System

Abstract

An object of a system according to an embodiment is to propose an optimal delivery timing based on a use frequency of a user.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects use frequency data of a user. The analysis unit analyzes the data collected by the collection unit and calculates a delivery timing. The proposing section proposes a delivery timing to the user based on the analysis result obtained by the analyzing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology is unable to suggest appropriate delivery timing based on the frequency of use by the user, resulting in the problem of overstock.

[0005] The system according to the embodiment aims to propose the optimal delivery timing based on the frequency of use by the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data on the frequency of use of a user. The analysis unit analyzes the data collected by the collection unit and calculates the timing of delivery. The suggestion unit suggests the timing of delivery to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the optimal delivery timing based on the frequency of use by the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A customized subscription system according to an embodiment of the present invention collects user usage frequency data, analyzes it using AI, and proposes optimal delivery timings. The customized subscription system collects user usage frequency data, analyzes it using AI, and proposes optimal delivery timings to prevent overstocking and improve user convenience. For example, the customized subscription system records in detail how frequently a user uses a product and when the product is needed. Then, the customized subscription system uses AI to analyze the collected data and calculates the optimal delivery timing for the user. Furthermore, the customized subscription system allows the user to receive the product at a timing that suits them best based on the delivery timing proposed by the AI. This allows the customized subscription system to receive the product at the optimal timing based on the user's usage frequency, preventing overstocking. This allows the customized subscription system to propose optimal delivery timings based on the user's usage frequency, preventing overstocking and improving user convenience. For example, if a user uses a consumable item daily, the product can be delivered daily according to the timing proposed by the AI. This allows the user to always have the product they need on hand and preventing overstocking.

[0029] A customized subscription system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects user usage frequency data. The user usage frequency data includes, but is not limited to, the number of uses, duration of use, and location of use. The collection unit records in detail, for example, how often a user uses a product and when the product is needed. The analysis unit analyzes the data collected by the collection unit and calculates the optimal delivery timing. The analysis unit calculates the optimal delivery timing for the user, for example, using a prediction algorithm based on the usage frequency data. The suggestion unit suggests the optimal delivery timing for the user based on the analysis results obtained by the analysis unit. The suggestion unit suggests the optimal delivery timing for the user based on, for example, the analysis results. As a result, the customized subscription system according to an embodiment suggests the optimal delivery timing based on the user's usage frequency, preventing overstocking and improving user convenience.

[0030] The collection unit can record how often a user uses a product and when the product is needed. For example, the collection unit records how often a user uses a product on a daily, weekly, or monthly basis. The collection unit can also record when the user needs the product based on time of day or before or after a specific event. This allows for more accurate data collection by recording the user's frequency of use and when the product is needed in detail. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input user usage frequency data into AI, which then analyzes and records the data.

[0031] The analysis unit can calculate the optimal delivery timing based on the user's usage frequency data. The analysis unit calculates the optimal delivery timing for the user, for example, using a prediction algorithm based on the usage frequency data. For example, for a consumable item used daily by the user, the analysis unit predicts when the consumable item will run out and suggests an appropriate delivery timing. Furthermore, for a product used once a week by the user, the analysis unit can also calculate the timing to receive the product once a week. In this way, calculating the optimal delivery timing based on the user's usage frequency data can prevent overstocking. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's usage frequency data into AI, which then calculates the optimal delivery timing.

[0032] The suggestion unit can suggest the optimal delivery timing to the user based on the analysis results. The suggestion unit, for example, suggests the optimal delivery timing to the user based on the analysis results. For example, if the user uses a consumable item daily, the suggestion unit can suggest the timing to receive the product every day. Also, if the user uses a product once a week, the suggestion unit can suggest the timing to receive the product once a week. This makes it possible to improve user convenience by suggesting the optimal delivery timing based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can suggest the optimal delivery timing calculated by AI to the user.

[0033] The collection unit can analyze the user's past usage history and select the optimal data collection method. The collection unit customizes the collection method based on, for example, products that the user has frequently used in the past. The collection unit can also collect data for specific time periods from the user's past usage history. The collection unit can also analyze the user's past usage patterns and suggest the optimal collection method. This allows the analysis of past usage history to select the optimal data collection method and enable efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past usage history data into AI, which can select the optimal data collection method.

[0034] When collecting usage frequency data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit filters the data to be collected according to the user's current living situation. The collection unit can also select the data to be collected based on the user's areas of interest. The collection unit can also optimize the data to be collected by taking the user's living situation and areas of interest into consideration. This allows more relevant data to be collected by filtering the data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and area of ​​interest data into AI, which then filters the data.

