Patent Publication Number: US-2023151524-A1

Title: Drying time prediction method, drying time prediction system, and dryer

Description:
CROSS-REFERENCE OF RELATED APPLICATIONS 
     This application is the U.S. National Phase under 35 U.S.C. § 371 of International Patent Application No. PCT/JP2021/024637, filed on Jun. 29, 2021, which in turn claims the benefit of Japanese Application No. 2021-028731, filed on Feb. 25, 2021, the entire disclosures of which Applications are incorporated by reference herein. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to drying time prediction. 
     BACKGROUND ART 
     There are known techniques for making a prediction about a future event (see Patent Literature 1 (PTL 1), for example). 
     CITATION LIST 
     Patent Literature 
     [PTL 1] Japanese Unexamined Patent Application Publication No. 2014-85914 
     SUMMARY OF INVENTION 
     Technical Problem 
     It is desirable to display the remaining drying time of a drying operation to a user who uses a dryer or other equipment to dry clothes and other items. 
     Thus, the present disclosure aims to provide, for example, a drying time prediction method. 
     Solution to Problem 
     A drying time prediction method according to one aspect of the present disclosure includes: obtaining an operation condition set at the start of operation of a first dryer; obtaining the first actual operation time period of each of at least one first operation performed by the first dryer; obtaining operation finish time information from a learned model by inputting, to the learned model, the operation condition and first actual information that is based on the first actual operation time period, the operation finish time information being related to the finish time of the operation started under the operation condition; and outputting first information based on the operation finish time information. 
     A drying time prediction system according to another aspect of the present disclosure includes: an operation condition obtainment unit that obtains an operation condition set at the start of operation of a first dryer; a first actual operation time period obtainment unit that obtains the first actual operation time period of each of at least one first operation performed by the first dryer; a learned model that outputs operation finish time information in response to input of the operation condition and first actual information that is based on the first actual operation time period, the operation finish time information being related to the finish time of the operation started under the operation condition; and an outputting unit that outputs first information based on the operation finish time information. 
     A dryer according to still another aspect of the present disclosure is the first dryer using the preceding drying time prediction system. The dryer includes: a provision unit that provides the drying time prediction system with the operation condition, the sensing information, and the first actual operation time period; an obtainment unit that obtains the first information from the drying time prediction system; and a display that displays information on the first information. 
     Advantageous Effects of Invention 
     It is possible to predict drying time by using the drying time prediction method, the drying time prediction system, or the dryer according to one aspect of the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    schematically illustrates a relationship between remaining drying time displayed by a conventional method and actual remaining drying time. 
         FIG.  2    schematically illustrates a relationship between remaining drying time displayed by a method using a learned model and actual remaining drying time. 
         FIG.  3    is a block diagram illustrating a configuration example of a drying time prediction system according to an embodiment. 
         FIG.  4    is a block diagram illustrating a configuration example of a dryer according to the embodiment. 
         FIG.  5    is a block diagram illustrating a configuration example of a server device according to the embodiment. 
         FIG.  6    schematically illustrates an example in which a standardization unit according to the embodiment calculates standardized actual operation time periods. 
         FIG.  7    is a block diagram illustrating a configuration example of a smartphone according to the embodiment. 
         FIG.  8    is a flowchart illustrating drying time prediction processing according to the embodiment. 
         FIG.  9    is a flowchart illustrating standardization processing according to the embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENT 
     (Background that Led to Obtainment of One Aspect of the Present Disclosure) 
     In conventional methods, when a dryer or other equipment displays the remaining drying time of a drying operation, remaining drying time obtained by adding a correction value to a default value preset for each amount of fabrics is displayed at the start of the drying operation, and then a countdown of the remaining drying time that decreases with elapse of time starts. 
     In addition, in conventional methods, to accurately display remaining drying time, the degree of dryness is determined at each fixed point (e.g., every 10 minutes) during a drying cycle by using sensing information on the state of the dryer, and the displayed remaining drying time is adjusted according to the determination result. 
       FIG.  1    schematically illustrates a relationship between remaining drying time displayed by a conventional method and actual remaining drying time. 
     As illustrated in  FIG.  1   , if remaining drying time is displayed by a conventional method, there are, for example, a phenomenon in which the displayed remaining drying time discontinuously changes and a phenomenon in which a countdown of the displayed remaining drying time stops for a certain period of time. The occurrence of such phenomena may confuse a user who looks at the displayed remaining drying time. 
