Using a machine estimation device

Abstract

Provided is an in-use equipment device that estimates the use of equipment in use such as home appliances in a simple manner without using sensors that only collect power consumption.　In a management device 100, a communication unit 101 acquires a user's biometric information from a wearable device 120 worn by the user. The acquired biometric information is stored in a log database 104. When an estimation timing is reached, an estimation unit 102 estimates the equipment being used by the user, on the basis of the biometric information stored in the log database 104.

Description

[Technical Field]

[0001] The present invention relates to an appliance usage estimation device that estimates the usage state of appliances such as home appliances. [Background technology]

[0002] Interest in the SDGs has increased worldwide, and in Japan in particular, there has been a growing movement to reduce energy consumption due to the country's low energy self-sufficiency rate.In particular, with the increase in remote work and voluntary refraining from going out due to the COVID-19 pandemic, household energy conservation, which reduces energy consumption at home, has attracted attention.

[0003] Existing solutions for promoting energy conservation in the home include devices that visualize household power consumption, but these devices have the following drawbacks: For example, while displaying the overall aggregate value of the electricity used in the home is effective for understanding the overall picture, it does not allow for understanding trends in the power consumption of each individual appliance, making it difficult to automatically suggest effective energy-saving methods. Also, while it is possible to collect the overall aggregate value of the electricity used in the home by collecting information from a smart meter, understanding the power consumption of each individual appliance requires installing communication-capable sensors in the outlets of each appliance. Installing a large number of sensors that have no other use than collecting power increases the financial and operational burden on users and may discourage them from conserving energy.

[0004] Patent Document 1 describes a technology that predicts power usage and, if it exceeds a reference amount, searches for and displays a proposal to change the usage time period of at least one of the home appliances that is estimated to be in use.

[0005] Furthermore, Patent Document 2 describes a home appliance state estimation device that does not require special work such as installation work and is capable of estimating the state of general home appliances that are not high-function home appliances. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-253340 Summary of the Invention [Problem to be solved by the invention]

[0007] The technology described in Patent Document 1 is based on the premise that information on fluctuations in power data when the consumer's electrical appliances are operated has been acquired in advance to estimate the home appliances being used. This requires measuring the power consumption trends of each home appliance owned by the consumer in advance, which requires the installation of measuring devices, etc., and may increase implementation costs.

[0008] Furthermore, the technology described in Patent Document 2 discloses the use of sensors provided in PCs, tablets, smartphones, and the like.

[0009] However, since these PCs and other devices are not always turned on and are not necessarily placed near each device, these sensors, such as temperature sensors and microphones, cannot constantly determine the external state of each device.

[0010] Therefore, in order to solve the above-mentioned problems, the present invention aims to provide an equipment usage device that easily estimates the usage of equipment such as each home appliance without using a sensor that only collects power consumption. [Means for solving the problem]

[0011] The device usage estimation device of the present invention includes a biometric information acquisition unit that acquires biometric information of a user from a wearable device worn by the user, and an estimation unit that estimates the device being used by the user based on the biometric information. [Effects of the Invention]

[0012] According to the present invention, the usage state of a device such as a home appliance can be easily estimated. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a diagram showing a system configuration in a home including a management device 100 which is an appliance usage estimation device according to the present disclosure. [Figure 2] 2 is a block diagram showing the functional configuration of a management device 100. FIG. [Figure 3] FIG. 2 is a diagram showing a specific example of a log database 104. [Figure 4] FIG. 2 is a schematic diagram of an estimation process by the estimation model 103. [Figure 5] 4 is a flowchart showing the operation of the management device 100. [Figure 6] 1 shows an example of a display on the display unit 105. [Figure 7] FIG. 10 is a diagram showing fixed power consumption or variable power consumption for each time period. [Figure 8] 10 is a flowchart (part 1) showing processing based on rules for classifying fixed power consumption and variable power consumption. [Figure 9] 10 is a flowchart (part 2) showing processing based on rules for classifying fixed power consumption and variable power consumption. [Figure 10] FIG. 10 is a diagram showing a system configuration including a management device 100a according to a modified example. [Figure 11] 10A and 10B are diagrams showing examples of the log database 104 and the display unit 105 when the used device is discarded. [Figure 12] 1 is a diagram illustrating an example of a hardware configuration of a management device 100 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] The present disclosure will be described with reference to the accompanying drawings. Whenever possible, the same parts are designated by the same reference numerals and redundant description will be omitted.

