the calculation unit will make a purchase at the time when the most points are accumulated in that ecosystem. Specifically, the calculation unit analyzes the campaign information provided by the collection unit and calculates the optimal purchase timing based on the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, it compares the point accrual conditions of each campaign to identify the time when the most points are accumulated in that ecosystem. The calculation unit also considers using specific member-only campaigns depending on the user's membership attributes. For example, if the user is a gold member, it will prioritize using gold member-only campaigns. The calculation unit comprehensively evaluates this information and calculates the most advantageous purchase timing. Furthermore, the calculation unit uses AI to learn the user's purchase history and consumption patterns and predict future purchase timings. For example, if the user has made bulk purchases at a specific time in the past, it will provide campaign information to coincide with that time. In this way, the calculation unit can provide the most beneficial purchase timing for the user and improve the overall efficiency of the system.

[0033] The purchasing unit makes purchases based on the timing calculated by the calculation unit. For example, the purchasing unit makes purchases by taking advantage of optimal campaigns. Specifically, the purchasing unit automatically performs the purchase procedure on shopping sites based on the optimal purchase timing provided by the calculation unit. For example, it purchases daily necessities specified by the user by taking advantage of campaigns that offer more points on specific days. The purchasing unit securely manages the user's account information and payment information and performs the purchase procedure quickly and accurately. Furthermore, the purchasing unit stores the purchase history in a database and uses it as a reference for future purchases. For example, it calculates the timing of the next purchase based on the products and campaign information the user has purchased in the past. The purchasing unit also has a function to track the delivery status after purchase and notify the user. For example, it tracks the delivery status of purchased products in real time and notifies the user via email or app notification. In this way, the purchasing unit can provide the most convenient purchasing experience for the user and improve the efficiency of the overall system. Furthermore, the purchasing unit collects feedback from users and uses it to improve the system. For example, it collects satisfaction and areas for improvement after purchase in the form of surveys and uses this data to improve the next purchasing experience. This allows the purchasing department to provide users with the best possible purchasing experience and improve the overall reliability and satisfaction of the system.

[0034] The data collection unit visits multiple shopping sites to gather information on sales and campaigns. For example, it collects information on campaigns where more points can be earned on specific days, or sales where specific products are discounted. The data collection unit can use AI to automatically collect campaign information from shopping sites. For example, the data collection unit can use AI to collect campaign information from shopping sites in real time. This allows the data collection unit to collect campaign information from multiple shopping sites.

[0035] The calculation unit calculates the optimal purchase timing by considering the collected campaign information and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the calculation unit will make a purchase at the time when the most points are accumulated in that ecosystem. The calculation unit also considers utilizing specific member-only campaigns depending on the user's membership attributes. The calculation unit can use AI to calculate the optimal purchase timing by considering the collected campaign information and the user's points ecosystem and membership attributes. As a result, the calculation unit can calculate the optimal purchase timing by considering the user's points ecosystem and membership attributes.

[0036] The calculation unit predicts when consumables are likely to run out, based on the number of days since the last purchase and the frequency of use. The calculation unit can use AI to predict when consumables are likely to run out, based on the number of days since the last purchase and the frequency of use. This allows the calculation unit to predict when consumables are likely to run out.

[0037] The purchasing department makes purchases using the most suitable campaigns. For example, the purchasing department makes purchases using the most suitable campaigns. The purchasing department can use AI to make purchases using the most suitable campaigns. This allows the purchasing department to make purchases using the most suitable campaigns.

[0038] The reception desk analyzes the user's past input history and provides the optimal input interface. For example, the reception desk automatically displays as suggestions the user has frequently entered in the past regarding consumption frequency and brand preferences. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk predicts and suggests consumption frequency and brand preferences to be used during specific time periods based on the user's past input history. This allows the reception desk to provide the optimal input interface based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0039] The reception desk will add a function to automatically complete input fields based on the user's lifestyle and usage. For example, if the user is busy, the reception desk will automatically complete the usage frequency and brand specification based on past usage. If the user has a specific lifestyle, the reception desk will automatically complete input fields that match that lifestyle. When the user adds new daily necessities, the reception desk will suggest the optimal usage frequency and brand specification based on past usage. This allows the reception desk to automatically complete input fields based on the user's lifestyle and usage. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's lifestyle and usage into a generating AI and have the generating AI perform the automatic completion of input fields.

[0040] The reception desk takes the user's geographical location into consideration and prompts the user to input region-specific daily necessities. For example, if the user lives in a specific region, the reception desk automatically suggests daily necessities commonly used in that region. If the user is traveling, the reception desk suggests necessary daily necessities based on the user's current location. If the user moves, the reception desk suggests necessary daily necessities in the new region. This allows the reception desk to prompt the user to input region-specific daily necessities based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI suggest region-specific daily necessities.

[0041] The reception desk analyzes the user's social media activity and suggests inputs for relevant daily necessities. For example, if the user mentions a specific daily necessity on social media, the reception desk will suggest that daily necessity as an input item. The reception desk will suggest daily necessities related to a specific event based on the user's social media activity. The reception desk will suggest input items by referring to the daily necessities used by the user's social media followers. In this way, the reception desk can suggest inputs for relevant daily necessities based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant daily necessities.

