system

Abstract

The system according to the embodiment aims to analyze data from IoT home appliances and recommend optimal products to users. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data from IoT home appliances. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends products based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately utilized data from IoT home appliances to recommend optimal products to users, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze data from IoT home appliances and recommend optimal products to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data from IoT home appliances. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends products based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data from IoT home appliances and recommend optimal products to users. [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 recommendation function service according to an embodiment of the present invention is a system that collects and analyzes data on IoT home appliances and recommends products. The recommendation function service recommends products on e-commerce sites by allowing users to select IoT home appliances and configure their connection settings. For example, a user selects IoT home appliances such as a refrigerator, washing machine, or air conditioner and configures their connection settings. The IoT home appliance data is then sent to a cloud server, which analyzes the received data. The analyzed data is then sent to the e-commerce site's recommendation engine, which then recommends optimal products for the user. For example, refrigerator data can be used to recommend recipes, seasonings, and storage containers related to stored food. Washing machine data can be used to recommend detergents, fabric softeners, and laundry nets. Air conditioner data can be used to recommend air purifiers, humidifiers, dehumidifiers, and other appliances based on the room temperature and humidity. This allows the recommendation function service to utilize users' IoT home appliance data to make e-commerce site shopping more convenient and enjoyable. Furthermore, e-commerce operators can effectively recommend products tailored to users' needs, potentially increasing their sales.

[0029] A recommendation function service according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data from IoT home appliances. For example, the collection unit collects data on the temperature and humidity inside a refrigerator, and the types and amounts of food stored therein. The collection unit can also collect data on the frequency of washing machine use and the type of detergent used. The collection unit can also collect data on the indoor temperature, humidity, and usage time of an air conditioner. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes data from the refrigerator to analyze the temperature and humidity inside the refrigerator, and the types and amounts of food stored therein. The analysis unit can also analyze data from the washing machine to analyze the frequency of laundry and the type of detergent used. The analysis unit can also analyze data from the air conditioner to analyze the indoor temperature, humidity, and usage time. The recommendation unit recommends products based on the analysis results obtained by the analysis unit. For example, the recommendation unit recommends recipes, seasonings, and storage containers related to the food stored therein based on the refrigerator data. The recommendation unit can also recommend detergent, fabric softener, and laundry net to be used based on washing machine data. Furthermore, the recommendation unit can also recommend air purifiers, humidifiers, and dehumidifiers based on indoor temperature and humidity based on air conditioner data. This allows the recommendation function service according to the embodiment to provide users with the most suitable products.

[0030] The analysis unit can include a refrigerator analysis unit that analyzes refrigerator data in detail. The refrigerator analysis unit analyzes, for example, the temperature and humidity inside the refrigerator, and the type and amount of food stored in the refrigerator in detail. For example, the refrigerator analysis unit can analyze fluctuations in the temperature inside the refrigerator and suggest an optimal storage temperature. The refrigerator analysis unit can also analyze the type and amount of food stored in the refrigerator and notify the user of food that is close to its expiration date. The refrigerator analysis unit can also analyze fluctuations in humidity inside the refrigerator and suggest an optimal storage humidity. This allows for more accurate recommendations by analyzing refrigerator data in detail.

[0031] The analysis unit may include a washing machine analysis unit that analyzes washing machine data in detail. The washing machine analysis unit analyzes, for example, the frequency of washing machine use and the type of detergent used in detail. For example, the washing machine analysis unit can analyze fluctuations in use frequency and propose an optimal washing schedule. The washing machine analysis unit can also analyze the type of detergent used and propose the optimal amount of detergent to use. The washing machine analysis unit can also analyze vibration data from the washing machine and detect abnormalities. This allows for more accurate recommendations by analyzing washing machine data in detail.

[0032] The analysis unit can include an air conditioner analysis unit that analyzes air conditioner data in detail. The air conditioner analysis unit analyzes, for example, the indoor temperature, humidity, and usage time of the air conditioner in detail. For example, the air conditioner analysis unit can analyze fluctuations in indoor temperature and suggest an optimal set temperature. The air conditioner analysis unit can also analyze usage time and suggest an optimal usage schedule. The air conditioner analysis unit can also analyze fluctuations in indoor humidity and suggest an optimal set humidity. This enables more accurate recommendations by analyzing air conditioner data in detail.

[0033] The recommendation unit may include a history consideration unit that performs analysis based on the user's past purchase history and browsing history. The history consideration unit, for example, analyzes the user's past purchase history and browsing history. For example, the history consideration unit can identify the user's preferences based on the past purchase history and recommend optimal products. The history consideration unit can also identify the user's interests based on the past browsing history and recommend related products. The history consideration unit can also analyze the user's purchasing patterns and recommend products at optimal times. This enables more personalized recommendations by taking the user's past purchase history and browsing history into consideration.

[0034] The recommendation unit includes an algorithm unit and can recommend products based on the analysis results. The algorithm unit, for example, recommends optimal products based on the analysis results. For example, the algorithm unit may recommend recipes, seasonings, and storage containers related to stored food based on refrigerator data. The algorithm unit may also recommend detergents, fabric softeners, and laundry nets to be used based on washing machine data. The algorithm unit may also recommend air purifiers, humidifiers, and dehumidifiers based on the room temperature and humidity based on air conditioner data. This makes it possible to provide optimal products to users by recommending optimal products based on the analysis results.

[0035] The collection unit monitors the operating status of IoT home appliances in real time and can strengthen data collection if an abnormality is detected. For example, if the temperature of a refrigerator suddenly rises, the collection unit collects detailed temperature data. The collection unit can also collect vibration data if an abnormal vibration is detected in a washing machine. The collection unit can also collect sound data if an air conditioner makes an abnormal sound. In this way, by strengthening data collection when an abnormality is detected, it is possible to quickly identify the problem and take measures.

[0036] The collection unit can dynamically adjust the frequency of data collection based on the frequency of use of the home appliance. For example, if the refrigerator is opened and closed frequently, the collection unit can increase the frequency of data collection. Also, if the washing machine is used infrequently, the collection unit can decrease the frequency of data collection. Also, if the frequency of use of the air conditioner varies depending on the season, the collection unit can adjust the frequency of data collection according to the season. In this way, by adjusting the frequency of data collection according to the frequency of use of the home appliance, efficient data collection is possible.

[0037] The collection unit can apply different data collection protocols for each type of home appliance to optimize collection efficiency. For example, the collection unit applies a temperature and humidity protocol to collect refrigerator data. The collection unit can also apply a vibration and water volume protocol to collect washing machine data. The collection unit can also apply a temperature and operating time protocol to collect air conditioner data. In this way, collection efficiency can be optimized by applying a protocol according to the type of home appliance.

[0038] The collection unit can prioritize collecting region-specific data by taking into account the geographical location information of the home appliances. For example, the collection unit can prioritize collecting refrigerator temperature data in cold regions. The collection unit can also prioritize collecting air conditioner humidity data in humid regions. The collection unit can also prioritize collecting washing machine usage frequency data in urban areas. In this way, by prioritizing the collection of region-specific data, it becomes possible to make recommendations that are appropriate for the region.

[0039] The collection unit can customize the type of data to be collected depending on the manufacturer and model of the home appliance. For example, the collection unit collects temperature and humidity data for refrigerators made by a specific manufacturer. The collection unit can also collect vibration and water volume data for washing machines made by a specific model. The collection unit can also collect temperature and operating time data for air conditioners made by a specific manufacturer. This makes it possible to collect data according to the manufacturer and model of the home appliance.

[0040] The collection unit can collect energy consumption data of home appliances and use it to improve energy efficiency. For example, the collection unit can collect energy consumption data of refrigerators and suggest efficient operating methods. The collection unit can also collect energy consumption data of washing machines and suggest efficient laundry methods. The collection unit can also collect energy consumption data of air conditioners and suggest efficient operating methods. In this way, collecting energy consumption data makes it possible to improve energy efficiency.

[0041] The analysis unit can analyze the usage patterns of home appliances over the long term and understand seasonal usage trends. For example, the analysis unit can analyze the usage patterns of a refrigerator and understand usage trends in summer and winter. The analysis unit can also analyze the usage patterns of a washing machine and understand the frequency of use by season. The analysis unit can also analyze the usage patterns of an air conditioner and understand the usage time by season. This allows for understanding seasonal usage trends, making it possible to make more appropriate recommendations.

[0042] The analysis unit strengthens the anomaly detection function of home appliances and can identify the cause when an anomaly occurs. For example, the analysis unit detects abnormal temperatures in a refrigerator and identifies the cause. The analysis unit can also detect abnormal vibrations in a washing machine and identify the cause. The analysis unit can also detect abnormal operation in an air conditioner and identify the cause. This allows for the cause to be identified when an anomaly occurs, allowing for quick countermeasures to be taken.