     In view of the foregoing, the inventors of the present disclosure conducted diligent experiments and analysis to obtain, for example, a drying time prediction method capable of predicting drying time more accurately than conventional methods. Through their efforts, the inventors identified that it is possible to predict drying time more accurately than conventional methods by using a learned model pre-trained to output remaining drying time in response to input of operation conditions set at the start of operation of a dryer or other equipment, sensing information on the state of the dryer performing an operation started under the operation conditions, and the actual operation time periods of past operations performed by the dryer. 
       FIG.  2    schematically illustrates a relationship between remaining drying time displayed by a method using the learned model and actual remaining drying time. 
     As it is known from  FIGS.  1  and  2   , drying time prediction by a method using the learned model enables more accurate display of remaining drying time than conventional methods. 
     The inventors conducted further diligent experiments and analysis on the basis of the findings and arrived at the creation of the drying time prediction method, the drying time prediction system, and the dryer described below. 
     A drying time prediction method according to one aspect of the present disclosure includes: obtaining an operation condition set at the start of operation of a first dryer; obtaining the first actual operation time period of each of at least one first operation performed by the first dryer; obtaining operation finish time information from a learned model by inputting, to the learned model, the operation condition and first actual information that is based on the first actual operation time period, the operation finish time information being related to the finish time of the operation started under the operation condition; and outputting first information based on the operation finish time information. 
     In the preceding drying time prediction method, it is possible to predict drying time by using the learned model pre-trained to output an operation finish time in response to input of operation conditions set at the start of operation of the first dryer and the first actual operation time period of each past operation performed by the first dryer, the operation finish time being the operation finish time of the operation started under the operation conditions. 
     In addition, the drying time prediction method may further include obtaining sensing information on the state of the first dryer performing the operation started under the operation condition. The obtaining of the operation finish time information may include obtaining the operation finish time information from the learned model by inputting the sensing information to the learned model in addition to the operation condition and the first actual information. 
     Thus, the operation finish time information output by the learned model also reflects the sensing information. This enables more accurate drying time prediction. 
     In addition, the drying time prediction method may further include obtaining, for each of the at least one first operation, a first actual operation condition set at the start of the first operation, the first actual operation condition being associated with the first actual operation time period of the first operation; obtaining, for each of at least one second operation that has been performed by at least one second dryer, a second actual operation time period of the second operation and a second actual operation condition set at the start of the second operation, the second actual operation condition being associated with the second actual operation time period of the second operation; and calculating a standardized actual operation time period for each of at least one first actual operation time period obtained in the obtaining of the first actual operation time period, by standardizing each of the at least one first actual operation time period according to the distribution of at least one second actual operation time period associated with the second actual operation condition corresponding to the first actual operation condition associated with the first actual operation time period, the at least one second actual operation time period being included in at least one second actual operation time period of the at least one second operation. The first actual information may be based on at least one standardized actual operation time period calculated for the at least one first actual operation time period in the calculating. 
     Thus, each standardized actual operation time period reflects whether the drying time period of a past operation performed by the first dryer tends to be longer or shorter when seen in the distribution of the drying time period(s) of past operation(s) performed by the at least one second dryer under the corresponding operation condition. This enables more accurate drying time prediction. 
     In addition, the drying time prediction method may further include calculating the mean value of the at least one standardized actual operation time period calculated for the at least one first actual operation time period. The first actual information may be based on the mean value. 
     Thus, the mean value reflects whether past operations performed under all the actual operation conditions tend to take longer or shorter. This enables predicted drying time to reflect whether the past operations performed under all the actual operation conditions tend to take longer or shorter. 
     In addition, the first operation may be an operation performed by the first dryer during a past predetermined period, and the second operation may be an operation performed by the at least one second dryer during the past predetermined period. 
     The types of items put in a dryer tend not to change significantly for a certain period. This enables more accurate drying time prediction. 
     In addition, the operation condition and the first actual operation condition may each include the fabric amount of items put in the first dryer and the operation program of the first dryer that have been set by the user of the first dryer. The second actual operation condition may each include the fabric amount of items put in each of the at least one second dryer and the operation program of the second dryer that have been set by the user of the second dryer. 
     Thus, it is possible to predict drying time according to the fabric amount of items put in the dryer and the operation program of the dryer that have been set by the user. 
     In addition, the first dryer and the at least one second dryer may be the same type of dryers. 
     This enables more accurate drying time prediction. 