[0042] The data collection unit analyzes past campaign data from the shopping site and optimizes the data collection algorithm. For example, the data collection unit identifies the most effective timing for data collection based on past campaign data. The data collection unit analyzes past campaign data, finds specific patterns, and optimizes the data collection algorithm. The data collection unit uses past campaign data to improve the accuracy of the data collection algorithm. This allows the data collection unit to analyze past campaign data from the shopping site and optimize the data collection algorithm. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past campaign data from the shopping site into a generating AI and have the generating AI perform the optimization of the data collection algorithm.

[0043] The data collection unit will add a function to collect campaign information in real time using the shopping site's API. For example, the data collection unit will collect sales information in real time using the shopping site's API. The data collection unit will collect points campaign information in real time using the shopping site's API. The data collection unit will collect member-only campaign information in real time using the shopping site's API. As a result, the data collection unit will be able to collect campaign information in real time using the shopping site's API. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the shopping site's API into a generating AI and have the generating AI perform real-time campaign information collection.

[0044] The data collection unit collects region-specific campaign information, taking into account the geographical campaign information of the shopping site. For example, if a user lives in a specific region, the data collection unit collects campaign information held in that region. If a user is traveling, the data collection unit collects region-specific campaign information based on their current location. If a user moves, the data collection unit collects campaign information in their new region. This allows the data collection unit to collect region-specific campaign information while taking into account the geographical campaign information of the shopping site. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical campaign information of the shopping site into a generating AI and have the generating AI perform the collection of region-specific campaign information.

[0045] The collection unit collects relevant product information from shopping sites and suggests it to the user. For example, the collection unit collects and suggests product information related to products the user has purchased. The collection unit collects and suggests relevant product information that the user might be interested in. The collection unit collects and suggests relevant product information based on the user's past purchase history. In this way, the collection unit can collect relevant product information from shopping sites and suggest it to the user. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input relevant product information from shopping sites into a generating AI and have the generating AI perform the task of suggesting relevant products.

[0046] The calculation unit analyzes past purchase data and optimizes the calculation algorithm for purchase timing. For example, the calculation unit identifies the most effective purchase timing based on past purchase data. The calculation unit analyzes past purchase data, finds specific patterns, and optimizes the calculation algorithm. The calculation unit uses past purchase data to improve the accuracy of the calculation algorithm. This allows the calculation unit to analyze past purchase data and optimize the calculation algorithm for purchase timing. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past purchase data into a generating AI and have the generating AI perform the optimization of the calculation algorithm.

[0047] The calculation unit adds a function to improve the accuracy of predicting purchase timing based on the user's consumption patterns. For example, the calculation unit analyzes the user's consumption patterns and predicts the optimal purchase timing. The calculation unit improves the accuracy of predicting purchase timing based on the user's consumption patterns. The calculation unit optimizes the purchase timing prediction algorithm by referring to the user's consumption patterns. This allows the calculation unit to improve the accuracy of predicting purchase timing based on the user's consumption patterns. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user consumption pattern data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0048] The calculation unit calculates region-specific purchase timings, taking into account the user's geographical location information. For example, if the user lives in a specific region, the calculation unit calculates the optimal purchase timing for that region. If the user is traveling, the calculation unit calculates the optimal purchase timing based on the user's current location. If the user moves, the calculation unit calculates the optimal purchase timing for the new region. In this way, the calculation unit can calculate region-specific purchase timings based on the user's geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information into a generating AI and have the generating AI perform the calculation of region-specific purchase timings.

[0049] The calculation unit analyzes the user's social media activity and suggests relevant purchase timings. For example, if the user mentions a specific product on social media, the calculation unit suggests the optimal time to purchase that product. The calculation unit suggests purchase timings related to specific events based on the user's social media activity. The calculation unit suggests purchase timings by referring to when the user's social media followers made purchases. In this way, the calculation unit can suggest relevant purchase timings based on the user's social media activity. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant purchase timings.

[0050] The purchasing department analyzes past purchase history and provides the optimal purchase procedure. For example, the purchasing department automatically displays products that the user has frequently purchased in the past as suggestions. The purchasing department prioritizes suggesting purchase methods that the user has used in the past (credit card, e-money, etc.). The purchasing department predicts and suggests purchase procedures to be used at specific times based on the user's past purchase history. In this way, the purchasing department can provide the optimal purchase procedure based on past purchase history. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input past purchase history data into a generating AI and have the generating AI perform the task of providing the optimal purchase procedure.

[0051] The purchasing section will add a function to automatically complete the user's payment method and shipping address information. For example, the purchasing section will automatically complete the payment method the user has used in the past. The purchasing section will automatically complete the shipping address information the user has used in the past. When the user adds new payment method or shipping address information, the purchasing section will automatically complete it by referring to past information. This will enable the purchasing section to automatically complete the user's payment method and shipping address information. Some or all of the above processing in the purchasing section may be performed using AI, for example, or without AI. For example, the purchasing section can input the user's payment method and shipping address information into a generating AI and have the generating AI perform the automatic completion.

[0052] The purchasing unit provides region-specific purchasing procedures, taking into account the user's geographical location. For example, if the user lives in a specific region, the purchasing unit provides the optimal purchasing procedure for that region. If the user is traveling, the purchasing unit provides the optimal purchasing procedure based on the user's current location. If the user moves, the purchasing unit provides the optimal purchasing procedure for the new region. In this way, the purchasing unit can provide region-specific purchasing procedures based on the user's geographical location. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or not using AI. For example, the purchasing unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of region-specific purchasing procedures.