[0035] When collecting usage frequency data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects voice data. Furthermore, when the user uses text input, the collection unit can also collect text data. Furthermore, when the user uses image input, the collection unit can also collect image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, which then selects the optimal collection means.

[0036] When collecting usage frequency data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also select highly relevant data based on the user's geographical location information. The collection unit can also collect optimal data according to the user's current location. This allows highly relevant data to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information data into AI, which can select highly relevant data.

[0037] When collecting the usage frequency data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data regarding the locations where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related data.

[0038] When collecting usage frequency data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the collection method, for example, based on feedback provided by the user in the past. The collection unit can also select the optimal collection method from the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. This allows the optimal collection method to be selected by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into AI, which can customize the collection method.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the use frequency data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a concise analysis on less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the use frequency data to AI, which can adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. For example, for consumables, the analysis unit applies an analysis algorithm that takes into account the consumption rate. The analysis unit can also apply a detailed analysis algorithm to expensive products. The analysis unit can also select the optimal analysis algorithm depending on the product category. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the product category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data into AI, which then applies the optimal analysis algorithm.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also perform highly accurate analysis from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the use frequency data. For example, the analysis unit prioritizes analysis of data submitted earlier. The analysis unit can also postpone analysis of data submitted later. The analysis unit can also determine the priority of analysis based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of use frequency data into AI, and the AI ​​can determine the priority of analysis.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the use frequency data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the use frequency data into AI, which can then adjust the order of analysis.

[0044] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also provide analysis results in simpler language for users with low levels of expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easy for users to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology.

[0045] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the product. For example, the suggestion unit makes a detailed proposal for an important product. The suggestion unit can also make a concise proposal for a less important product. The suggestion unit can also adjust the level of detail of the proposal according to the importance of the product. This makes it possible to provide an optimal proposal for the user by adjusting the level of detail of the proposal according to the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input product importance data into AI, which can then adjust the level of detail of the proposal.

[0046] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. For example, for consumables, the suggestion unit can apply a suggestion algorithm that takes into account the consumption rate. In addition, for expensive products, the suggestion unit can also apply a detailed suggestion algorithm. In addition, the suggestion unit can select the optimal suggestion algorithm depending on the product category. This enables highly accurate suggestions by applying the optimal suggestion algorithm depending on the product category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product category data into AI, which then applies the optimal suggestion algorithm.

[0047] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also make highly accurate suggestions based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. As a result, the accuracy of the suggestion is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI, which can improve the accuracy of the suggestion.

[0048] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. For example, the proposal unit preferentially proposes products that have been submitted earlier. The proposal unit can also postpone the proposal of products that have been submitted later. The proposal unit can also determine the priority of the proposal based on the submission time. This enables efficient proposals by determining the priority of the proposal based on the submission time of the product. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input product submission time data into AI, which can then determine the priority of the proposal.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit preferentially suggests highly relevant products. The suggestion unit can also postpone the suggestion of less relevant products. The suggestion unit can also adjust the order of suggestions based on the relevance of products. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of products. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product relevance data into AI, which can then adjust the order of suggestions.

[0050] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit uses a lot of technical terminology for a user with high level of expertise. The suggestion unit can also provide a proposal in simple language for a user with low level of expertise. The suggestion unit can also adjust the use of technical terminology in the proposal according to the user's level of expertise. This makes it possible to provide a proposal that is easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into AI, which can adjust the use of technical terminology.

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

[0052] The custom subscription system can also analyze a user's purchasing history, and the analysis unit can calculate the optimal delivery timing based on that data. For example, it can analyze the frequency of use of products that a user has purchased in the past and predict when similar products will be needed. It can also identify products that a user purchases during a particular season and suggest products that suit that season. It can also analyze products that a user purchases for specific events (such as birthdays or anniversaries) and deliver products that are timed to suit those events. This can improve the user's purchasing experience by suggesting the optimal delivery timing based on the user's purchasing history.

[0053] The custom subscription system can also analyze a user's social media activity, and the analysis unit can calculate the optimal delivery timing based on that data. For example, based on the location where the user checked in on social media, it can suggest products needed at that location. It can also analyze the content of a user's social media posts and suggest products related to the content of the posts. It can also suggest products that the user's friends are using based on the activity of the user's friends on social media. This makes it possible to suggest the optimal delivery timing based on the user's social media activity, making it possible to suggest products that are tailored to the user's interests.