[0043] The analysis unit can analyze data from home appliances in cooperation with other smart home devices to grasp comprehensive lifestyle patterns. For example, the analysis unit can analyze lifestyle patterns by linking refrigerator data with smart lighting data. The analysis unit can also analyze lifestyle patterns by linking washing machine data with smart speaker data. The analysis unit can also analyze lifestyle patterns by linking air conditioner data with smart security data. In this way, by linking with other smart home devices, comprehensive lifestyle patterns can be grasped.

[0044] The analysis unit can take into account the user's lifestyle and health condition when analyzing home appliance data. For example, the analysis unit analyzes refrigerator data based on the user's lifestyle. The analysis unit can also analyze washing machine data based on the user's health condition. The analysis unit can also analyze air conditioner data based on the user's lifestyle. This allows for more personalized analysis by taking the user's lifestyle and health condition into consideration.

[0045] When analyzing data on home appliances, the analysis unit can improve the accuracy of the analysis by referring to regional climate data. For example, the analysis unit analyzes refrigerator data by referring to climate data for cold regions. The analysis unit can also analyze washing machine data by referring to climate data for humid regions. The analysis unit can also analyze air conditioner data by referring to climate data for hot regions. In this way, by referring to regional climate data, the accuracy of the analysis is improved.

[0046] The analysis unit can take into account the user's energy consumption pattern when analyzing the data of the home appliances. For example, the analysis unit analyzes refrigerator data based on the user's energy consumption pattern. The analysis unit can also analyze washing machine data based on the user's energy consumption pattern. The analysis unit can also analyze air conditioner data based on the user's energy consumption pattern. This makes it possible to improve energy efficiency by taking into account the user's energy consumption pattern.

[0047] When making a recommendation, the recommendation unit can check the inventory status of a product in real time and recommend only products that are in stock. For example, the recommendation unit can recommend food-related products that are in stock based on refrigerator data. The recommendation unit can also recommend detergents and fabric softeners that are in stock based on washing machine data. The recommendation unit can also recommend air purifiers and humidifiers that are in stock based on air conditioner data. This ensures that products that the user can purchase are provided by recommending only products that are in stock.

[0048] When making a recommendation, the recommendation unit can prioritize highly reliable products by taking into consideration product ratings and reviews. For example, the recommendation unit can recommend highly rated food-related products based on refrigerator data. The recommendation unit can also recommend detergents and fabric softeners with good reviews based on washing machine data. The recommendation unit can also recommend highly reliable air purifiers and humidifiers based on air conditioner data. This prioritizes the recommendation of highly reliable products, thereby improving user satisfaction.

[0049] When making a recommendation, the recommendation unit can recommend the latest products by combining the user's purchase history with current trends. For example, the recommendation unit can recommend the latest food-related products based on refrigerator data. The recommendation unit can also recommend the latest detergents and fabric softeners based on washing machine data. The recommendation unit can also recommend the latest air purifiers and humidifiers based on air conditioner data. This makes it possible to provide products that are attractive to users by recommending the latest products.

[0050] When making recommendations, the recommendation unit can recommend products that are specific to the region, taking into account the user's geographical location information. For example, the recommendation unit can recommend heating appliances and cold weather gear in cold regions. The recommendation unit can also recommend dehumidifiers and moisture-proof products in humid regions. The recommendation unit can also recommend compact home appliances and storage products in urban areas. This makes it possible to recommend products that are specific to the region and provide products that are appropriate for the user.

[0051] When making a recommendation, the recommendation unit can analyze the user's social media activity and recommend related products. For example, the recommendation unit recommends products related to places where the user has checked in on social media. The recommendation unit can also analyze the content of the user's posts on social media and recommend related products. The recommendation unit can also recommend related products by referring to the activities of the user's friends on social media. In this way, highly related products can be recommended by analyzing the user's social media activity.

[0052] The recommendation unit can customize the recommendation algorithm by reflecting the user's past feedback when making a recommendation. For example, the recommendation unit preferentially recommends products that the user has previously rated highly. The recommendation unit can also recommend related products based on products that the user has previously purchased. The recommendation unit can also analyze the user's past feedback and optimize the recommendation algorithm. This allows for more personalized recommendations by reflecting the user's past feedback.

[0053] The refrigerator analysis unit can analyze the types and amounts of food in the refrigerator and provide advice on nutritional balance. For example, if the amount of vegetables in the refrigerator is low, the refrigerator analysis unit can provide advice on increasing the amount of vegetables. Also, if the amount of meat in the refrigerator is high, the refrigerator analysis unit can suggest a balanced meal. Also, the refrigerator analysis unit can provide advice on nutritional balance based on the types of food in the refrigerator. In this way, by analyzing the types and amounts of food in the refrigerator, it is possible to suggest a nutritionally balanced meal.

[0054] The refrigerator analysis unit can analyze the temperature and humidity inside the refrigerator and suggest optimal storage conditions. For example, if the temperature inside the refrigerator is high, the refrigerator analysis unit can suggest lowering the temperature. Also, if the humidity inside the refrigerator is low, the refrigerator analysis unit can suggest increasing the humidity. Also, the refrigerator analysis unit can suggest optimal storage conditions based on the temperature and humidity inside the refrigerator. In this way, by analyzing the temperature and humidity inside the refrigerator, it is possible to optimize the storage conditions of food.

[0055] The refrigerator analysis unit can analyze the consumption pattern of food in the refrigerator and make suggestions to reduce waste. For example, the refrigerator analysis unit can analyze the consumption pattern of food in the refrigerator and make suggestions to reduce waste. The refrigerator analysis unit can also suggest an efficient shopping list based on the consumption pattern of food in the refrigerator. The refrigerator analysis unit can also analyze the consumption pattern of food in the refrigerator and provide advice to reduce waste. In this way, by analyzing the food consumption pattern, suggestions to reduce waste are possible.

[0056] The refrigerator analysis unit can analyze the type and amount of food in the refrigerator and suggest recipes. For example, the refrigerator analysis unit can suggest recipes using vegetables based on the type and amount of vegetables in the refrigerator. The refrigerator analysis unit can also suggest meat recipes based on the type and amount of meat in the refrigerator. The refrigerator analysis unit can also suggest balanced recipes based on the type and amount of food in the refrigerator. In this way, appropriate recipes can be suggested by analyzing the type and amount of food in the refrigerator.

[0057] The refrigerator analysis unit can analyze the temperature and humidity inside the refrigerator and suggest improvements to energy efficiency. For example, if the temperature inside the refrigerator is high, the refrigerator analysis unit makes suggestions to improve energy efficiency. Also, if the humidity inside the refrigerator is low, the refrigerator analysis unit can make suggestions to improve energy efficiency. Also, the refrigerator analysis unit can make suggestions to improve energy efficiency based on the temperature and humidity inside the refrigerator. In this way, energy efficiency can be improved by analyzing the temperature and humidity inside the refrigerator.

[0058] The refrigerator analysis unit can analyze the consumption pattern of food in the refrigerator and automatically generate a shopping list. The refrigerator analysis unit can, for example, analyze the consumption pattern of food in the refrigerator and automatically generate an efficient shopping list. The refrigerator analysis unit can also list necessary foods based on the consumption pattern of food in the refrigerator. The refrigerator analysis unit can also analyze the consumption pattern of food in the refrigerator and automatically generate a shopping list to reduce waste. In this way, an efficient shopping list can be automatically generated by analyzing food consumption patterns.

[0059] The washing machine analysis unit can analyze how often the washing machine is used and notify the user of the optimal maintenance time. For example, if the washing machine is used frequently, the washing machine analysis unit can suggest earlier maintenance. If the washing machine is used infrequently, the washing machine analysis unit can also suggest a regular maintenance time. The washing machine analysis unit can also notify the user of the optimal maintenance time based on how often the washing machine is used. In this way, by analyzing how often the washing machine is used, the user can be notified of the optimal maintenance time.

[0060] The washing machine analysis unit can analyze the usage pattern of the washing machine and suggest the optimal amount of detergent to use. The washing machine analysis unit can, for example, suggest the optimal amount of detergent to use based on the usage pattern of the washing machine. The washing machine analysis unit can also adjust the amount of detergent to use based on the frequency of use of the washing machine. The washing machine analysis unit can also analyze the usage pattern of the washing machine and suggest an efficient amount of detergent to use. In this way, by analyzing the usage pattern of the washing machine, the optimal amount of detergent to use can be suggested.