[0053] The purchasing department analyzes the user's social media activity and proposes relevant purchase procedures. For example, if the user mentions a specific product on social media, the purchasing department will propose a purchase procedure for that product. The purchasing department will propose purchase procedures related to a specific event based on the user's social media activity. The purchasing department will propose purchase procedures by referring to purchase procedures used by the user's social media followers. In this way, the purchasing department can propose relevant purchase procedures based on the user's social media activity. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for relevant purchase procedures.

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

[0055] The data collection unit can analyze a user's purchase history and prioritize collecting campaign information they have previously used. For example, if a user has frequently purchased from a particular shopping site in the past, the unit will prioritize collecting campaign information from that site. The data collection unit can also analyze the effectiveness of campaigns the user has previously used and collect the most effective campaign information. Furthermore, the data collection unit can collect campaign information related to specific seasons or events from the user's purchase history. This allows the data collection unit to analyze the user's purchase history and prioritize collecting campaign information they have previously used.

[0056] The calculation unit can be enhanced with a function to improve the accuracy of purchase timing predictions based on user consumption patterns. For example, it can analyze user consumption patterns and predict the optimal purchase timing. It can improve the accuracy of purchase timing predictions based on user consumption patterns. Furthermore, it can optimize the purchase timing prediction algorithm by referring to user consumption patterns. This allows the calculation unit to improve the accuracy of purchase timing predictions based on user consumption patterns.

[0057] The reception desk can take the user's geographical location into consideration and prompt them to input region-specific daily necessities. For example, if a user lives in a specific region, it can automatically suggest daily necessities commonly used in that area. If the user is traveling, it can suggest necessary daily necessities based on their current location. Furthermore, if the user has moved, it can suggest necessary daily necessities for their new area. This allows the reception desk to prompt users to input region-specific daily necessities based on their geographical location.

[0058] The data collection unit can be enhanced with the ability to collect campaign information in real time using shopping site APIs. For example, it can collect sales information in real time using shopping site APIs. It can also collect points campaign information in real time using shopping site APIs. Furthermore, it can collect member-only campaign information in real time using shopping site APIs. This allows the data collection unit to collect campaign information in real time using shopping site APIs.

[0059] The calculation unit can analyze a user's social media activity and suggest relevant purchase timings. For example, if a user mentions a specific product on social media, it can suggest the optimal time to purchase that product. It can also suggest purchase timings related to specific events based on the user's social media activity. Furthermore, it can suggest purchase timings by referring to when the user's social media followers made purchases. In this way, the calculation unit can suggest relevant purchase timings based on the user's social media activity.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives input from the user regarding the frequency of use and brand preferences for daily necessities. For example, the user can specify the brand of shampoo but not laundry detergent or fabric softener. The reception desk then sends the entered information to the AI. Step 2: The collection unit collects campaign information from shopping sites based on the information entered by the reception unit. The collection unit, for example, visits multiple shopping sites to collect information on sales and campaigns. For example, the collection unit collects information on campaigns where more points can be earned on a specific day, or sales where specific products are discounted. Step 3: The calculation unit calculates the optimal purchase timing, taking into account the campaign information collected by the collection unit and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the calculation unit will make a purchase at the time when the most points are accumulated in that ecosystem. The calculation unit also considers utilizing specific member-only campaigns depending on the user's membership attributes. Step 4: The purchasing unit makes purchases based on the purchase timing calculated by the calculation unit. For example, the purchasing unit makes purchases using the most suitable campaigns. This allows the agent system to enable users to purchase daily necessities at the optimal time.

[0062] (Example of form 2) An agent system according to an embodiment of the present invention is a system that uses AI to suggest the optimal timing for purchasing daily necessities. This agent system allows the user to input the frequency of consumption and brand preferences for daily necessities. The AI ​​then collects campaign information from shopping sites and calculates the optimal purchase timing, taking into account the user's points ecosystem and membership attributes. The AI ​​estimates the rate at which consumables are being used based on past purchase history and makes a purchase at the appropriate time using the most suitable campaign. This mechanism allows users to purchase daily necessities at the right time and at a good price without accumulating excess inventory. For example, the user inputs the frequency of consumption and brand preferences for daily necessities. For instance, the user might specify a particular brand for shampoo, but leave laundry detergent and fabric softener unspecified. This information is input into the AI. Next, the AI ​​collects campaign information from shopping sites. The AI ​​visits multiple shopping sites to collect information on sales and campaigns. For example, it collects information on campaigns where a large number of points can be earned on a specific day, or sales where specific products are discounted. The AI ​​calculates the optimal purchase timing, taking into account the collected campaign information and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the AI ​​makes a purchase at the time when the most points can be earned in that ecosystem. Furthermore, the system considers utilizing specific member-only campaigns based on the user's membership attributes. In addition, the AI ​​estimates the rate at which consumables are being used based on past purchase history. For example, it predicts when consumables are likely to run out based on the number of days since the last purchase and usage frequency. This allows users to purchase items at the optimal time using the most relevant campaigns. This ensures users don't end up with excess inventory and can purchase daily necessities at the right time and at a good price. For instance, by purchasing shampoo when it's about to run out using the campaign offering the most points, users can enjoy significant economic benefits. This allows the agent system to ensure users purchase daily necessities at the optimal time.