[0054] The custom subscription system can also analyze the user's geographic location information, and the analysis unit can calculate the optimal delivery timing based on that data. For example, if the user is in a specific area, products needed in that area can be suggested. It can also select highly relevant data based on the user's geographic location information and suggest products based on that data. It can also collect optimal data according to the user's current location and suggest products based on that data. This makes it possible to suggest products that suit the user's lifestyle by suggesting the optimal delivery timing based on the user's geographic location information.

[0055] The custom subscription system can also collect data on the user's lifestyle habits, and the analysis unit can calculate the optimal delivery timing based on that data. For example, it can collect the user's wake-up time and bedtime and deliver products based on those times. It can also collect the user's meal times and deliver ingredients based on meal times. It can also collect the user's exercise habits and suggest products that will be needed after exercise. This makes it possible to suggest products that fit the user's lifestyle by suggesting the optimal delivery timing based on the user's lifestyle habits.

[0056] The custom subscription system can also analyze a user's purchasing patterns, and the analysis unit can suggest optimal product sets based on that data. For example, it can analyze combinations of products a user has purchased in the past and suggest similar product sets. It can also identify products that a user purchases in a particular season and suggest product sets that match that season. It can also analyze products that a user purchases for a particular event (e.g., a birthday or anniversary) and suggest product sets that match that event. This can improve the user's purchasing experience by suggesting optimal product sets based on the user's purchasing patterns.

[0057] The custom subscription system can also collect user feedback, and the analysis unit can use that data to propose optimal product improvement proposals. For example, it can identify areas for product improvement based on feedback previously provided by the user and propose those improvement proposals. It can also make proposals to add specific features based on user feedback. It can also reflect user feedback and make proposals to improve product design and usability. This makes it possible to improve user satisfaction by proposing optimal product improvement proposals based on user feedback.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects user usage frequency data. The user usage frequency data includes the number of uses, the duration of use, and the location of use. The collection unit records in detail how often the user uses the product and when the product is needed. Step 2: The analysis unit analyzes the data collected by the collection unit and calculates the optimal delivery timing. The analysis unit calculates the optimal delivery timing for the user using a prediction algorithm based on usage frequency data. Step 3: The suggestion unit suggests the optimal delivery timing to the user based on the analysis results obtained by the analysis unit. This allows the optimal delivery timing to be suggested based on the frequency of use by the user, preventing excess inventory and improving user convenience.

[0060] (Example 2) A customized subscription system according to an embodiment of the present invention collects user usage frequency data, analyzes it using AI, and proposes optimal delivery timings. The customized subscription system collects user usage frequency data, analyzes it using AI, and proposes optimal delivery timings to prevent overstocking and improve user convenience. For example, the customized subscription system records in detail how frequently a user uses a product and when the product is needed. Then, the customized subscription system uses AI to analyze the collected data and calculates the optimal delivery timing for the user. Furthermore, the customized subscription system allows the user to receive the product at a timing that suits them best based on the delivery timing proposed by the AI. This allows the customized subscription system to receive the product at the optimal timing based on the user's usage frequency, preventing overstocking. This allows the customized subscription system to propose optimal delivery timings based on the user's usage frequency, preventing overstocking and improving user convenience. For example, if a user uses a consumable item daily, the product can be delivered daily according to the timing proposed by the AI. This allows the user to always have the product they need on hand and preventing overstocking.

[0061] A customized subscription system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects user usage frequency data. The user usage frequency data includes, but is not limited to, the number of uses, duration of use, and location of use. The collection unit records in detail, for example, how often a user uses a product and when the product is needed. The analysis unit analyzes the data collected by the collection unit and calculates the optimal delivery timing. The analysis unit calculates the optimal delivery timing for the user, for example, using a prediction algorithm based on the usage frequency data. The suggestion unit suggests the optimal delivery timing for the user based on the analysis results obtained by the analysis unit. The suggestion unit suggests the optimal delivery timing for the user based on, for example, the analysis results. As a result, the customized subscription system according to an embodiment suggests the optimal delivery timing based on the user's usage frequency, preventing overstocking and improving user convenience.

[0062] The collection unit can record how often a user uses a product and when the product is needed. For example, the collection unit records how often a user uses a product on a daily, weekly, or monthly basis. The collection unit can also record when the user needs the product based on time of day or before or after a specific event. This allows for more accurate data collection by recording the user's frequency of use and when the product is needed in detail. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input user usage frequency data into AI, which then analyzes and records the data.