[0061] The washing machine analysis unit can analyze washing machine data and propose a washing program according to the type of laundry. For example, the washing machine analysis unit can propose a washing program suitable for delicate clothes based on the washing machine data. The washing machine analysis unit can also propose a washing program suitable for heavily soiled clothes based on the washing machine data. The washing machine analysis unit can also propose a washing program suitable for normal clothes based on the washing machine data. In this way, by analyzing the washing machine data, it is possible to propose the optimal washing program according to the type of laundry.

[0062] The washing machine analysis unit can analyze the frequency of use of the washing machine and suggest improvements to energy efficiency. For example, if the washing machine is used frequently, the washing machine analysis unit makes suggestions to improve energy efficiency. Also, if the washing machine is used infrequently, the washing machine analysis unit can make suggestions to maintain normal energy efficiency. Also, the washing machine analysis unit can suggest improvements to energy efficiency based on the frequency of use of the washing machine. In this way, energy efficiency can be improved by analyzing the frequency of use of the washing machine.

[0063] The washing machine analysis unit can analyze the usage pattern of the washing machine and suggest a laundry drying method. The washing machine analysis unit can, for example, suggest an efficient drying method based on the usage pattern of the washing machine. The washing machine analysis unit can also adjust the drying method based on the frequency of use of the washing machine. The washing machine analysis unit can also analyze the usage pattern of the washing machine and suggest an optimal drying method. In this way, the optimal drying method can be suggested by analyzing the usage pattern of the washing machine.

[0064] The washing machine analysis unit can analyze washing machine data and suggest laundry storage methods. For example, the washing machine analysis unit can suggest storage methods for delicate clothes based on the washing machine data. The washing machine analysis unit can also suggest storage methods for heavily soiled clothes based on the washing machine data. The washing machine analysis unit can also suggest storage methods for regular clothes based on the washing machine data. In this way, the optimal storage method can be suggested by analyzing the washing machine data.

[0065] The air conditioner analysis unit can analyze the usage pattern of the air conditioner and notify the optimal filter replacement time. The air conditioner analysis unit can, for example, suggest the filter replacement time based on the usage pattern of the air conditioner. The air conditioner analysis unit can also adjust the filter replacement time based on the frequency of use of the air conditioner. The air conditioner analysis unit can also analyze the usage pattern of the air conditioner and notify the optimal filter replacement time. In this way, by analyzing the usage pattern of the air conditioner, it is possible to notify the optimal filter replacement time.

[0066] The air conditioner analysis unit can analyze the air conditioner data and make suggestions to improve indoor air quality. For example, the air conditioner analysis unit can suggest the use of an air purifier based on the air conditioner data. The air conditioner analysis unit can also suggest the use of a humidifier based on the air conditioner data. The air conditioner analysis unit can also suggest the use of a dehumidifier based on the air conditioner data. In this way, by analyzing the air conditioner data, suggestions to improve indoor air quality can be made.

[0067] The air conditioner analysis unit can analyze the frequency of air conditioner use and propose optimization of energy consumption. For example, if the air conditioner is used frequently, the air conditioner analysis unit makes a proposal to optimize energy consumption. Furthermore, if the air conditioner is used infrequently, the air conditioner analysis unit can also make a proposal to maintain normal energy consumption. Furthermore, the air conditioner analysis unit can also propose optimization of energy consumption based on the frequency of air conditioner use. In this way, energy consumption can be optimized by analyzing the frequency of air conditioner use.

[0068] The air conditioner analysis unit can analyze the usage pattern of the air conditioner and propose an optimal operation schedule. The air conditioner analysis unit can propose an efficient operation schedule based on, for example, the usage pattern of the air conditioner. The air conditioner analysis unit can also adjust the operation schedule based on the frequency of use of the air conditioner. The air conditioner analysis unit can also analyze the usage pattern of the air conditioner and propose an optimal operation schedule. In this way, by analyzing the usage pattern of the air conditioner, an optimal operation schedule can be proposed.

[0069] The air conditioner analysis unit can analyze the air conditioner data and suggest indoor humidity control. For example, the air conditioner analysis unit can suggest the use of a humidifier based on the air conditioner data. The air conditioner analysis unit can also suggest the use of a dehumidifier based on the air conditioner data. The air conditioner analysis unit can also suggest optimal humidity control based on the air conditioner data. In this way, optimal humidity control can be suggested by analyzing the air conditioner data.

[0070] The air conditioner analysis unit can analyze the frequency of air conditioner use and suggest improvements to energy efficiency. For example, if the air conditioner is used frequently, the air conditioner analysis unit makes suggestions to improve energy efficiency. Furthermore, if the air conditioner is used infrequently, the air conditioner analysis unit can also make suggestions to maintain normal energy efficiency. Furthermore, the air conditioner analysis unit can also suggest improvements to energy efficiency based on the frequency of air conditioner use. In this way, energy efficiency can be improved by analyzing the frequency of air conditioner use.

[0071] The history consideration unit can analyze the past purchase history and identify the user's purchasing pattern. For example, the history consideration unit can analyze the past purchase history and identify the user's purchasing pattern. The history consideration unit can also identify the user's preferences based on the past purchase history. The history consideration unit can also analyze the past purchase history and identify the user's purchasing behavior. In this way, the user's purchasing pattern can be identified by analyzing the past purchase history.

[0072] The history consideration unit can analyze past purchase history and suggest products based on the user's preferences. The history consideration unit can, for example, suggest products that match the user's preferences based on the past purchase history. The history consideration unit can also analyze past purchase history and suggest products that match the user's preferences. The history consideration unit can also suggest products based on the user's preferences based on the past purchase history. In this way, by analyzing the past purchase history, it is possible to suggest products based on the user's preferences.

[0073] The history consideration unit can analyze past purchase history and grasp changes in the user's purchasing behavior. The history consideration unit, for example, analyzes past purchase history and grasps changes in the user's purchasing behavior. The history consideration unit can also grasp trends in the user's purchasing behavior based on the past purchase history. The history consideration unit can also analyze the past purchase history and identify changes in the user's purchasing behavior. In this way, changes in the user's purchasing behavior can be grasped by analyzing the past purchase history.

[0074] The history consideration unit can analyze past purchase history and suggest products based on the user's lifestyle. The history consideration unit can, for example, suggest products that suit the user's lifestyle based on the past purchase history. The history consideration unit can also analyze past purchase history and suggest products that suit the user's lifestyle. The history consideration unit can also suggest products that suit the user's lifestyle based on the past purchase history. In this way, by analyzing the past purchase history, it is possible to suggest products that suit the user's lifestyle.

[0075] The history consideration unit can analyze past purchase history and suggest products based on the user's purchase frequency. The history consideration unit can, for example, suggest products that match the user's purchase frequency based on the past purchase history. The history consideration unit can also analyze past purchase history and suggest products based on the user's purchase frequency. The history consideration unit can also suggest products that match the user's purchase frequency based on the past purchase history. In this way, by analyzing the past purchase history, it is possible to suggest products that match the user's purchase frequency.

[0076] The history consideration unit can analyze past purchase histories and grasp trends in user purchasing behavior. The history consideration unit, for example, analyzes past purchase histories and grasps trends in user purchasing behavior. The history consideration unit can also grasp changes in user purchasing behavior based on past purchase histories. The history consideration unit can also analyze past purchase histories and identify trends in user purchasing behavior. In this way, trends in user purchasing behavior can be grasped by analyzing past purchase histories.

[0077] The algorithm unit can analyze the recommendation algorithm and automatically adjust the optimal parameters. For example, the algorithm unit can analyze the recommendation algorithm and automatically adjust the optimal parameters. The algorithm unit can also analyze the parameters of the recommendation algorithm and automatically adjust the optimal settings. The algorithm unit can also analyze the recommendation algorithm and automatically adjust the optimal parameters. In this way, the optimal parameters can be automatically adjusted by analyzing the recommendation algorithm.

[0078] The algorithm unit can analyze the recommendation algorithm and preferentially recommend products based on the user's preferences. The algorithm unit, for example, analyzes the recommendation algorithm and preferentially recommends products based on the user's preferences. The algorithm unit can also preferentially recommend products that match the user's preferences based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and preferentially recommend products based on the user's preferences. In this way, by analyzing the recommendation algorithm, it is possible to preferentially recommend products based on the user's preferences.

[0079] The algorithm unit can analyze the recommendation algorithm and recommend products based on the user's purchasing behavior. The algorithm unit, for example, analyzes the recommendation algorithm and recommends products based on the user's purchasing behavior. The algorithm unit can also recommend products that match the user's purchasing behavior based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and recommend products based on the user's purchasing behavior. In this way, by analyzing the recommendation algorithm, it is possible to recommend products based on the user's purchasing behavior.