[0063] The agent system according to this embodiment comprises a reception unit, a collection unit, a calculation unit, and a purchase unit. The reception unit receives input from the user regarding the frequency of consumption and brand specifications of daily necessities. For example, the user can specify a shampoo brand but not laundry detergent or fabric softener. The reception unit transmits the input information to the AI. The collection unit collects campaign information from shopping sites based on the information entered by the reception unit. The collection unit, for example, visits multiple shopping sites to collect information on sales and campaigns. For example, the collection unit collects information on campaigns where a large number of points can be earned on a specific day, or sales where specific products are discounted. The calculation unit calculates the optimal purchase timing by considering the campaign information collected by the collection unit and the user's point ecosystem and membership attributes. For example, if the user has specified a specific point ecosystem, the calculation unit will make a purchase at the time when the most points can be earned in that ecosystem. The calculation unit also considers using specific member-only campaigns depending on the user's membership attributes. The purchase unit makes a purchase based on the purchase timing calculated by the calculation unit. For example, the purchase unit makes a purchase using the optimal campaign. This allows the agent system to enable users to purchase daily necessities at the optimal time.

[0064] The reception desk allows users to input their usage frequency and brand preferences for daily necessities. For example, a user can specify a shampoo brand but not laundry detergent or fabric softener. The reception desk then sends the entered information to the AI. Specifically, users input their usage frequency and brand preferences for daily necessities through a dedicated application or web interface. For example, a user can specify "Company A's shampoo" as the shampoo brand and input "one bottle per month" as the usage frequency. For laundry detergent and fabric softener, it is also possible to input "Leave it to you" without specifying a particular brand. The reception desk sends this input information to the AI ​​in real time to accurately understand the user's needs. Furthermore, the reception desk has a function that learns the user's past purchase history and usage patterns and suggests predictive options for the next input. For example, based on brands and usage frequencies that the user has frequently purchased in the past, it may suggest "Is the same brand as last time okay?" for the next input. This allows users to input information easily without any hassle. The reception desk centrally manages user input information and can collaborate with other departments as needed. For example, the entered information is sent to the data collection and calculation units and used to calculate the optimal purchase timing and collect campaign information. This allows the reception unit to accurately understand user needs and improve the overall efficiency of the system.

[0065] The data collection unit collects campaign information from shopping sites based on the information entered by the reception unit. For example, the data collection unit visits multiple shopping sites to collect information on sales and campaigns. Specifically, the data collection unit automatically visits multiple shopping sites on the internet and collects sales and campaign information from each site. For example, it collects information on campaigns where points are accumulated in large quantities on specific days, or sales where specific products are discounted. The data collection unit uses web scraping technology to analyze the HTML structure of each shopping site and extract the necessary information. Furthermore, the data collection unit can also obtain information directly from shopping sites using APIs. For example, it can use APIs provided by specific shopping sites to obtain the latest sales and campaign information. The data collection unit stores the collected information in a database and updates it as needed. For example, it performs data collection at a fixed time each day, and updates the database when new campaign information is added. This ensures that the data collection unit always has the latest campaign information available for use by the calculation and purchase units. Furthermore, the data collection unit classifies the collected information and provides information tailored to the user's needs. For example, it prioritizes providing campaign information related to brands and products specified by the user. This allows the data collection unit to provide the most useful information to the user and improve the overall efficiency of the system.

[0066] The calculation unit calculates the optimal purchase timing by considering campaign information collected by the collection unit and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the calculation unit will make a purchase at the time when the most points are accumulated in that ecosystem. Specifically, the calculation unit analyzes the campaign information provided by the collection unit and calculates the optimal purchase timing based on the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, it compares the point accrual conditions of each campaign to identify the time when the most points are accumulated in that ecosystem. The calculation unit also considers using specific member-only campaigns depending on the user's membership attributes. For example, if the user is a gold member, it will prioritize using gold member-only campaigns. The calculation unit comprehensively evaluates this information and calculates the most advantageous purchase timing. Furthermore, the calculation unit uses AI to learn the user's purchase history and consumption patterns and predict future purchase timings. For example, if the user has made bulk purchases at a specific time in the past, it will provide campaign information to coincide with that time. In this way, the calculation unit can provide the most beneficial purchase timing for the user and improve the overall efficiency of the system.

[0067] The purchasing unit makes purchases based on the timing calculated by the calculation unit. For example, the purchasing unit makes purchases by taking advantage of optimal campaigns. Specifically, the purchasing unit automatically performs the purchase procedure on shopping sites based on the optimal purchase timing provided by the calculation unit. For example, it purchases daily necessities specified by the user by taking advantage of campaigns that offer more points on specific days. The purchasing unit securely manages the user's account information and payment information and performs the purchase procedure quickly and accurately. Furthermore, the purchasing unit stores the purchase history in a database and uses it as a reference for future purchases. For example, it calculates the timing of the next purchase based on the products and campaign information the user has purchased in the past. The purchasing unit also has a function to track the delivery status after purchase and notify the user. For example, it tracks the delivery status of purchased products in real time and notifies the user via email or app notification. In this way, the purchasing unit can provide the most convenient purchasing experience for the user and improve the efficiency of the overall system. Furthermore, the purchasing unit collects feedback from users and uses it to improve the system. For example, it collects satisfaction and areas for improvement after purchase in the form of surveys and uses this data to improve the next purchasing experience. This allows the purchasing department to provide users with the best possible purchasing experience and improve the overall reliability and satisfaction of the system.

[0068] The data collection unit visits multiple shopping sites to gather information on sales and campaigns. For example, it collects information on campaigns where more points can be earned on specific days, or sales where specific products are discounted. The data collection unit can use AI to automatically collect campaign information from shopping sites. For example, the data collection unit can use AI to collect campaign information from shopping sites in real time. This allows the data collection unit to collect campaign information from multiple shopping sites.