[0063] The analysis unit can calculate the optimal delivery timing based on the user's usage frequency data. The analysis unit calculates the optimal delivery timing for the user, for example, using a prediction algorithm based on the usage frequency data. For example, for a consumable item used daily by the user, the analysis unit predicts when the consumable item will run out and suggests an appropriate delivery timing. Furthermore, for a product used once a week by the user, the analysis unit can also calculate the timing to receive the product once a week. In this way, calculating the optimal delivery timing based on the user's usage frequency data can prevent overstocking. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's usage frequency data into AI, which then calculates the optimal delivery timing.

[0064] The suggestion unit can suggest the optimal delivery timing to the user based on the analysis results. The suggestion unit, for example, suggests the optimal delivery timing to the user based on the analysis results. For example, if the user uses a consumable item daily, the suggestion unit can suggest the timing to receive the product every day. Also, if the user uses a product once a week, the suggestion unit can suggest the timing to receive the product once a week. This makes it possible to improve user convenience by suggesting the optimal delivery timing based on the analysis results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can suggest the optimal delivery timing calculated by AI to the user.

[0065] The collection unit can analyze the user's emotions and adjust the timing of collecting usage frequency data based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to quickly collect data. This allows the collection timing to be adjusted according to the user's emotions, reducing the user's burden and collecting more detailed data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI, which can then adjust the collection timing.

[0066] The collection unit can analyze the user's past usage history and select the optimal data collection method. The collection unit customizes the collection method based on, for example, products that the user has frequently used in the past. The collection unit can also collect data for specific time periods from the user's past usage history. The collection unit can also analyze the user's past usage patterns and suggest the optimal collection method. This allows the analysis of past usage history to select the optimal data collection method and enable efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past usage history data into AI, which can select the optimal data collection method.

[0067] When collecting usage frequency data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit filters the data to be collected according to the user's current living situation. The collection unit can also select the data to be collected based on the user's areas of interest. The collection unit can also optimize the data to be collected by taking the user's living situation and areas of interest into consideration. This allows more relevant data to be collected by filtering the data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and area of ​​interest data into AI, which then filters the data.

[0068] When collecting usage frequency data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects voice data. Furthermore, when the user uses text input, the collection unit can also collect text data. Furthermore, when the user uses image input, the collection unit can also collect image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, which then selects the optimal collection means.

[0069] The collection unit can analyze the user's emotions and determine the priority of data to be collected based on the analyzed user's emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting important data. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. Thus, by determining the priority of data according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI, which can then prioritize the data.

[0070] When collecting usage frequency data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also select highly relevant data based on the user's geographical location information. The collection unit can also collect optimal data according to the user's current location. This allows highly relevant data to be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information data into AI, which can select highly relevant data.

[0071] When collecting the usage frequency data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data regarding the locations where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related data.

[0072] When collecting usage frequency data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the collection method, for example, based on feedback provided by the user in the past. The collection unit can also select the optimal collection method from the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. This allows the optimal collection method to be selected by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the user's past feedback data into AI, which can customize the collection method.

[0073] The analysis unit can analyze the user's emotions and adjust the presentation method of the analysis based on the analyzed user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the presentation method of the analysis.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the use frequency data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a concise analysis on less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the use frequency data to AI, which can adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the product category. For example, for consumables, the analysis unit applies an analysis algorithm that takes into account the consumption rate. The analysis unit can also apply a detailed analysis algorithm to expensive products. The analysis unit can also select the optimal analysis algorithm depending on the product category. This enables highly accurate analysis by applying the optimal analysis algorithm depending on the product category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input product category data into AI, which then applies the optimal analysis algorithm.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also perform highly accurate analysis from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the accuracy of the analysis.

[0077] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the length of the analysis.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the use frequency data. For example, the analysis unit prioritizes analysis of data submitted earlier. The analysis unit can also postpone analysis of data submitted later. The analysis unit can also determine the priority of analysis based on the submission time. This enables efficient analysis by determining the priority of analysis based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of use frequency data into AI, and the AI ​​can determine the priority of analysis.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the use frequency data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the use frequency data into AI, which can then adjust the order of analysis.

[0080] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terminology for users with high levels of expertise. The analysis unit can also provide analysis results in simpler language for users with low levels of expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easy for users to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI, which can then adjust the use of technical terminology.

[0081] The suggestion unit can analyze the user's emotions and adjust the way the suggestions are expressed based on the analyzed user's emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually stimulating suggestions when the user is excited. By adjusting the way the suggestions are expressed according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can then adjust the way the suggestions are expressed.