[0015] 1 is a diagram showing a system configuration in a home including a management device 100, which is a wearable device usage estimation device according to the present disclosure. As shown in the diagram, the system includes the management device 100, a wearable device 120, and an external server 200.

[0016] A home includes appliances such as a refrigerator 131, a TV 132, an electric fan 133, and an air conditioner 134, and these appliances are supplied with power from a commercial power source 300. A smart meter 110 measures the power consumption of these appliances.

[0017] The wearable device 120 is a device that acquires biometric information (body temperature, pulse, blood pressure, etc.) of the user.

[0018] The management device 100 acquires power consumption information of each device in use from the smart meter 110, and acquires biometric information or user behavior (number of steps, body tilt, whether the user is active, etc.) from the wearable device 120. Then, the management device 100 determines whether or not a device in use, such as the refrigerator 131, is being used based on the power consumption information and the biometric information.

[0019] Furthermore, the management device 100 acquires weather information (weather, temperature, humidity, etc.) from the external server 200, and estimates the operating status of the device in use based on the weather information (external information) along with biological information, power consumption, etc.

[0020] The external server 200 is a server that stores weather information (information related to the weather, such as the weather, temperature, humidity, and amount of precipitation). The external server 200 is a server that stores weather information that generally exists.

[0021] 2 is a block diagram showing the functional configuration of the management device 100. As shown in the figure, the management device 100 includes a communication unit 101, an estimation unit 102, an estimation model 103, a log database 104, a display unit 105, a learning unit 106, a situation determination unit 107, and a log data management unit 108.

[0022] The communication unit 101 is a part that acquires biological information and external information (weather information, user behavior, power consumption information, etc.) from the wearable device 120 and the external server 200.

[0023] The estimation unit 102 is a part that estimates the usage status of the device in use using the estimation model 103 and the log database 104. That is, the estimation unit 102 inputs the user's most recent biometric information and most recent external information stored in the log database 104 into the estimation model 103, and acquires the usage status (whether or not the device is in use) output from the estimation model 103. The external information may be weather information, user behavior, power consumption, power consumption type, etc., but all of these are not necessarily required and at least one of them is sufficient.

[0024] The estimation unit 102 may input information such as biological information directly to the estimation model 103, or may derive a fluctuation state (increase / decrease, etc.) from the most recent state and input the fluctuation state to the estimation model 103.

[0025] The estimation model 103 is a machine learning model that inputs the user's biometric information and external information stored in the log database 104 and outputs the usage status of the device being used. This estimation model 103 is stored in a storage unit (not shown). For convenience, the biometric information, external information, etc. are stored in the log database 104, but the estimation unit 102 may directly acquire them from the communication unit 101 and input them to the estimation model 103.

[0026] The log database 104 is a section that stores biometric information acquired by the communication unit 101, weather information acquired from the external server 200, and other external information. The log database 104 also stores the results of a questionnaire conducted in advance in association with the date and time, etc. The results of this questionnaire may be from a single user to be estimated, or may include results from other users.

[0027] The display unit 105 displays the usage status of the device in use and asks the user whether the usage status was successful. For example, the display unit 105 is configured as a touch panel display, and asks the user whether the usage status of the device in use was successful and receives a response. This response is reflected in and stored in the log database 104.

[0028] The learning unit 106 is a part that learns and updates the estimation model 103. The learning unit 106 generates the estimation model 103 by learning using known machine learning, using the log data stored in the log database 104 as an explanatory variable and the equipment used, which is the survey result or response, as a target variable.

[0029] The situation determination unit 107 is a part that determines whether the power consumption is fixed or variable based on the user's behavior, etc. That is, the situation determination unit 107 determines the power consumption type based on the user's (management device 100) location information, residence, user activity status, power consumption, etc. This information on the power consumption type is used in the estimation process.

[0030] Next, a description will be given of the operation of the estimation unit 102. As described above, the estimation unit 102 estimates the usage status of devices in use in the user's home based on the user's biological information, weather information, and power consumption information.