[0069] The calculation unit calculates the optimal purchase timing by considering the collected campaign information and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the calculation unit will make a purchase at the time when the most points are accumulated in that ecosystem. The calculation unit also considers utilizing specific member-only campaigns depending on the user's membership attributes. The calculation unit can use AI to calculate the optimal purchase timing by considering the collected campaign information and the user's points ecosystem and membership attributes. As a result, the calculation unit can calculate the optimal purchase timing by considering the user's points ecosystem and membership attributes.

[0070] The calculation unit predicts when consumables are likely to run out, based on the number of days since the last purchase and the frequency of use. The calculation unit can use AI to predict when consumables are likely to run out, based on the number of days since the last purchase and the frequency of use. This allows the calculation unit to predict when consumables are likely to run out.

[0071] The purchasing department makes purchases using the most suitable campaigns. For example, the purchasing department makes purchases using the most suitable campaigns. The purchasing department can use AI to make purchases using the most suitable campaigns. This allows the purchasing department to make purchases using the most suitable campaigns.

[0072] The reception desk estimates the user's emotions and adjusts the input method for consumption frequency and brand preference based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. If the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of consumption frequency and brand preference. This allows the reception desk to adjust the input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The reception desk analyzes the user's past input history and provides the optimal input interface. For example, the reception desk automatically displays as suggestions the user has frequently entered in the past regarding consumption frequency and brand preferences. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk predicts and suggests consumption frequency and brand preferences to be used during specific time periods based on the user's past input history. This allows the reception desk to provide the optimal input interface based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0074] The reception desk will add a function to automatically complete input fields based on the user's lifestyle and usage. For example, if the user is busy, the reception desk will automatically complete the usage frequency and brand specification based on past usage. If the user has a specific lifestyle, the reception desk will automatically complete input fields that match that lifestyle. When the user adds new daily necessities, the reception desk will suggest the optimal usage frequency and brand specification based on past usage. This allows the reception desk to automatically complete input fields based on the user's lifestyle and usage. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's lifestyle and usage into a generating AI and have the generating AI perform the automatic completion of input fields.

[0075] The reception desk estimates the user's emotions and prioritizes input fields based on the estimated emotions. For example, if the user is tired, the reception desk prioritizes displaying the most important input fields and simplifies the input process. If the user is relaxed, the reception desk displays detailed input fields and provides a customizable input method. If the user is in a hurry, the reception desk prioritizes displaying the fields that can be completed most quickly. This allows the reception desk to prioritize input fields according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reception desk takes the user's geographical location into consideration and prompts the user to input region-specific daily necessities. For example, if the user lives in a specific region, the reception desk automatically suggests daily necessities commonly used in that region. If the user is traveling, the reception desk suggests necessary daily necessities based on the user's current location. If the user moves, the reception desk suggests necessary daily necessities in the new region. This allows the reception desk to prompt the user to input region-specific daily necessities based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI suggest region-specific daily necessities.

[0077] The reception desk analyzes the user's social media activity and suggests inputs for relevant daily necessities. For example, if the user mentions a specific daily necessity on social media, the reception desk will suggest that daily necessity as an input item. The reception desk will suggest daily necessities related to a specific event based on the user's social media activity. The reception desk will suggest input items by referring to the daily necessities used by the user's social media followers. In this way, the reception desk can suggest inputs for relevant daily necessities based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant daily necessities.

[0078] The data collection unit estimates the user's emotions and adjusts the frequency of campaign information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the collection frequency and collects only important campaign information. If the user is relaxed, the data collection unit increases the collection frequency and provides detailed campaign information. If the user is in a hurry, the data collection unit optimizes the collection frequency and provides important campaign information quickly. This allows the data collection unit to adjust the frequency of campaign information collection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The data collection unit analyzes past campaign data from the shopping site and optimizes the data collection algorithm. For example, the data collection unit identifies the most effective timing for data collection based on past campaign data. The data collection unit analyzes past campaign data, finds specific patterns, and optimizes the data collection algorithm. The data collection unit uses past campaign data to improve the accuracy of the data collection algorithm. This allows the data collection unit to analyze past campaign data from the shopping site and optimize the data collection algorithm. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past campaign data from the shopping site into a generating AI and have the generating AI perform the optimization of the data collection algorithm.

[0080] The data collection unit will add a function to collect campaign information in real time using the shopping site's API. For example, the data collection unit will collect sales information in real time using the shopping site's API. The data collection unit will collect points campaign information in real time using the shopping site's API. The data collection unit will collect member-only campaign information in real time using the shopping site's API. As a result, the data collection unit will be able to collect campaign information in real time using the shopping site's API. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the shopping site's API into a generating AI and have the generating AI perform real-time campaign information collection.

[0081] The data collection unit estimates the user's emotions and prioritizes the campaign information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting the most important campaign information. If the user is relaxed, the data collection unit collects and provides detailed campaign information. If the user is in a hurry, the data collection unit prioritizes collecting campaign information that can be collected quickly. This allows the data collection unit to prioritize the campaign information to collect according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The data collection unit collects region-specific campaign information, taking into account the geographical campaign information of the shopping site. For example, if a user lives in a specific region, the data collection unit collects campaign information held in that region. If a user is traveling, the data collection unit collects region-specific campaign information based on their current location. If a user moves, the data collection unit collects campaign information in their new region. This allows the data collection unit to collect region-specific campaign information while taking into account the geographical campaign information of the shopping site. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical campaign information of the shopping site into a generating AI and have the generating AI perform the collection of region-specific campaign information.