[0080] The algorithm unit can analyze the recommendation algorithm and recommend products based on the user's lifestyle. The algorithm unit, for example, analyzes the recommendation algorithm and recommends products that suit the user's lifestyle. The algorithm unit can also recommend products that suit the user's lifestyle based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and recommend products based on the user's lifestyle. In this way, by analyzing the recommendation algorithm, it is possible to recommend products that suit the user's lifestyle.

[0081] The algorithm unit can analyze the recommendation algorithm and recommend products based on the user's purchasing frequency. The algorithm unit, for example, analyzes the recommendation algorithm and recommends products that match the user's purchasing frequency. The algorithm unit can also recommend products that match the user's purchasing frequency based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and recommend products based on the user's purchasing frequency. In this way, by analyzing the recommendation algorithm, it is possible to recommend products that match the user's purchasing frequency.

[0082] The algorithm unit can analyze the recommendation algorithm and grasp trends in user purchasing behavior. The algorithm unit, for example, analyzes the recommendation algorithm and grasps trends in user purchasing behavior. The algorithm unit can also grasp trends in user purchasing behavior based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and grasp trends in user purchasing behavior. In this way, trends in user purchasing behavior can be grasped by analyzing the recommendation algorithm.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The analysis unit can also collect the user's health data and recommend health-related products based on the analysis results. For example, it can analyze the user's exercise data and recommend fitness equipment or health foods if the user is not getting enough exercise. It can also analyze the user's sleep data and recommend sleep aids or supplements if the user's sleep quality is poor. It can also analyze the user's dietary data and recommend nutritional supplements or recipes if the user's nutritional balance is unbalanced. This allows for more personalized recommendations by providing products that match the user's health condition.

[0085] The recommendation unit can analyze the user's social media activity and recommend related products. For example, it can recommend products related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and recommend related products. It can also recommend related products based on the activities of the user's friends on social media. In this way, highly relevant products can be recommended by analyzing the user's social media activity.

[0086] The collection unit can collect energy consumption data of home appliances and use it to improve energy efficiency. For example, it can collect energy consumption data of refrigerators and suggest efficient operating methods. It can also collect energy consumption data of washing machines and suggest efficient laundry methods. It can also collect energy consumption data of air conditioners and suggest efficient operating methods. In this way, collecting energy consumption data makes it possible to improve energy efficiency.

[0087] The analysis unit can customize the recommended products taking into account the user's lifestyle and health condition. For example, if the user is health-conscious, health foods and fitness equipment can be recommended. If the user has a busy lifestyle, time-saving home appliances and convenient gadgets can be recommended. Furthermore, if the user is looking to relax, relaxation goods and aroma products can be recommended. This allows for more personalized recommendations by providing products that suit the user's lifestyle and health condition.

[0088] The analysis unit can analyze data from home appliances in conjunction with other smart home devices to understand comprehensive lifestyle patterns. For example, refrigerator data can be linked with smart lighting data to analyze lifestyle patterns. It can also link washing machine data with smart speaker data to analyze lifestyle patterns. It can also link air conditioner data with smart security data to analyze lifestyle patterns. By linking with other smart home devices, comprehensive lifestyle patterns can be understood.

[0089] When making a recommendation, the recommendation unit checks product inventory status in real time and can recommend only products that are in stock. For example, it can recommend food-related products that are in stock based on refrigerator data. It can also recommend detergents and fabric softeners that are in stock based on washing machine data. It can also recommend air purifiers and humidifiers that are in stock based on air conditioner data. This ensures that products that users can purchase are provided by recommending only products that are in stock.

[0090] The processing flow of the first embodiment will be briefly explained below.

[0091] Step 1: The collection unit collects data from IoT home appliances. For example, the collection unit collects the temperature and humidity inside a refrigerator, as well as the type and amount of food stored inside. The collection unit can also collect how often a washing machine is used and the type of detergent used. The collection unit can also collect the indoor temperature, humidity, and usage time of an air conditioner. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes refrigerator data to analyze the temperature and humidity inside the refrigerator, and the type and amount of food stored therein. The analysis unit can also analyze washing machine data to analyze the frequency of laundry and the type of detergent used. Furthermore, the analysis unit can analyze air conditioner data to analyze the indoor temperature, humidity, and usage time. Step 3: The recommendation unit recommends products based on the analysis results obtained by the analysis unit. For example, the recommendation unit may use refrigerator data to recommend recipes, seasonings, and storage containers related to the food stored in the refrigerator. The recommendation unit may also use washing machine data to recommend detergents, fabric softeners, and laundry nets to use. Furthermore, the recommendation unit may use air conditioner data to recommend air purifiers, humidifiers, and dehumidifiers based on the room temperature and humidity.

[0092] (Example 2) A recommendation function service according to an embodiment of the present invention is a system that collects and analyzes data on IoT home appliances and recommends products. The recommendation function service recommends products on e-commerce sites by allowing users to select IoT home appliances and configure their connection settings. For example, a user selects IoT home appliances such as a refrigerator, washing machine, or air conditioner and configures their connection settings. The IoT home appliance data is then sent to a cloud server, which analyzes the received data. The analyzed data is then sent to the e-commerce site's recommendation engine, which then recommends optimal products for the user. For example, refrigerator data can be used to recommend recipes, seasonings, and storage containers related to stored food. Washing machine data can be used to recommend detergents, fabric softeners, and laundry nets. Air conditioner data can be used to recommend air purifiers, humidifiers, dehumidifiers, and other appliances based on the room temperature and humidity. This allows the recommendation function service to utilize users' IoT home appliance data to make e-commerce site shopping more convenient and enjoyable. Furthermore, e-commerce operators can effectively recommend products tailored to users' needs, potentially increasing their sales.

[0093] A recommendation function service according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects data from IoT home appliances. For example, the collection unit collects data on the temperature and humidity inside a refrigerator, and the types and amounts of food stored therein. The collection unit can also collect data on the frequency of washing machine use and the type of detergent used. The collection unit can also collect data on the indoor temperature, humidity, and usage time of an air conditioner. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes data from the refrigerator to analyze the temperature and humidity inside the refrigerator, and the types and amounts of food stored therein. The analysis unit can also analyze data from the washing machine to analyze the frequency of laundry and the type of detergent used. The analysis unit can also analyze data from the air conditioner to analyze the indoor temperature, humidity, and usage time. The recommendation unit recommends products based on the analysis results obtained by the analysis unit. For example, the recommendation unit recommends recipes, seasonings, and storage containers related to the food stored therein based on the refrigerator data. The recommendation unit can also recommend detergent, fabric softener, and laundry net to be used based on washing machine data. Furthermore, the recommendation unit can also recommend air purifiers, humidifiers, and dehumidifiers based on indoor temperature and humidity based on air conditioner data. This allows the recommendation function service according to the embodiment to provide users with the most suitable products.

[0094] The analysis unit can include a refrigerator analysis unit that analyzes refrigerator data in detail. The refrigerator analysis unit analyzes, for example, the temperature and humidity inside the refrigerator, and the type and amount of food stored in the refrigerator in detail. For example, the refrigerator analysis unit can analyze fluctuations in the temperature inside the refrigerator and suggest an optimal storage temperature. The refrigerator analysis unit can also analyze the type and amount of food stored in the refrigerator and notify the user of food that is close to its expiration date. The refrigerator analysis unit can also analyze fluctuations in humidity inside the refrigerator and suggest an optimal storage humidity. This allows for more accurate recommendations by analyzing refrigerator data in detail.

[0095] The analysis unit may include a washing machine analysis unit that analyzes washing machine data in detail. The washing machine analysis unit analyzes, for example, the frequency of washing machine use and the type of detergent used in detail. For example, the washing machine analysis unit can analyze fluctuations in use frequency and propose an optimal washing schedule. The washing machine analysis unit can also analyze the type of detergent used and propose the optimal amount of detergent to use. The washing machine analysis unit can also analyze vibration data from the washing machine and detect abnormalities. This allows for more accurate recommendations by analyzing washing machine data in detail.

[0096] The analysis unit can include an air conditioner analysis unit that analyzes air conditioner data in detail. The air conditioner analysis unit analyzes, for example, the indoor temperature, humidity, and usage time of the air conditioner in detail. For example, the air conditioner analysis unit can analyze fluctuations in indoor temperature and suggest an optimal set temperature. The air conditioner analysis unit can also analyze usage time and suggest an optimal usage schedule. The air conditioner analysis unit can also analyze fluctuations in indoor humidity and suggest an optimal set humidity. This enables more accurate recommendations by analyzing air conditioner data in detail.