[0031] Prior to the estimation process, the estimation unit 102 processes the data in the log database 104. Fig. 3 is a diagram showing a specific example. Fig. 3(a) shows a specific example of log data of a target user stored in the log database 104. As shown in the diagram, the log database 104 stores the date and time of measurement, the user's behavior (activity and sleep) at that time, the user's biological information (the user's body temperature and the user's heart rate and blood pressure), the power consumption obtained from the smart meter 110, the temperature obtained from the external server 200, the weather, the type of power consumption (fixed / variable), etc.

[0032] The estimation unit 102 processes this log data into fluctuation value data indicating the fluctuation state (increase, decrease, maintenance, etc.). Figure 3(b) is a diagram showing the processed information indicating the fluctuation state of body temperature, heart rate, power consumption, air temperature, etc. for each hour. The increase or decrease in power consumption further indicates whether it is an increase or decrease in fixed power consumption or an increase or decrease in variable power consumption. The estimation unit 102 inputs this fluctuation value data into the estimation model 103, and receives an output of the usage status of the device being used from the estimation model 103. Variable power consumption and fixed power consumption will be described later.

[0033] Using the fluctuation value data shown in FIG. 3(b) as explanatory variables and the equipment used at that time as a target variable, learning processing is performed to generate an estimation model 103. At this time, the equipment used is prepared in advance based on a questionnaire or the like. For example, in FIG. 3(b), body temperature, heart rate, fixed power consumption, and air temperature increase between 8:00 and 9:00 on August 12, 2020. Assume that the questionnaire results indicate that the TV was on at that time (see FIG. 3(c)).

[0034] Figure 3(c) shows the survey results. The devices used by users are registered in advance here. The devices used here are reflected in the device used column in Figure 3(b) and are used as learning data.

[0035] The learning unit 106 performs machine learning using body temperature, heart rate, fixed power consumption, and an increase in air temperature as explanatory variables and whether the TV was on as a response variable to learn the estimation model 103. Note that although whether the TV was on is used as the response variable in the above example, this is not the only example, and the response variable may also be the usage status of multiple devices, such as whether the TV was on or whether the lights were on.

[0036] In this way, the learning unit 106 can generate the estimation model 103 by performing machine learning based on the variation value data and the questionnaire results (devices used).

[0037] When the learning unit 106 initially generates the estimation model 103, it uses the survey results to obtain the equipment to be used as the learning target. After the estimation model 103 is generated, the learning unit 106 presents the estimated equipment to the user and updates the log database 104 in accordance with the user's response, and performs the learning process based on that.

[0038] 3(d) is a diagram showing a specific example of an updated log database 104. As shown in the figure, no survey results have been obtained from the time slot after 10:00 on August 12, 2020, but the used devices can be obtained by estimating the devices used using the estimation model 103 that has been trained up to that point and inquiring about the results from the user. Thereafter, the learning unit 106 periodically re-learns based on the log database 104 or the results of a re-administered survey, thereby updating the estimation model 103.

[0039] FIG. 4(a) is a schematic diagram of the estimation process by the estimation model 103. The estimation model 103 is trained based on the fluctuation value data and the questionnaire results / answers (devices used). For example, as shown in FIG. 4(b), when the outside temperature rises, the body temperature falls (or remains the same), the heart rate does not increase, and the fluctuation power consumption increases, the estimation model 103 estimates that an electric fan was used. Similarly, when the outside temperature rises, the body temperature rises (or remains the same), the heart rate increases, and the fluctuation power consumption increases, the estimation model 103 estimates that a television was used.

[0040] Next, the operation of the management device 100 in the present disclosure will be described. Fig. 5 is a flowchart showing the operation of the management device 100. The communication unit 101 periodically acquires various information (for example, weather information such as external information) from the external server 200 (S101). The communication unit 101 also periodically acquires various information (biometric information) from the wearable device 120 (S103). Furthermore, when generating the initial estimation model 103, the communication unit 101 acquires survey result information from a survey information DB (not shown).

[0041] The communication unit 101 stores the acquired external information (weather information, etc.) and biological information in the log database 104 (S102, S104). Initially, the log database 104 stores the questionnaire result information.

[0042] The learning unit 106 learns and generates the estimation model 103 (S105), and stores it as the estimation model 103 in a storage unit (not shown).

[0043] The processes S101 to S105 are performed periodically at a predetermined timing and / or according to data update (when a certain number of records (devices in use) are obtained).