[0083] The collection unit collects relevant product information from shopping sites and suggests it to the user. For example, the collection unit collects and suggests product information related to products the user has purchased. The collection unit collects and suggests relevant product information that the user might be interested in. The collection unit collects and suggests relevant product information based on the user's past purchase history. In this way, the collection unit can collect relevant product information from shopping sites and suggest it to the user. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input relevant product information from shopping sites into a generating AI and have the generating AI perform the task of suggesting relevant products.

[0084] The calculation unit estimates the user's emotions and adjusts the purchase timing calculation method based on the estimated user emotions. For example, if the user is stressed, the calculation unit uses a simple calculation method to quickly determine the purchase timing. If the user is relaxed, the calculation unit uses a detailed calculation method to suggest the optimal purchase timing. If the user is in a hurry, the calculation unit uses a method that allows for quick calculation to determine the purchase timing. In this way, the calculation unit can adjust the purchase timing calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The calculation unit analyzes past purchase data and optimizes the calculation algorithm for purchase timing. For example, the calculation unit identifies the most effective purchase timing based on past purchase data. The calculation unit analyzes past purchase data, finds specific patterns, and optimizes the calculation algorithm. The calculation unit uses past purchase data to improve the accuracy of the calculation algorithm. This allows the calculation unit to analyze past purchase data and optimize the calculation algorithm for purchase timing. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input past purchase data into a generating AI and have the generating AI perform the optimization of the calculation algorithm.

[0086] The calculation unit adds a function to improve the accuracy of predicting purchase timing based on the user's consumption patterns. For example, the calculation unit analyzes the user's consumption patterns and predicts the optimal purchase timing. The calculation unit improves the accuracy of predicting purchase timing based on the user's consumption patterns. The calculation unit optimizes the purchase timing prediction algorithm by referring to the user's consumption patterns. This allows the calculation unit to improve the accuracy of predicting purchase timing based on the user's consumption patterns. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user consumption pattern data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0087] The calculation unit estimates the user's emotions and determines the priority of purchase timing based on the estimated emotions. For example, if the user is stressed, the calculation unit prioritizes calculating the most important purchase timing. If the user is relaxed, the calculation unit calculates and provides detailed purchase timings. If the user is in a hurry, the calculation unit prioritizes determining purchase timings that can be calculated quickly. This allows the calculation unit to prioritize purchase timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The calculation unit calculates region-specific purchase timings, taking into account the user's geographical location information. For example, if the user lives in a specific region, the calculation unit calculates the optimal purchase timing for that region. If the user is traveling, the calculation unit calculates the optimal purchase timing based on the user's current location. If the user moves, the calculation unit calculates the optimal purchase timing for the new region. In this way, the calculation unit can calculate region-specific purchase timings based on the user's geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information into a generating AI and have the generating AI perform the calculation of region-specific purchase timings.

[0089] The calculation unit analyzes the user's social media activity and suggests relevant purchase timings. For example, if the user mentions a specific product on social media, the calculation unit suggests the optimal time to purchase that product. The calculation unit suggests purchase timings related to specific events based on the user's social media activity. The calculation unit suggests purchase timings by referring to when the user's social media followers made purchases. In this way, the calculation unit can suggest relevant purchase timings based on the user's social media activity. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant purchase timings.

[0090] The purchasing department estimates the user's emotions and adjusts the purchase process based on those emotions. For example, if the user is stressed, the purchasing department provides a simple purchase process to complete the purchase quickly. If the user is relaxed, the purchasing department provides detailed purchase options and suggests a customizable purchase process. If the user is in a hurry, the purchasing department prioritizes voice input to complete the purchase quickly. This allows the purchasing department to adjust the purchase process according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The purchasing department analyzes past purchase history and provides the optimal purchase procedure. For example, the purchasing department automatically displays products that the user has frequently purchased in the past as suggestions. The purchasing department prioritizes suggesting purchase methods that the user has used in the past (credit card, e-money, etc.). The purchasing department predicts and suggests purchase procedures to be used at specific times based on the user's past purchase history. In this way, the purchasing department can provide the optimal purchase procedure based on past purchase history. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input past purchase history data into a generating AI and have the generating AI perform the task of providing the optimal purchase procedure.

[0092] The purchasing section will add a function to automatically complete the user's payment method and shipping address information. For example, the purchasing section will automatically complete the payment method the user has used in the past. The purchasing section will automatically complete the shipping address information the user has used in the past. When the user adds new payment method or shipping address information, the purchasing section will automatically complete it by referring to past information. This will enable the purchasing section to automatically complete the user's payment method and shipping address information. Some or all of the above processing in the purchasing section may be performed using AI, for example, or without AI. For example, the purchasing section can input the user's payment method and shipping address information into a generating AI and have the generating AI perform the automatic completion.

[0093] The purchasing department estimates the user's emotions and prioritizes the purchase process based on those emotions. For example, if the user is stressed, the purchasing department will prioritize the most important purchase steps. If the user is relaxed, the purchasing department will provide detailed purchase steps and suggest customizable options. If the user is in a hurry, the purchasing department will prioritize methods that allow for quick completion of the purchase process. This allows the purchasing department to prioritize the purchase process according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The purchasing unit provides region-specific purchasing procedures, taking into account the user's geographical location. For example, if the user lives in a specific region, the purchasing unit provides the optimal purchasing procedure for that region. If the user is traveling, the purchasing unit provides the optimal purchasing procedure based on the user's current location. If the user moves, the purchasing unit provides the optimal purchasing procedure for the new region. In this way, the purchasing unit can provide region-specific purchasing procedures based on the user's geographical location. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or not using AI. For example, the purchasing unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of region-specific purchasing procedures.