[0097] The recommendation unit may include a history consideration unit that performs analysis based on the user's past purchase history and browsing history. The history consideration unit, for example, analyzes the user's past purchase history and browsing history. For example, the history consideration unit can identify the user's preferences based on the past purchase history and recommend optimal products. The history consideration unit can also identify the user's interests based on the past browsing history and recommend related products. The history consideration unit can also analyze the user's purchasing patterns and recommend products at optimal times. This enables more personalized recommendations by taking the user's past purchase history and browsing history into consideration.

[0098] The recommendation unit includes an algorithm unit and can recommend products based on the analysis results. The algorithm unit, for example, recommends optimal products based on the analysis results. For example, the algorithm unit may recommend recipes, seasonings, and storage containers related to stored food based on refrigerator data. The algorithm unit may also recommend detergents, fabric softeners, and laundry nets to be used based on washing machine data. The algorithm unit may also recommend air purifiers, humidifiers, and dehumidifiers based on the room temperature and humidity based on air conditioner data. This makes it possible to provide optimal products to users by recommending optimal products based on the analysis results.

[0099] The collection unit can estimate the user's emotions and adjust the timing of collecting IoT home appliance data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the collection timing to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to quickly collect the minimum amount of data necessary. This allows the data collection timing to be adjusted according to the user's emotions, reducing the user's burden and collecting detailed data. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0100] The collection unit monitors the operating status of IoT home appliances in real time and can strengthen data collection if an abnormality is detected. For example, if the temperature of a refrigerator suddenly rises, the collection unit collects detailed temperature data. The collection unit can also collect vibration data if an abnormal vibration is detected in a washing machine. The collection unit can also collect sound data if an air conditioner makes an abnormal sound. In this way, by strengthening data collection when an abnormality is detected, it is possible to quickly identify the problem and take measures.

[0101] The collection unit can dynamically adjust the frequency of data collection based on the frequency of use of the home appliance. For example, if the refrigerator is opened and closed frequently, the collection unit can increase the frequency of data collection. Also, if the washing machine is used infrequently, the collection unit can decrease the frequency of data collection. Also, if the frequency of use of the air conditioner varies depending on the season, the collection unit can adjust the frequency of data collection according to the season. In this way, by adjusting the frequency of data collection according to the frequency of use of the home appliance, efficient data collection is possible.

[0102] The collection unit can apply different data collection protocols for each type of home appliance to optimize collection efficiency. For example, the collection unit applies a temperature and humidity protocol to collect refrigerator data. The collection unit can also apply a vibration and water volume protocol to collect washing machine data. The collection unit can also apply a temperature and operating time protocol to collect air conditioner data. In this way, collection efficiency can be optimized by applying a protocol according to the type of home appliance.

[0103] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only 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 also prioritize collecting data that can be collected quickly. In this way, 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 using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0104] The collection unit can prioritize collecting region-specific data by taking into account the geographical location information of the home appliances. For example, the collection unit can prioritize collecting refrigerator temperature data in cold regions. The collection unit can also prioritize collecting air conditioner humidity data in humid regions. The collection unit can also prioritize collecting washing machine usage frequency data in urban areas. In this way, by prioritizing the collection of region-specific data, it becomes possible to make recommendations that are appropriate for the region.

[0105] The collection unit can customize the type of data to be collected depending on the manufacturer and model of the home appliance. For example, the collection unit collects temperature and humidity data for refrigerators made by a specific manufacturer. The collection unit can also collect vibration and water volume data for washing machines made by a specific model. The collection unit can also collect temperature and operating time data for air conditioners made by a specific manufacturer. This makes it possible to collect data according to the manufacturer and model of the home appliance.

[0106] The collection unit can collect energy consumption data of home appliances and use it to improve energy efficiency. For example, the collection unit can collect energy consumption data of refrigerators and suggest efficient operating methods. The collection unit can also collect energy consumption data of washing machines and suggest efficient laundry methods. The collection unit can also collect energy consumption data of air conditioners and suggest efficient operating methods. In this way, collecting energy consumption data makes it possible to improve energy efficiency.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] The analysis unit can analyze the usage patterns of home appliances over the long term and understand seasonal usage trends. For example, the analysis unit can analyze the usage patterns of a refrigerator and understand usage trends in summer and winter. The analysis unit can also analyze the usage patterns of a washing machine and understand the frequency of use by season. The analysis unit can also analyze the usage patterns of an air conditioner and understand the usage time by season. This allows for understanding seasonal usage trends, making it possible to make more appropriate recommendations.

[0109] The analysis unit strengthens the anomaly detection function of home appliances and can identify the cause when an anomaly occurs. For example, the analysis unit detects abnormal temperatures in a refrigerator and identifies the cause. The analysis unit can also detect abnormal vibrations in a washing machine and identify the cause. The analysis unit can also detect abnormal operation in an air conditioner and identify the cause. This allows for the cause to be identified when an anomaly occurs, allowing for quick countermeasures to be taken.

[0110] The analysis unit can analyze data from home appliances in cooperation with other smart home devices to grasp comprehensive lifestyle patterns. For example, the analysis unit can analyze lifestyle patterns by linking refrigerator data with smart lighting data. The analysis unit can also analyze lifestyle patterns by linking washing machine data with smart speaker data. The analysis unit can also analyze lifestyle patterns by linking air conditioner data with smart security data. In this way, by linking with other smart home devices, comprehensive lifestyle patterns can be grasped.

[0111] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can display only important analysis results. Furthermore, if the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display analysis results that focus on the main points. In this way, by adjusting the importance of the analysis results according to the user's emotions, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] The analysis unit can take into account the user's lifestyle and health condition when analyzing home appliance data. For example, the analysis unit analyzes refrigerator data based on the user's lifestyle. The analysis unit can also analyze washing machine data based on the user's health condition. The analysis unit can also analyze air conditioner data based on the user's lifestyle. This allows for more personalized analysis by taking the user's lifestyle and health condition into consideration.

[0113] When analyzing data on home appliances, the analysis unit can improve the accuracy of the analysis by referring to regional climate data. For example, the analysis unit analyzes refrigerator data by referring to climate data for cold regions. The analysis unit can also analyze washing machine data by referring to climate data for humid regions. The analysis unit can also analyze air conditioner data by referring to climate data for hot regions. In this way, by referring to regional climate data, the accuracy of the analysis is improved.

[0114] The analysis unit can take into account the user's energy consumption pattern when analyzing the data of the home appliances. For example, the analysis unit analyzes refrigerator data based on the user's energy consumption pattern. The analysis unit can also analyze washing machine data based on the user's energy consumption pattern. The analysis unit can also analyze air conditioner data based on the user's energy consumption pattern. This makes it possible to improve energy efficiency by taking into account the user's energy consumption pattern.

[0115] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is nervous, the recommendation unit can provide simple, highly visible recommendations. If the user is relaxed, the recommendation unit can also provide recommendations that include detailed information. If the user is in a hurry, the recommendation unit can also provide recommendations that focus on the main points. This allows the recommendation presentation method to be adjusted according to the user's emotions, making it possible to provide recommendations that are easy for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0116] When making a recommendation, the recommendation unit can check the inventory status of a product in real time and recommend only products that are in stock. For example, the recommendation unit can recommend food-related products that are in stock based on refrigerator data. The recommendation unit can also recommend detergents and fabric softeners that are in stock based on washing machine data. The recommendation unit can also recommend air purifiers and humidifiers that are in stock based on air conditioner data. This ensures that products that the user can purchase are provided by recommending only products that are in stock.

[0117] When making a recommendation, the recommendation unit can prioritize highly reliable products by taking into consideration product ratings and reviews. For example, the recommendation unit can recommend highly rated food-related products based on refrigerator data. The recommendation unit can also recommend detergents and fabric softeners with good reviews based on washing machine data. The recommendation unit can also recommend highly reliable air purifiers and humidifiers based on air conditioner data. This prioritizes the recommendation of highly reliable products, thereby improving user satisfaction.

[0118] When making a recommendation, the recommendation unit can recommend the latest products by combining the user's purchase history with current trends. For example, the recommendation unit can recommend the latest food-related products based on refrigerator data. The recommendation unit can also recommend the latest detergents and fabric softeners based on washing machine data. The recommendation unit can also recommend the latest air purifiers and humidifiers based on air conditioner data. This makes it possible to provide products that are attractive to users by recommending the latest products.

[0119] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated user emotions. For example, if the user is feeling stressed, the recommendation unit can prioritize recommending important products. Furthermore, if the user is relaxed, the recommendation unit can provide recommendations including detailed product information. Furthermore, if the user is in a hurry, the recommendation unit can prioritize recommending products that can be purchased quickly. In this way, by determining the priority of recommendations according to the user's emotions, important products can be prioritized. Emotion estimation is realized using an emotion estimation function using, 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.