[0044] The estimation unit 102 acquires the most recent (for example, the past hour) necessary information (biometric information, weather information, etc.) from the log database 104 at a pre-specified timing (for example, every hour) (S106), and estimates the device in use using the estimation model 103 (S107). The estimation unit 102 displays the estimated device in use on the display unit 105, and asks the user whether the device in use is correct (S108). The estimation unit 102 also outputs the estimation result to the log database 104, and registers it in a field for the device in use for that time period (S109).

[0045] The user looks at the devices being used displayed on the display unit 105 and responds according to the actual situation by operating the user (S110). FIG. 6 shows an example of the display on the display unit 105. In the figure, a question is posed as to whether or not the electric fan was used. The user selects (tap) YES if the electric fan was actually used, and NO if not. As a result, the devices actually used are reflected in the log database 104 from the display unit 105 and are used to update the estimation model 103.

[0046] The display unit 105 displays the estimated result of the used device so that the user can check it at any time. At this time, the display unit 105 may randomly select one of the multiple used devices as the estimated result and display it. The user can respond by operating the display unit 105 as to whether or not the displayed used device was used.

[0047] The user's response results are stored in the log database 104 (see FIG. 3(d)). The learning unit 106 sequentially updates (re-learns) the estimation model 103 when a certain number of responses (e.g., 30 or more records of both arbitrarily set, fixed power consumption, and variable power consumption) are accumulated in the log database 104. At this time, the user's response data is clearly distinguished from data other than the response data (a group of data obtained from several patterns of subjects), thereby making it possible to reflect the user's tendencies while also reflecting general information.

[0048] For example, the initial estimation model 103 based on the survey results may be for general use and may not be a model specialized for the user. Therefore, when updating the estimation model 103, it is preferable to update the estimation model 103 specialized for the target user by reducing or eliminating the application of the survey results. For example, when learning, the learning unit 106 performs weighting processing or the like to extract only the response results from the target user from the log database 104 and perform the learning process.

[0049] There may be cases where a used device that existed when the estimation model 103 was initially generated has been discarded. Conversely, there may be cases where a newly added used device exists. In such cases, the log data management unit 108 updates the used device stored in the log database 104. For example, when a user writes in a used device that has been added, the log data management unit 108 stores the used device in the log database 104.

[0050] Conversely, when the log data management unit 108 receives an entry of a used device that has been discarded by the user, it deletes the used device from the log database 104 .

[0051] Furthermore, when the display unit 105 displays the discarded used device and an instruction to that effect is given, the log data management unit 108 does not update the log database 104 with the discarded used device.

[0052] Next, we will explain fixed / variable power consumption. Fixed power consumption refers to a type of power consumption that does not fluctuate much depending on the user's intention, while variable power consumption refers to a type of power consumption that is likely to fluctuate depending on the user's intention. These power consumption types are classified based on rules. Fixed power consumption is mainly power consumed by refrigerators. Variable power consumption is power consumed that fluctuates depending on the user's operation of devices such as TVs.

[0053] 7 is a diagram showing fixed or variable power consumption for each time period. As shown in the diagram, power consumption is classified as fixed or variable depending on the time period.

[0054] The information on home appliances and family structure used in the classification of power consumption types is based on a questionnaire conducted in advance. The user activity status used in the classification is acquired from the wearable device 120.

[0055] Next, the classification rules for fixed power consumption and variable power consumption will be described. FIGS. 8 and 9 are flowcharts showing processing based on the classification rules for fixed power consumption and variable power consumption. When a predetermined condition is met (every hour in the present disclosure), the situation determination unit 107 starts processing to classify the user's power consumption into two types, fixed power consumption and variable power consumption, based on the user's location information (S201). The situation determination unit 107 references the user information (FIG. 3(e)) in the log database 104 to determine whether the user's family consists of one user (S202). If the situation determination unit 107 determines that the family consists of one user (S202: YES), it determines whether the user's location information is outside the user's place of residence (by reference to the user information) (S203).

[0056] When the situation determination unit 107 determines that the user's location information is outside the user's place of residence (S203: YES), it records the power consumption for the most recent arbitrary period as fixed power consumption in the log database 104 (S204). For example, referring to FIG. 3(a) or (b), when determining the state at 9:00 on August 12, 2020, the most recent arbitrary period (one hour, from 8:00 to 9:00) is determined to be fixed power consumption. Thereafter, power consumption determination is made by referring to the log database 104.