[0095] The purchasing department analyzes the user's social media activity and proposes relevant purchase procedures. For example, if the user mentions a specific product on social media, the purchasing department will propose a purchase procedure for that product. The purchasing department will propose purchase procedures related to a specific event based on the user's social media activity. The purchasing department will propose purchase procedures by referring to purchase procedures used by the user's social media followers. In this way, the purchasing department can propose relevant purchase procedures based on the user's social media activity. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for relevant purchase procedures.

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

[0097] The reception system can analyze the user's voice input and automatically recognize consumption frequency and brand preferences. For example, if a user voice-inputs, "I want one bottle of shampoo per month, of a specific brand," the reception system accurately recognizes this and sends it to the AI. It can also convert the user's voice input into text and display a confirmation screen. Furthermore, the reception system can estimate the user's emotions from their voice tone and speaking style and adjust the input method accordingly. For example, if the user is tired, it can provide a simple voice input interface to allow them to complete the input quickly. This enables the reception system to analyze the user's voice input and automatically recognize consumption frequency and brand preferences.

[0098] The data collection unit can analyze a user's purchase history and prioritize collecting campaign information they have previously used. For example, if a user has frequently purchased from a particular shopping site in the past, the unit will prioritize collecting campaign information from that site. The data collection unit can also analyze the effectiveness of campaigns the user has previously used and collect the most effective campaign information. Furthermore, the data collection unit can collect campaign information related to specific seasons or events from the user's purchase history. This allows the data collection unit to analyze the user's purchase history and prioritize collecting campaign information they have previously used.

[0099] The calculation unit can estimate the user's emotions and adjust the purchase timing calculation method based on the estimated emotions. For example, if the user is stressed, a simple calculation method is used to quickly determine the purchase timing. If the user is relaxed, a detailed calculation method is used to suggest the optimal purchase timing. Furthermore, if the user is in a hurry, a method that allows for quick calculation can be used to determine the purchase timing. In this way, the calculation unit can adjust the purchase timing calculation method according to the user's emotions.

[0100] The calculation unit can be enhanced with a function to improve the accuracy of purchase timing predictions based on user consumption patterns. For example, it can analyze user consumption patterns and predict the optimal purchase timing. It can improve the accuracy of purchase timing predictions based on user consumption patterns. Furthermore, it can optimize the purchase timing prediction algorithm by referring to user consumption patterns. This allows the calculation unit to improve the accuracy of purchase timing predictions based on user consumption patterns.

[0101] The purchasing function can estimate the user's emotions and adjust the purchase process based on those emotions. For example, if the user is stressed, it can offer a simple purchase process to complete the purchase quickly. If the user is relaxed, it can offer detailed purchase options and suggest a customizable purchase process. Furthermore, if the user is in a hurry, it can prioritize voice input to complete the purchase quickly. In this way, the purchasing function can adjust the purchase process according to the user's emotions.

[0102] The reception desk can take the user's geographical location into consideration and prompt them to input region-specific daily necessities. For example, if a user lives in a specific region, it can automatically suggest daily necessities commonly used in that area. If the user is traveling, it can suggest necessary daily necessities based on their current location. Furthermore, if the user has moved, it can suggest necessary daily necessities for their new area. This allows the reception desk to prompt users to input region-specific daily necessities based on their geographical location.

[0103] The data collection unit can estimate the user's emotions and adjust the frequency of campaign information collection based on those emotions. For example, if the user is stressed, the collection frequency is reduced, and only important campaign information is collected. If the user is relaxed, the collection frequency is increased, and detailed campaign information is provided. Furthermore, if the user is in a hurry, the collection frequency can be optimized to quickly provide important campaign information. In this way, the data collection unit can adjust the frequency of campaign information collection according to the user's emotions.

[0104] The data collection unit can be enhanced with the ability to collect campaign information in real time using shopping site APIs. For example, it can collect sales information in real time using shopping site APIs. It can also collect points campaign information in real time using shopping site APIs. Furthermore, it can collect member-only campaign information in real time using shopping site APIs. This allows the data collection unit to collect campaign information in real time using shopping site APIs.

[0105] The calculation unit can analyze a user's social media activity and suggest relevant purchase timings. For example, if a user mentions a specific product on social media, it can suggest the optimal time to purchase that product. It can also suggest purchase timings related to specific events based on the user's social media activity. Furthermore, it can suggest purchase timings by referring to when the user's social media followers made purchases. In this way, the calculation unit can suggest relevant purchase timings based on the user's social media activity.