[0082] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the product. For example, the suggestion unit makes a detailed proposal for an important product. The suggestion unit can also make a concise proposal for a less important product. The suggestion unit can also adjust the level of detail of the proposal according to the importance of the product. This makes it possible to provide an optimal proposal for the user by adjusting the level of detail of the proposal according to the importance of the product. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input product importance data into AI, which can then adjust the level of detail of the proposal.

[0083] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. For example, for consumables, the suggestion unit can apply a suggestion algorithm that takes into account the consumption rate. In addition, for expensive products, the suggestion unit can also apply a detailed suggestion algorithm. In addition, the suggestion unit can select the optimal suggestion algorithm depending on the product category. This enables highly accurate suggestions by applying the optimal suggestion algorithm depending on the product category. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product category data into AI, which then applies the optimal suggestion algorithm.

[0084] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also make highly accurate suggestions based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. As a result, the accuracy of the suggestion is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI, which can improve the accuracy of the suggestion.

[0085] The suggestion unit can analyze the user's emotions and adjust the length of the suggestions based on the analyzed user's emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the length of the suggestions according to the user's emotions, the suggestion unit can provide optimal suggestions for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into an AI, which can then adjust the length of the suggestions.

[0086] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. For example, the proposal unit preferentially proposes products that have been submitted earlier. The proposal unit can also postpone the proposal of products that have been submitted later. The proposal unit can also determine the priority of the proposal based on the submission time. This enables efficient proposals by determining the priority of the proposal based on the submission time of the product. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input product submission time data into AI, which can then determine the priority of the proposal.

[0087] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. For example, the suggestion unit preferentially suggests highly relevant products. The suggestion unit can also postpone the suggestion of less relevant products. The suggestion unit can also adjust the order of suggestions based on the relevance of products. This enables efficient suggestions by adjusting the order of suggestions based on the relevance of products. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input product relevance data into AI, which can then adjust the order of suggestions.

[0088] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit uses a lot of technical terminology for a user with high level of expertise. The suggestion unit can also provide a proposal in simple language for a user with low level of expertise. The suggestion unit can also adjust the use of technical terminology in the proposal according to the user's level of expertise. This makes it possible to provide a proposal that is easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into AI, which can adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user usage frequency data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to calculate the optimal delivery timing. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the optimal delivery timing to the user based on the analysis results. The suggestion unit may be realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user usage frequency data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to calculate the optimal delivery timing. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the optimal delivery timing to the user based on the analysis results. The suggestion unit may be realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user usage frequency data using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to calculate the optimal delivery timing. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the optimal delivery timing to the user based on the analysis results. The suggestion unit may be realized by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user usage frequency data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 by the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to calculate the optimal delivery timing. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests the optimal delivery timing to the user based on the analysis results. The suggestion unit may be realized by the control unit 46A of the robot 414.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The custom subscription system can also collect user health data, and the analysis unit can use that data to calculate the optimal delivery timing. For example, by collecting and analyzing a user's sleep patterns and exercise volume, it can deliver products at times when the user is most relaxed. It can also collect a user's dietary data and deliver ingredients at times when they are most likely to consume them. It can also monitor a user's stress level and suggest products that have a relaxing effect during times of high stress. This can support the user's health management by suggesting the optimal delivery timing based on the user's health condition.

[0091] The custom subscription system can also analyze a user's purchasing history, and the analysis unit can calculate the optimal delivery timing based on that data. For example, it can analyze the frequency of use of products that a user has purchased in the past and predict when similar products will be needed. It can also identify products that a user purchases during a particular season and suggest products that suit that season. It can also analyze products that a user purchases for specific events (such as birthdays or anniversaries) and deliver products that are timed to suit those events. This can improve the user's purchasing experience by suggesting the optimal delivery timing based on the user's purchasing history.

[0092] The custom subscription system can also analyze a user's social media activity, and the analysis unit can calculate the optimal delivery timing based on that data. For example, based on the location where the user checked in on social media, it can suggest products needed at that location. It can also analyze the content of a user's social media posts and suggest products related to the content of the posts. It can also suggest products that the user's friends are using based on the activity of the user's friends on social media. This makes it possible to suggest the optimal delivery timing based on the user's social media activity, making it possible to suggest products that are tailored to the user's interests.