[0057] When the situation determination unit 107 determines that the user's location information indicates that the user is at home (S203: YES), it determines whether the user is asleep (S205). This is determined based on a gyro sensor or the like of the wearable device 120. When the situation determination unit 107 determines that the user is asleep (S205: YES), it records the power consumption for the most recent arbitrary period as fixed power consumption (S206).

[0058] The situation determination unit 107 determines whether or not data recorded as fixed power consumption exists in the log database 104 (S207). If the situation determination unit 107 determines that data recorded as fixed power consumption does not exist (S207: NO), it records the data as variable power consumption in the log database 104 (S208).

[0059] If there is data recorded as fixed power consumption (S207: YES), the situation determination unit 107 records it as (power consumption in the most recent arbitrary period) - (most recently recorded fixed power consumption) = (variable power consumption) (S209, S210).

[0060] 9 is a flowchart showing the processing when the user's family consists of more than one person. When the situation determination unit 107 determines that the user is asleep (S301: YES), it further determines whether the transition in power consumption has fluctuated by more than a threshold (S302). When the situation determination unit 107 determines that the transition in power consumption has not fluctuated by more than a threshold, it records the power consumption for the most recent arbitrary period as fixed power consumption in the log database 104 (S303).

[0061] Furthermore, if it is determined in process S301 that the user is not asleep, or if it is determined in process S302 that the power consumption trend does not fluctuate by more than the threshold, the situation determination unit 107 further determines whether or not there is data recorded in the log database 104 as fixed power consumption (S304).

[0062] Here, if it is determined that data exists, the situation determination unit 107 records the data as variable power consumption (power consumption for the most recent arbitrary period - most recently recorded fixed power consumption = variable power consumption) if data recorded as fixed power consumption exists (S304: YES) (S305, S306).

[0063] When the situation determination unit 107 determines that there is no data recorded as fixed power consumption (S304: NO), it records the data as variable power consumption in the log database 104 (S307). In this way, the situation determination unit 107 records the type of power consumption in the fixed / variable column in the log database.

[0064] Next, a modified example of the management device 100 of the present disclosure will be described. The management device 100 in Fig. 1 has been described as being equivalent to a so-called user terminal (such as a smartphone). The management device 100 is not limited to a user terminal, and may be a device that functions as a server located on a network.

[0065] 10 is a diagram showing a system configuration including a management device 100a in a modified example. As shown in the figure, a wearable device 120 transmits biometric information and the like to the management device 100a via a network. The management device 100a notifies a user terminal 100b of the equipment being used, determined based on the biometric information. On the user terminal 100b, the user selects whether the equipment being used is correct or incorrect, and the response is transmitted to the management device 100. The management device 100 updates the log database 104 based on the correct or incorrect result.

[0066] Next, a description will be given of updating the log database 104 in the management device 100 according to the present disclosure when a used device to be estimated is newly added or discarded.

[0067] FIG. 11 is a diagram showing an example of the log database 104 and a display on the display unit 105. As shown in FIG.

[0068] As shown in FIG. 11(a), an attempt is made to estimate the devices used in the log database 104 at 10:00 on August 12, 2020. As shown in FIG. 11(b), the display unit 105 displays a display for a query to confirm the device used. If the user selects "No" in response to the display (FIG. 11(c)), the display unit 105 queries whether the device used has been discarded. If the user selects "Yes" in this case (FIG. 11(d)), the log data management unit 108 does not update the log database 104 with the device used. Thereafter, discarded devices used are not displayed on the display unit 105. In Figure 11(e), a TV is registered as a used device. The TV is registered as a used device based on the result of an inquiry to the user as to whether or not the TV has been used. Note that the inquiry is made randomly, so the inquiry about the use of the TV is not necessarily made. 11(d), if discarded is selected, a device that is actually being used may be newly added and registered in place of the discarded device. In this case, the added device is registered as a used device in the log database shown in FIG. 11(e).

[0069] Next, the effects of the management device 100 (and the management device 100a) according to the present disclosure will be described.

[0070] The management device 100 or management device 100a disclosed herein functions as a device-in-use estimation device. Unless otherwise specified, the management device 100 will hereinafter be understood to include the management device 100a. In the management device 100, the communication unit 101 acquires biometric information of a user from a wearable device 120 worn by the user. The acquired biometric information is stored in a log database 104.