[0106] The purchasing department can estimate the user's emotions and prioritize the purchase process based on those emotions. For example, if the user is stressed, it will prioritize the most important purchase steps. If the user is relaxed, it will provide detailed purchase steps and suggest customizable options. Furthermore, if the user is in a hurry, it can prioritize providing ways to complete the purchase quickly. In this way, the purchasing department can prioritize the purchase process according to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives input from the user regarding the frequency of use and brand preferences for daily necessities. For example, the user can specify the brand of shampoo but not laundry detergent or fabric softener. The reception desk then sends the entered information to the AI. Step 2: The collection unit collects campaign information from shopping sites based on the information entered by the reception unit. The collection unit, for example, visits multiple shopping sites to collect information on sales and campaigns. For example, the collection unit collects information on campaigns where more points can be earned on a specific day, or sales where specific products are discounted. Step 3: The calculation unit calculates the optimal purchase timing, taking into account the campaign information collected by the collection unit and the user's points ecosystem and membership attributes. For example, if the user has specified a particular points ecosystem, the calculation unit will make a purchase at the time when the most points are accumulated in that ecosystem. The calculation unit also considers utilizing specific member-only campaigns depending on the user's membership attributes. Step 4: The purchasing unit makes purchases based on the purchase timing calculated by the calculation unit. For example, the purchasing unit makes purchases using the most suitable campaigns. This allows the agent system to enable users to purchase daily necessities at the optimal time.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, and purchase unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs the frequency of consumption of daily necessities and brand preferences. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, where campaign information from shopping sites is collected. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, where the optimal purchase timing is calculated considering the collected campaign information and the user's point economy and membership attributes. The purchase unit is implemented by, for example, the control unit 46A of the smart device 14, where the purchase is made based on the calculated purchase timing. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, and purchase unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs the frequency of consumption of daily necessities and brand preferences. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where campaign information from shopping sites is collected. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the optimal purchase timing is calculated considering the collected campaign information and the user's point economy and membership attributes. The purchase unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the purchase is made based on the calculated purchase timing. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, and purchase unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs the frequency of consumption of daily necessities and brand specifications. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where campaign information from shopping sites is collected. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the optimal purchase timing is calculated considering the collected campaign information and the user's point economy and membership attributes. The purchase unit is implemented by, for example, the control unit 46A of the headset terminal 314, where the purchase is made based on the calculated purchase timing. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, and purchase unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs the frequency of consumption of daily necessities and brand specifications. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where campaign information from shopping sites is collected. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the optimal purchase timing is calculated considering the collected campaign information and the user's points ecosystem and membership attributes. The purchase unit is implemented by, for example, the control unit 46A of the robot 414, where the purchase is made based on the calculated purchase timing. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk where users input the frequency of use and brand preferences for daily necessities, A collection unit collects campaign information from shopping sites based on the information entered by the reception unit, A calculation unit calculates the optimal purchase timing considering the campaign information collected by the aforementioned collection unit and the user's points ecosystem and membership attributes. The system includes a purchase unit that performs a purchase based on the purchase timing calculated by the calculation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is I visit multiple shopping websites to gather information on sales and promotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The calculation unit, We calculate the optimal purchase timing by considering the collected campaign information and the user's points ecosystem and membership attributes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The calculation unit, Based on the number of days since the last purchase and the frequency of use, we predict when consumables are likely to run out. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned purchasing department, Make your purchase by taking advantage of the best campaign. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for consumption frequency and brand preference based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and provides the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Add a feature that automatically completes input fields based on the user's lifestyle and usage. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Considering the user's geographical location, prompts them to input information about local daily necessities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze users' social media activity and suggest relevant inputs for everyday items. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate user sentiment and adjust the frequency of campaign information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We analyze past campaign data from shopping sites and optimize our data collection algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is We will add a feature that collects campaign information in real time using the shopping site's API. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates user sentiment and prioritizes the campaign information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is We collect region-specific campaign information, taking into account the geographical campaign details of the shopping site. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is Collect related product information from shopping sites and suggest them to users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit, We estimate the user's emotions and adjust the calculation method for purchase timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit, We analyze past purchase data and optimize the algorithm for calculating the optimal purchase timing. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit, We will add a feature that improves the accuracy of predicting purchase timing based on user consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit, It estimates the user's emotions and prioritizes the timing of purchases based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The calculation unit, The system calculates region-specific purchase timings, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The calculation unit, We analyze users' social media activity and suggest relevant purchase timings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned purchasing department, It estimates the user's emotions and adjusts the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned purchasing department, We analyze your past purchase history and provide the most suitable purchase procedure. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned purchasing department, Add a feature that automatically completes the user's payment method and shipping address information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned purchasing department, It estimates the user's emotions and determines the priority of the purchase process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned purchasing department, We provide region-specific purchasing procedures, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned purchasing department, Analyze users' social media activity and suggest relevant purchase options. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users input the frequency of use and brand preferences for daily necessities, A collection unit collects campaign information from shopping sites based on the information entered by the reception unit, A calculation unit calculates the optimal purchase timing considering the campaign information collected by the aforementioned collection unit and the user's points ecosystem and membership attributes. The system includes a purchase unit that performs a purchase based on the purchase timing calculated by the calculation unit. A system characterized by the following features.

2. The aforementioned collection unit is I visit multiple shopping websites to gather information on sales and promotions. The system according to feature 1.

3. The calculation unit, We calculate the optimal purchase timing by considering the collected campaign information and the user's points ecosystem and membership attributes. The system according to feature 1.

4. The calculation unit, Based on the number of days since the last purchase and the frequency of use, we predict when consumables are likely to run out. The system according to feature 1.

5. The aforementioned purchasing department, Make your purchase by taking advantage of the best campaign. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for consumption frequency and brand preference based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history and provide the optimal input interface. The system according to feature 1.

8. The aforementioned reception unit is Add a feature that automatically completes input fields based on the user's lifestyle and usage. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

10. The aforementioned reception unit is Considering the user's geographical location, prompts them to input information about local daily necessities. The system according to feature 1.

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