[0120] When making recommendations, the recommendation unit can recommend products that are specific to the region, taking into account the user's geographical location information. For example, the recommendation unit can recommend heating appliances and cold weather gear in cold regions. The recommendation unit can also recommend dehumidifiers and moisture-proof products in humid regions. The recommendation unit can also recommend compact home appliances and storage products in urban areas. This makes it possible to recommend products that are specific to the region and provide products that are appropriate for the user.

[0121] When making a recommendation, the recommendation unit can analyze the user's social media activity and recommend related products. For example, the recommendation unit recommends products related to places where the user has checked in on social media. The recommendation unit can also analyze the content of the user's posts on social media and recommend related products. The recommendation unit can also recommend related products by referring to the activities of the user's friends on social media. In this way, highly related products can be recommended by analyzing the user's social media activity.

[0122] The recommendation unit can customize the recommendation algorithm by reflecting the user's past feedback when making a recommendation. For example, the recommendation unit preferentially recommends products that the user has previously rated highly. The recommendation unit can also recommend related products based on products that the user has previously purchased. The recommendation unit can also analyze the user's past feedback and optimize the recommendation algorithm. This allows for more personalized recommendations by reflecting the user's past feedback.

[0123] The refrigerator analysis unit can estimate the user's emotions and notify the user of the expiration dates of food items in the refrigerator based on the estimated user emotions. For example, if the user is feeling stressed, the refrigerator analysis unit can prioritize notifying the user of food items that are close to their expiration date. The refrigerator analysis unit can also notify the user of detailed expiration date information if the user is relaxed. The refrigerator analysis unit can also notify the user of key expiration date information if the user is in a hurry. This can reduce food waste by notifying the user of food expiration dates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0124] The refrigerator analysis unit can analyze the types and amounts of food in the refrigerator and provide advice on nutritional balance. For example, if the amount of vegetables in the refrigerator is low, the refrigerator analysis unit can provide advice on increasing the amount of vegetables. Also, if the amount of meat in the refrigerator is high, the refrigerator analysis unit can suggest a balanced meal. Also, the refrigerator analysis unit can provide advice on nutritional balance based on the types of food in the refrigerator. In this way, by analyzing the types and amounts of food in the refrigerator, it is possible to suggest a nutritionally balanced meal.

[0125] The refrigerator analysis unit can analyze the temperature and humidity inside the refrigerator and suggest optimal storage conditions. For example, if the temperature inside the refrigerator is high, the refrigerator analysis unit can suggest lowering the temperature. Also, if the humidity inside the refrigerator is low, the refrigerator analysis unit can suggest increasing the humidity. Also, the refrigerator analysis unit can suggest optimal storage conditions based on the temperature and humidity inside the refrigerator. In this way, by analyzing the temperature and humidity inside the refrigerator, it is possible to optimize the storage conditions of food.

[0126] The refrigerator analysis unit can analyze the consumption pattern of food in the refrigerator and make suggestions to reduce waste. For example, the refrigerator analysis unit can analyze the consumption pattern of food in the refrigerator and make suggestions to reduce waste. The refrigerator analysis unit can also suggest an efficient shopping list based on the consumption pattern of food in the refrigerator. The refrigerator analysis unit can also analyze the consumption pattern of food in the refrigerator and provide advice to reduce waste. In this way, by analyzing the food consumption pattern, suggestions to reduce waste are possible.

[0127] The refrigerator analysis unit can estimate the user's emotions and suggest food placement in the refrigerator based on the estimated user's emotions. For example, if the user is feeling stressed, the refrigerator analysis unit can suggest a simple food placement. If the user is relaxed, the refrigerator analysis unit can also suggest a detailed food placement. If the user is in a hurry, the refrigerator analysis unit can also suggest a food placement that focuses on the main points. This makes it easier to organize the refrigerator by suggesting food placement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] The refrigerator analysis unit can analyze the type and amount of food in the refrigerator and suggest recipes. For example, the refrigerator analysis unit can suggest recipes using vegetables based on the type and amount of vegetables in the refrigerator. The refrigerator analysis unit can also suggest meat recipes based on the type and amount of meat in the refrigerator. The refrigerator analysis unit can also suggest balanced recipes based on the type and amount of food in the refrigerator. In this way, appropriate recipes can be suggested by analyzing the type and amount of food in the refrigerator.

[0129] The refrigerator analysis unit can analyze the temperature and humidity inside the refrigerator and suggest improvements to energy efficiency. For example, if the temperature inside the refrigerator is high, the refrigerator analysis unit makes suggestions to improve energy efficiency. Also, if the humidity inside the refrigerator is low, the refrigerator analysis unit can make suggestions to improve energy efficiency. Also, the refrigerator analysis unit can make suggestions to improve energy efficiency based on the temperature and humidity inside the refrigerator. In this way, energy efficiency can be improved by analyzing the temperature and humidity inside the refrigerator.

[0130] The refrigerator analysis unit can analyze the consumption pattern of food in the refrigerator and automatically generate a shopping list. The refrigerator analysis unit can, for example, analyze the consumption pattern of food in the refrigerator and automatically generate an efficient shopping list. The refrigerator analysis unit can also list necessary foods based on the consumption pattern of food in the refrigerator. The refrigerator analysis unit can also analyze the consumption pattern of food in the refrigerator and automatically generate a shopping list to reduce waste. In this way, an efficient shopping list can be automatically generated by analyzing food consumption patterns.

[0131] The washing machine analysis unit can estimate the user's emotions and suggest the timing of laundry based on the estimated user emotions. For example, if the user is feeling stressed, the washing machine analysis unit can suggest reducing the frequency of laundry. Furthermore, if the user is relaxed, the washing machine analysis unit can also suggest increasing the frequency of laundry. Furthermore, if the user is in a hurry, the washing machine analysis unit can also suggest an efficient timing of laundry. In this way, the burden on the user can be reduced by suggesting the timing of laundry according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The washing machine analysis unit can analyze how often the washing machine is used and notify the user of the optimal maintenance time. For example, if the washing machine is used frequently, the washing machine analysis unit can suggest earlier maintenance. If the washing machine is used infrequently, the washing machine analysis unit can also suggest a regular maintenance time. The washing machine analysis unit can also notify the user of the optimal maintenance time based on how often the washing machine is used. In this way, by analyzing how often the washing machine is used, the user can be notified of the optimal maintenance time.

[0133] The washing machine analysis unit can analyze the usage pattern of the washing machine and suggest the optimal amount of detergent to use. The washing machine analysis unit can, for example, suggest the optimal amount of detergent to use based on the usage pattern of the washing machine. The washing machine analysis unit can also adjust the amount of detergent to use based on the frequency of use of the washing machine. The washing machine analysis unit can also analyze the usage pattern of the washing machine and suggest an efficient amount of detergent to use. In this way, by analyzing the usage pattern of the washing machine, the optimal amount of detergent to use can be suggested.

[0134] The washing machine analysis unit can analyze washing machine data and propose a washing program according to the type of laundry. For example, the washing machine analysis unit can propose a washing program suitable for delicate clothes based on the washing machine data. The washing machine analysis unit can also propose a washing program suitable for heavily soiled clothes based on the washing machine data. The washing machine analysis unit can also propose a washing program suitable for normal clothes based on the washing machine data. In this way, by analyzing the washing machine data, it is possible to propose the optimal washing program according to the type of laundry.

[0135] The washing machine analysis unit can estimate the user's emotions and suggest how to use the washing machine based on the estimated user's emotions. For example, if the user is feeling stressed, the washing machine analysis unit can suggest a simple usage method. If the user is relaxed, the washing machine analysis unit can also suggest a detailed usage method. If the user is in a hurry, the washing machine analysis unit can also suggest an efficient usage method. This reduces the burden on the user by suggesting how to use the washing machine according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0136] The washing machine analysis unit can analyze the frequency of use of the washing machine and suggest improvements to energy efficiency. For example, if the washing machine is used frequently, the washing machine analysis unit makes suggestions to improve energy efficiency. Also, if the washing machine is used infrequently, the washing machine analysis unit can make suggestions to maintain normal energy efficiency. Also, the washing machine analysis unit can suggest improvements to energy efficiency based on the frequency of use of the washing machine. In this way, energy efficiency can be improved by analyzing the frequency of use of the washing machine.