[0071] When the estimation timing arrives, the estimation unit 102 estimates the device that the user is using based on the biometric information stored in the log database 104.

[0072] According to this configuration, it is possible to estimate the devices used at home or the like based on biological information acquired from the wearable device 120 that the user always carries. Therefore, it is possible to estimate the devices used without using special sensors or the like.

[0073] The biological information used to estimate the device in use includes, for example, at least one of the user's body temperature, heart rate, and blood pressure. These are information that affect the user's activity and, as a result, affect the device in use. Naturally, other biological information may also be included.

[0074] In the management device 100 of the present disclosure, the communication unit 101 and the situation determination unit 107 function as a power consumption acquisition unit and acquire the power consumption type (variable / fixed). The estimation unit 102 estimates the device being used based on the power consumption type. This power consumption type is information determined based on whether the power consumption varies depending on the user's will. For example, the power consumption of a television is treated as variable power consumption because the television is used at the user's will. On the other hand, the power consumption of a refrigerator is treated as fixed power consumption because the power consumption does not vary depending on the user's will.

[0075] The power consumption acquisition unit acquires the power consumption type, which is one of the external information, based on the user behavior (whether the user is asleep), the user location (whether the user is at a residence), and the power consumption.

[0076] According to this configuration, by estimating the devices in use according to the power consumption type, the estimation accuracy can be improved.

[0077] In the management device 100 of the present disclosure, the communication unit 101 functions as an external information acquisition unit. The estimation unit 102 estimates the device in use based on the external information. The external information includes at least one of weather, temperature, humidity, and precipitation.

[0078] According to this configuration, it is possible to improve the estimation accuracy of the equipment in use by using external information such as weather. Weather and other factors affect the equipment in use, so the estimation accuracy can be improved.

[0079] The management device 100 of the present disclosure further includes a log database 104 that functions as a log storage unit that stores biometric information at predetermined time intervals. The estimation unit 102 estimates the device being used based on fluctuations in the biometric information.

[0080] In the present disclosure, actual measured values ​​indicating biological information, etc. may be used, but the accuracy can be improved by estimating based on fluctuations from the most recent situation. Fluctuations in biological information, such as increases in body temperature and heart rate, are affected by the devices used by the user. For example, turning on an electric fan will cause a decrease in body temperature. Therefore, the accuracy of estimation can be improved by observing fluctuations in biological information, etc.

[0081] In the present disclosure, the log database 104 stores, for example, hourly biological information, external information, and power consumption types. The estimation unit 102 calculates fluctuations (increase, decrease, slight increase, slight decrease, maintenance, etc.) of the biological information stored in the log database 104 in chronological order, and estimates the device being used based on the fluctuations, thereby improving the accuracy of the estimation.

[0082] The estimation unit 102 estimates the device used by the user using an estimation model trained with at least biological information as an explanatory variable and the device used as a target variable. When external information and power consumption types are used as estimation parameters, they are input as explanatory variables and trained.

[0083] The management device 100 (100a) which is the device usage estimation device of the present invention has the following configuration.

[0084] [1] a biometric information acquisition unit that acquires biometric information of a user from a wearable device worn by the user; an estimation unit that estimates a device that a user is using based on the biometric information; An equipment usage estimation device comprising:

[0085] [2] The biological information includes at least one of the user's body temperature, heart rate, and blood pressure. [1] The equipment usage estimation device described in [1].

[0086] [3] An external information acquisition unit that acquires external information that affects the device in use, the estimation unit estimates the device being used based on the external information in addition to the biological information. [1] or [2].

[0087] [4] The external information acquisition unit acquires a power consumption type (variable or fixed), The estimation unit estimates a device in use based on the power consumption type. [3] The equipment usage estimation device described in [3].

[0088] [5] The power consumption type is determined based on whether the power consumption varies depending on the user's intention. [4] The equipment usage estimation device described in [4].

[0089] [6] the external information acquisition unit acquires a power consumption type based on a user's behavior, a user's location, and power consumption; [3] to [5].

[0090] [7] the external information acquisition unit includes, as the external information, at least one of weather, temperature, humidity, and precipitation; [3] to [6].