[0093] The custom subscription system can also analyze the user's geographic location information, and the analysis unit can calculate the optimal delivery timing based on that data. For example, if the user is in a specific area, products needed in that area can be suggested. It can also select highly relevant data based on the user's geographic location information and suggest products based on that data. It can also collect optimal data according to the user's current location and suggest products based on that data. This makes it possible to suggest products that suit the user's lifestyle by suggesting the optimal delivery timing based on the user's geographic location information.

[0094] The custom subscription system can also analyze the user's emotions, and the analysis unit can calculate the optimal delivery timing based on that data. For example, if the user is feeling stressed, it can suggest products that have a relaxing effect. Or, if the user is relaxed, it can suggest products that will increase enjoyment. Furthermore, if the user is in a hurry, it can suggest products that can be used quickly. This makes it possible to suggest products that match the user's emotions by suggesting the optimal delivery timing based on the user's emotions.

[0095] The custom subscription system can also collect data on the user's lifestyle habits, and the analysis unit can calculate the optimal delivery timing based on that data. For example, it can collect the user's wake-up time and bedtime and deliver products based on those times. It can also collect the user's meal times and deliver ingredients based on meal times. It can also collect the user's exercise habits and suggest products that will be needed after exercise. This makes it possible to suggest products that fit the user's lifestyle by suggesting the optimal delivery timing based on the user's lifestyle habits.

[0096] The custom subscription system can also analyze the user's emotions, with the analysis unit suggesting the most appropriate product category based on that data. For example, if the user is feeling stressed, it can suggest a product category that has a relaxing effect. Or, if the user is relaxed, it can suggest a product category that increases enjoyment. Furthermore, if the user is in a hurry, it can suggest a product category that can be used quickly. This makes it possible to suggest products that match the user's emotions by suggesting the most appropriate product category based on the user's emotions.

[0097] The custom subscription system can also analyze a user's purchasing patterns, and the analysis unit can suggest optimal product sets based on that data. For example, it can analyze combinations of products a user has purchased in the past and suggest similar product sets. It can also identify products that a user purchases in a particular season and suggest product sets that match that season. It can also analyze products that a user purchases for a particular event (e.g., a birthday or anniversary) and suggest product sets that match that event. This can improve the user's purchasing experience by suggesting optimal product sets based on the user's purchasing patterns.

[0098] The custom subscription system can also analyze the user's emotions, with the analysis unit suggesting the optimal product price range based on that data. For example, if the user is feeling stressed, it can suggest reasonably priced products. If the user is relaxed, it can suggest slightly more expensive products. Furthermore, if the user is in a hurry, it can suggest products in a price range that can be purchased quickly. This makes it possible to suggest products that match the user's emotions by suggesting the optimal product price range based on the user's emotions.

[0099] The custom subscription system can also collect user feedback, and the analysis unit can use that data to propose optimal product improvement proposals. For example, it can identify areas for product improvement based on feedback previously provided by the user and propose those improvement proposals. It can also make proposals to add specific features based on user feedback. It can also reflect user feedback and make proposals to improve product design and usability. This makes it possible to improve user satisfaction by proposing optimal product improvement proposals based on user feedback.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects user usage frequency data. The user usage frequency data includes the number of uses, the duration of use, and the location of use. The collection unit records in detail how often the user uses the product and when the product is needed. Step 2: The analysis unit analyzes the data collected by the collection unit and calculates the optimal delivery timing. The analysis unit calculates the optimal delivery timing for the user using a prediction algorithm based on usage frequency data. Step 3: The suggestion unit suggests the optimal delivery timing to the user based on the analysis results obtained by the analysis unit. This allows the optimal delivery timing to be suggested based on the frequency of use by the user, preventing excess inventory and improving user convenience.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user usage frequency data; an analysis unit that analyzes the data collected by the collection unit and calculates delivery timing; a suggestion unit that suggests a delivery timing to the user based on the analysis result obtained by the analysis unit; A system characterized by:

2. The collecting unit Record how often users use your products and when they need them 2. The system of claim 1.

3. The analysis unit Calculate the optimal delivery timing based on user usage frequency data 2. The system of claim 1.

4. The proposal unit Propose optimal delivery timing to users based on analysis results 2. The system of claim 1.

5. The collecting unit Analyze user sentiment and adjust the timing of collecting usage frequency data based on the analyzed user sentiment.

2. The system of claim 1.

6. The collecting unit Analyze users' past usage history and select the most appropriate data collection method 2. The system of claim 1.

7. The collecting unit When collecting usage data, filter it based on the user's current lifestyle and interests.

2. The system of claim 1.

8. The collecting unit When collecting usage frequency data, select the optimal collection method according to the user's input method.

2. The system of claim 1.

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