[0137] The washing machine analysis unit can analyze the usage pattern of the washing machine and suggest a laundry drying method. The washing machine analysis unit can, for example, suggest an efficient drying method based on the usage pattern of the washing machine. The washing machine analysis unit can also adjust the drying method based on the frequency of use of the washing machine. The washing machine analysis unit can also analyze the usage pattern of the washing machine and suggest an optimal drying method. In this way, the optimal drying method can be suggested by analyzing the usage pattern of the washing machine.

[0138] The washing machine analysis unit can analyze washing machine data and suggest laundry storage methods. For example, the washing machine analysis unit can suggest storage methods for delicate clothes based on the washing machine data. The washing machine analysis unit can also suggest storage methods for heavily soiled clothes based on the washing machine data. The washing machine analysis unit can also suggest storage methods for regular clothes based on the washing machine data. In this way, the optimal storage method can be suggested by analyzing the washing machine data.

[0139] The air conditioner analysis unit can estimate the user's emotions and adjust the air conditioner's set temperature based on the estimated user's emotions. For example, if the user is feeling stressed, the air conditioner analysis unit can set the temperature to a comfortable level. If the user is feeling relaxed, the air conditioner analysis unit can also set a slightly cooler temperature. If the user is in a hurry, the air conditioner analysis unit can also quickly adjust the temperature. This allows for a comfortable indoor environment to be provided by adjusting the air conditioner's set temperature according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0140] The air conditioner analysis unit can analyze the usage pattern of the air conditioner and notify the optimal filter replacement time. The air conditioner analysis unit can, for example, suggest the filter replacement time based on the usage pattern of the air conditioner. The air conditioner analysis unit can also adjust the filter replacement time based on the frequency of use of the air conditioner. The air conditioner analysis unit can also analyze the usage pattern of the air conditioner and notify the optimal filter replacement time. In this way, by analyzing the usage pattern of the air conditioner, it is possible to notify the optimal filter replacement time.

[0141] The air conditioner analysis unit can analyze the air conditioner data and make suggestions to improve indoor air quality. For example, the air conditioner analysis unit can suggest the use of an air purifier based on the air conditioner data. The air conditioner analysis unit can also suggest the use of a humidifier based on the air conditioner data. The air conditioner analysis unit can also suggest the use of a dehumidifier based on the air conditioner data. In this way, by analyzing the air conditioner data, suggestions to improve indoor air quality can be made.

[0142] The air conditioner analysis unit can analyze the frequency of air conditioner use and propose optimization of energy consumption. For example, if the air conditioner is used frequently, the air conditioner analysis unit makes a proposal to optimize energy consumption. Furthermore, if the air conditioner is used infrequently, the air conditioner analysis unit can also make a proposal to maintain normal energy consumption. Furthermore, the air conditioner analysis unit can also propose optimization of energy consumption based on the frequency of air conditioner use. In this way, energy consumption can be optimized by analyzing the frequency of air conditioner use.

[0143] The air conditioner analysis unit can estimate the user's emotions and suggest an air conditioner operation mode based on the estimated user's emotions. For example, if the user is feeling stressed, the air conditioner analysis unit can suggest a relaxation mode. Furthermore, if the user is relaxed, the air conditioner analysis unit can also suggest an eco mode. Furthermore, if the user is in a hurry, the air conditioner analysis unit can also suggest a mode that quickly cools or heats the room. In this way, a comfortable indoor environment can be provided by suggesting an air conditioner operation mode according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0144] The air conditioner analysis unit can analyze the usage pattern of the air conditioner and propose an optimal operation schedule. The air conditioner analysis unit can propose an efficient operation schedule based on, for example, the usage pattern of the air conditioner. The air conditioner analysis unit can also adjust the operation schedule based on the frequency of use of the air conditioner. The air conditioner analysis unit can also analyze the usage pattern of the air conditioner and propose an optimal operation schedule. In this way, by analyzing the usage pattern of the air conditioner, an optimal operation schedule can be proposed.

[0145] The air conditioner analysis unit can analyze the air conditioner data and suggest indoor humidity control. For example, the air conditioner analysis unit can suggest the use of a humidifier based on the air conditioner data. The air conditioner analysis unit can also suggest the use of a dehumidifier based on the air conditioner data. The air conditioner analysis unit can also suggest optimal humidity control based on the air conditioner data. In this way, optimal humidity control can be suggested by analyzing the air conditioner data.

[0146] The air conditioner analysis unit can analyze the frequency of air conditioner use and suggest improvements to energy efficiency. For example, if the air conditioner is used frequently, the air conditioner analysis unit makes suggestions to improve energy efficiency. Furthermore, if the air conditioner is used infrequently, the air conditioner analysis unit can also make suggestions to maintain normal energy efficiency. Furthermore, the air conditioner analysis unit can also suggest improvements to energy efficiency based on the frequency of air conditioner use. In this way, energy efficiency can be improved by analyzing the frequency of air conditioner use.

[0147] The history consideration unit can estimate the user's emotions and analyze past purchase history based on the estimated user emotions. For example, when the user is feeling stressed, the history consideration unit analyzes only important purchase history. Furthermore, when the user is relaxed, the history consideration unit can analyze detailed purchase history. Furthermore, when the user is in a hurry, the history consideration unit can analyze purchase history that focuses on the main points. This enables more personalized recommendations by analyzing past purchase history according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0148] The history consideration unit can analyze the past purchase history and identify the user's purchasing pattern. For example, the history consideration unit can analyze the past purchase history and identify the user's purchasing pattern. The history consideration unit can also identify the user's preferences based on the past purchase history. The history consideration unit can also analyze the past purchase history and identify the user's purchasing behavior. In this way, the user's purchasing pattern can be identified by analyzing the past purchase history.

[0149] The history consideration unit can analyze past purchase history and suggest products based on the user's preferences. The history consideration unit can, for example, suggest products that match the user's preferences based on the past purchase history. The history consideration unit can also analyze past purchase history and suggest products that match the user's preferences. The history consideration unit can also suggest products based on the user's preferences based on the past purchase history. In this way, by analyzing the past purchase history, it is possible to suggest products based on the user's preferences.

[0150] The history consideration unit can analyze past purchase history and grasp changes in the user's purchasing behavior. The history consideration unit, for example, analyzes past purchase history and grasps changes in the user's purchasing behavior. The history consideration unit can also grasp trends in the user's purchasing behavior based on the past purchase history. The history consideration unit can also analyze the past purchase history and identify changes in the user's purchasing behavior. In this way, changes in the user's purchasing behavior can be grasped by analyzing the past purchase history.

[0151] The history consideration unit can estimate the user's emotions and analyze past browsing history based on the estimated user emotions. For example, if the user is feeling stressed, the history consideration unit analyzes only important browsing history. Furthermore, if the user is relaxed, the history consideration unit can analyze detailed browsing history. Furthermore, if the user is in a hurry, the history consideration unit can analyze browsing history that focuses on the main points. This enables more personalized recommendations by analyzing past browsing history according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0152] The history consideration unit can analyze past purchase history and suggest products based on the user's lifestyle. The history consideration unit can, for example, suggest products that suit the user's lifestyle based on the past purchase history. The history consideration unit can also analyze past purchase history and suggest products that suit the user's lifestyle. The history consideration unit can also suggest products that suit the user's lifestyle based on the past purchase history. In this way, by analyzing the past purchase history, it is possible to suggest products that suit the user's lifestyle.

[0153] The history consideration unit can analyze past purchase history and suggest products based on the user's purchase frequency. The history consideration unit can, for example, suggest products that match the user's purchase frequency based on the past purchase history. The history consideration unit can also analyze past purchase history and suggest products based on the user's purchase frequency. The history consideration unit can also suggest products that match the user's purchase frequency based on the past purchase history. In this way, by analyzing the past purchase history, it is possible to suggest products that match the user's purchase frequency.

[0154] The history consideration unit can analyze past purchase histories and grasp trends in user purchasing behavior. The history consideration unit, for example, analyzes past purchase histories and grasps trends in user purchasing behavior. The history consideration unit can also grasp changes in user purchasing behavior based on past purchase histories. The history consideration unit can also analyze past purchase histories and identify trends in user purchasing behavior. In this way, trends in user purchasing behavior can be grasped by analyzing past purchase histories.

[0155] The algorithm unit can estimate the user's emotions and adjust the recommendation algorithm based on the estimated user emotions. For example, if the user is stressed, the algorithm unit can use a simple algorithm. Alternatively, if the user is relaxed, the algorithm unit can use a detailed algorithm. Alternatively, if the user is in a hurry, the algorithm unit can use an algorithm that can make quick recommendations. This allows for more personalized recommendations by adjusting the recommendation algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0156] The algorithm unit can analyze the recommendation algorithm and automatically adjust the optimal parameters. For example, the algorithm unit can analyze the recommendation algorithm and automatically adjust the optimal parameters. The algorithm unit can also analyze the parameters of the recommendation algorithm and automatically adjust the optimal settings. The algorithm unit can also analyze the recommendation algorithm and automatically adjust the optimal parameters. In this way, the optimal parameters can be automatically adjusted by analyzing the recommendation algorithm.