[0091] [8] a log storage unit that stores the biological information at predetermined time intervals; The estimation unit estimates the device being used based on the variation in the biological information. [1] to [7].

[0092] [9] the estimation unit estimates the device being used by the user using an estimation model trained with the biometric information as an explanatory variable and the device being used as a target variable; [1] to [8].

[0093] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are connected directly or indirectly (for example, by wire, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0094] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocation, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0095] For example, the management device 100 and management device 100a (hereinafter referred to as the management device 100) according to an embodiment of the present disclosure may function as a computer that performs processing of the device usage estimation method of the present disclosure. Fig. 12 is a diagram illustrating an example of the hardware configuration of the management device 100 according to an embodiment of the present disclosure. The above-described management device 100 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.

[0096] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the management apparatus 100 may be configured to include one or more of the apparatuses shown in the figure, or may be configured to exclude some of the apparatuses.

[0097] Each function of the management device 100 is realized by loading specified software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and storage 1003.

[0098] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned estimation unit 102, learning unit 106, situation determination unit 107, log data management unit 108, etc. may be realized by the processor 1001.

[0099] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with the programs. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the estimation unit 102, the learning unit 106, the situation determination unit 107, and the log data management unit 108 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be made for other functional blocks. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may also be transmitted from a network via a telecommunications line.

[0100] The memory 1002 is a computer-readable recording medium and may be configured, for example, by at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a device usage estimation method according to an embodiment of the present disclosure.

[0101] Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0102] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned communication unit 101 may be realized by the communication device 1004. The communication unit 101 may be implemented as a transmission unit and a reception unit that are physically or logically separated.

[0103] The input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (for example, a touch panel).

[0104] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0105] Furthermore, the management device 100 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0106] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0107] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0108] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0109] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0110] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, notification of predetermined information (e.g., notification that "X is true") is not limited to being done explicitly, but may be done implicitly (e.g., by not notifying the predetermined information).

[0111] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0112] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0113] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), these wired and / or wireless technologies are included within the definition of transmission media.

[0114] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0115] Note that terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0116] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0117] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0118] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0119] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0120] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0121] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0122] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0123] Any reference to an element using a designation such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0124] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0125] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0126] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0127] 100...management device, 120...wearable device, 200...external server, 131...refrigerator, 132...TV, 133...electric fan, 134...air conditioner, 300...commercial power, 110...smart meter, 101...communication unit, 102...estimation unit, 103...estimation model, 104...log database, 105...display unit, 106...learning unit, 107...situation judgment unit, 108...log data management unit.

Claims

1. a biometric information acquisition unit that acquires biometric information of a user from a wearable device worn by the user; an estimation unit that estimates a device that the user is using based on the biometric information; Equipped with the estimation unit estimates the device being used by the user using an estimation model trained with the biometric information as an explanatory variable and the device being used as a target variable; Equipment used estimation device.

2. The biological information includes at least one of the user's body temperature, heart rate, and blood pressure. The device usage estimation device according to claim 1 .

3. An external information acquisition unit that acquires external information that affects the device in use, the estimation model is trained using the external information as explanatory variables in addition to the biological information, the estimation unit estimates the device being used based on the external information in addition to the biological information. The device usage estimation device according to claim 1 .

4. the external information acquisition unit acquires a power consumption type; the estimation model is trained using the power consumption type as an explanatory variable in addition to the biological information and the external information, the estimation unit estimates the device in use based on the power consumption type in addition to the biological information and the external information. The device usage estimation device according to claim 3 .

5. The power consumption type is determined based on whether the power consumption varies depending on the user's intention. The device usage estimation device according to claim 4 .

6. the external information acquisition unit acquires a power consumption type based on a user's behavior, a user's location, and power consumption; The device usage estimation device according to claim 3 .

7. the external information acquisition unit includes, as the external information, at least one of weather, temperature, humidity, and precipitation; The device usage estimation device according to claim 3 .

8. a log storage unit that stores the biological information at predetermined time intervals; The estimation model is trained using a variation in biological information as an explanatory variable and the device in use as a target variable, The estimation unit estimates the device being used based on the variation in the biological information. The device usage estimation device according to claim 1 .

9. a learning unit configured to learn the biological information as an explanatory variable and the device in use as a target variable, The device usage estimation device according to claim 1 .

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