[0157] The algorithm unit can analyze the recommendation algorithm and preferentially recommend products based on the user's preferences. The algorithm unit, for example, analyzes the recommendation algorithm and preferentially recommends products based on the user's preferences. The algorithm unit can also preferentially recommend products that match the user's preferences based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and preferentially recommend products based on the user's preferences. In this way, by analyzing the recommendation algorithm, it is possible to preferentially recommend products based on the user's preferences.

[0158] The algorithm unit can analyze the recommendation algorithm and recommend products based on the user's purchasing behavior. The algorithm unit, for example, analyzes the recommendation algorithm and recommends products based on the user's purchasing behavior. The algorithm unit can also recommend products that match the user's purchasing behavior based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and recommend products based on the user's purchasing behavior. In this way, by analyzing the recommendation algorithm, it is possible to recommend products based on the user's purchasing behavior.

[0159] The algorithm unit can estimate the user's emotions and determine the priority of recommendation algorithms based on the estimated user emotions. For example, if the user is feeling stressed, the algorithm unit can prioritize a simple algorithm. Also, if the user is relaxed, the algorithm unit can prioritize a detailed algorithm. Also, if the user is in a hurry, the algorithm unit can prioritize an algorithm that can make quick recommendations. In this way, by determining the priority of recommendation algorithms according to the user's emotions, important products can be preferentially recommended. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0160] The algorithm unit can analyze the recommendation algorithm and recommend products based on the user's lifestyle. The algorithm unit, for example, analyzes the recommendation algorithm and recommends products that suit the user's lifestyle. The algorithm unit can also recommend products that suit the user's lifestyle based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and recommend products based on the user's lifestyle. In this way, by analyzing the recommendation algorithm, it is possible to recommend products that suit the user's lifestyle.

[0161] The algorithm unit can analyze the recommendation algorithm and recommend products based on the user's purchasing frequency. The algorithm unit, for example, analyzes the recommendation algorithm and recommends products that match the user's purchasing frequency. The algorithm unit can also recommend products that match the user's purchasing frequency based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and recommend products based on the user's purchasing frequency. In this way, by analyzing the recommendation algorithm, it is possible to recommend products that match the user's purchasing frequency.

[0162] The algorithm unit can analyze the recommendation algorithm and grasp trends in user purchasing behavior. The algorithm unit, for example, analyzes the recommendation algorithm and grasps trends in user purchasing behavior. The algorithm unit can also grasp trends in user purchasing behavior based on the analysis results of the recommendation algorithm. The algorithm unit can also analyze the recommendation algorithm and grasp trends in user purchasing behavior. In this way, trends in user purchasing behavior can be grasped by analyzing the recommendation algorithm. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and recommendation 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 data of IoT home appliances using the camera 42 or a sensor of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and recommendation 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 data of IoT home appliances using the camera 42 or a sensor of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and recommendation 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 data of IoT home appliances using the camera 42 or sensor 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, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and recommendation 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 data of IoT home appliances using the camera 42 and sensors of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and recommends products based on the analysis results.

[0163] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0164] The analysis unit can also collect the user's health data and recommend health-related products based on the analysis results. For example, it can analyze the user's exercise data and recommend fitness equipment or health foods if the user is not getting enough exercise. It can also analyze the user's sleep data and recommend sleep aids or supplements if the user's sleep quality is poor. It can also analyze the user's dietary data and recommend nutritional supplements or recipes if the user's nutritional balance is unbalanced. This allows for more personalized recommendations by providing products that match the user's health condition.

[0165] The analysis unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of recommendations can be reduced to reduce the user's burden. Also, if the user is relaxed, the frequency of recommendations can be increased to suggest more products. Furthermore, if the user is in a hurry, recommendations can be made quickly to provide the necessary products immediately. In this way, by adjusting the timing of recommendations according to the user's emotions, user satisfaction can be improved.

[0166] The recommendation unit can analyze the user's social media activity and recommend related products. For example, it can recommend products related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and recommend related products. It can also recommend related products based on the activities of the user's friends on social media. In this way, highly relevant products can be recommended by analyzing the user's social media activity.

[0167] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to see.

[0168] The collection unit can collect energy consumption data of home appliances and use it to improve energy efficiency. For example, it can collect energy consumption data of refrigerators and suggest efficient operating methods. It can also collect energy consumption data of washing machines and suggest efficient laundry methods. It can also collect energy consumption data of air conditioners and suggest efficient operating methods. In this way, collecting energy consumption data makes it possible to improve energy efficiency.

[0169] The analysis unit can customize the recommended products taking into account the user's lifestyle and health condition. For example, if the user is health-conscious, health foods and fitness equipment can be recommended. If the user has a busy lifestyle, time-saving home appliances and convenient gadgets can be recommended. Furthermore, if the user is looking to relax, relaxation goods and aroma products can be recommended. This allows for more personalized recommendations by providing products that suit the user's lifestyle and health condition.

[0170] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible recommendation can be provided. If the user is relaxed, a recommendation including detailed information can be provided. Furthermore, if the user is in a hurry, a recommendation that focuses on the main points can be provided. In this way, by adjusting the way recommendations are presented according to the user's emotions, it becomes possible to provide recommendations that are easy for the user to read.

[0171] The analysis unit can analyze data from home appliances in conjunction with other smart home devices to understand comprehensive lifestyle patterns. For example, refrigerator data can be linked with smart lighting data to analyze lifestyle patterns. It can also link washing machine data with smart speaker data to analyze lifestyle patterns. It can also link air conditioner data with smart security data to analyze lifestyle patterns. By linking with other smart home devices, comprehensive lifestyle patterns can be understood.

[0172] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, only important analysis results can be displayed. If the user is relaxed, detailed analysis results can be displayed. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be displayed. In this way, by adjusting the importance of analysis results according to the user's emotions, important information can be displayed preferentially.

[0173] When making a recommendation, the recommendation unit checks product inventory status in real time and can recommend only products that are in stock. For example, it can recommend food-related products that are in stock based on refrigerator data. It can also recommend detergents and fabric softeners that are in stock based on washing machine data. It can also recommend air purifiers and humidifiers that are in stock based on air conditioner data. This ensures that products that users can purchase are provided by recommending only products that are in stock.

[0174] The processing flow of the second embodiment will be briefly explained below.

[0175] Step 1: The collection unit collects data from IoT home appliances. For example, the collection unit collects the temperature and humidity inside a refrigerator, as well as the type and amount of food stored inside. The collection unit can also collect how often a washing machine is used and the type of detergent used. The collection unit can also collect the indoor temperature, humidity, and usage time of an air conditioner. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes refrigerator data to analyze the temperature and humidity inside the refrigerator, and the type and amount of food stored therein. The analysis unit can also analyze washing machine data to analyze the frequency of laundry and the type of detergent used. Furthermore, the analysis unit can analyze air conditioner data to analyze the indoor temperature, humidity, and usage time. Step 3: The recommendation unit recommends products based on the analysis results obtained by the analysis unit. For example, the recommendation unit may use refrigerator data to recommend recipes, seasonings, and storage containers related to the food stored in the refrigerator. The recommendation unit may also use washing machine data to recommend detergents, fabric softeners, and laundry nets to use. Furthermore, the recommendation unit may use air conditioner data to recommend air purifiers, humidifiers, and dehumidifiers based on the room temperature and humidity.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0181] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0197] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0212] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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."

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] [Explanation of symbols]

[0248] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data from IoT home appliances; an analysis unit that analyzes the data collected by the collection unit; a recommendation unit that recommends products based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Equipped with a refrigerator analysis unit that analyzes refrigerator data in detail 2. The system of claim 1.

3. The analysis unit Equipped with a washing machine analysis unit that analyzes washing machine data in detail 2. The system of claim 1.

4. The analysis unit Equipped with an air conditioner analysis unit that analyzes air conditioner data in detail 2. The system of claim 1.

5. The recommendation unit Equipped with a history consideration unit that analyzes the user's past purchase history and browsing history 2. The system of claim 1.

6. The recommendation unit Equipped with an algorithm section, it recommends products based on the analysis results 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of IoT home appliance data collection in a specific way based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Monitor the operation status of IoT home appliances in real time and strengthen data collection when an abnormality is detected 2. The system of claim 1.

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