     A drying time prediction system according to another aspect of the present disclosure includes: an operation condition obtainment unit that obtains an operation condition set at the start of operation of a first dryer; a first actual operation time period obtainment unit that obtains the first actual operation time period of each of at least one first operation performed by the first dryer; a learned model that outputs operation finish time information in response to input of the operation condition and first actual information that is based on the first actual operation time period, the operation finish time information being related to the finish time of the operation started under the operation condition; and an outputting unit that outputs first information based on the operation finish time information. 
     The preceding drying time prediction system can predict drying time by using the learned model pre-trained to output an operation finish time in response to input of operation conditions set at the start of operation of the first dryer and the first actual operation time period of each past operation performed by the first dryer, the operation finish time being the operation finish time of the operation started under the operation conditions. 
     A dryer according to still another aspect of the present disclosure is the first dryer using the drying time prediction system. The dryer includes: a provision unit that provides the drying time prediction system with the operation condition and the first actual operation time period; an obtainment unit that obtains the first information from the drying time prediction system; and a display that displays information on the first information. 
     The preceding dryer obtains the first information from the drying time prediction system, which enables drying time predication. 
     Hereinafter, specific examples of a drying time prediction method, a drying time prediction system, and a dryer according to some aspects of the present disclosure are described with reference to the drawings. The embodiment described herein represents a specific example of the present disclosure. Thus, for instance, the numerical values, shapes, structural elements, arrangements and connections of the structural elements, steps, and order of the steps described in the embodiment below are mere examples and are not intended to limit the present disclosure. In addition, the drawings are schematic diagrams and are not necessarily precisely drawn. 
     It should be noted that general and specific aspects of the present disclosure may be embodied as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium, such as a CD-ROM, or any combination of the system, method, integrated circuit, computer program, and recording medium. 
     Embodiment 
     [1. Configuration] 
       FIG.  3    is a block diagram illustrating a configuration example of a drying time prediction system according to an embodiment. 
     As illustrated in  FIG.  3   , drying time prediction system  1  according to the embodiment includes dryers  10 , server device  20 , smartphones  30 , and network  40 . 
     In  FIG.  3   , dryer  10 A, dryer  10 B, dryer  10 M, and other dryers correspond to dryers  10 . Hereinafter, unless the individual dryers have to be explicitly differentiated, each of dryer  10 A, dryer  10 B, dryer  10 M, and the other dryers is simply referred to as dryer  10 . Although dryers  10  described herein are the same type of dryers, dryers  10  do not necessarily have to be the same type of dryers. 
     In  FIG.  3   , smartphone  30 A, smartphone  30 B, smartphone  30 N, and other smartphones correspond to smartphones  30 . Hereinafter, unless the individual smartphones have to be explicitly differentiated, each of smartphone  30 A, smartphone  30 B, smartphone  30 N, and the other smartphones is simply referred to as smartphone  30 . 
     Network  40  relays communication between connected devices. The devices connected to network  40  include dryers  10 , server device  20 , and smartphones  30 . 
     Network  40  may be, for example, the Internet, a cellular network, or a local area network (LAN). 
       FIG.  4    is a block diagram illustrating a configuration example of dryer  10 . 
     Dryer  10  is connected to network  40  and has the function of drying clothes and other items. Dryer  10  may be, for example, a washer-dryer also having the function of washing clothes and other items. 
     As illustrated in  FIG.  4   , dryer  10  includes communication unit  100 , input receiving unit  110 , operation controller  120 , drying unit  130 , past data accumulator  140 , sensing unit  150 , provision unit  160 , obtainment unit  170 , and display  180 . 
     Communication unit  100  is connected to network  40  and communicates with external devices connected to network  40 , via network  40 . The external devices include server device  20  and smartphones  30 . 
     For instance, communication unit  100  includes an input-output interface (not illustrated) and is caused to function by the processor (not illustrated) of dryer  10  running one or more programs stored in the memory (not illustrated) of dryer  10  to control the input-output interface. 
     Drying unit  130  includes a drying chamber (not illustrated) and dries clothes and other items put in the drying chamber. 
     Drying unit  130  includes, for example, a tumble-drying drum (not illustrated), a heater (not illustrated), and a fan (not illustrated) and dries clothes and other items put in the tumble-drying drum in the following manner: the clothes and other items put in are agitated by spinning the tumble-drying drum, and at the same time, the air warmed by the heater is sent to the tumble-drying drum by using the fan 
     Drying unit  130  further includes sensors that sense the state of the drying chamber and/or the state of the clothes and other items put in the drying chamber. The sensors include, for example, a thermometer for sensing the outlet temperature of the air sent to the drying chamber, a thermometer for sensing the inlet temperature of the air sent to the outside of the drying chamber, an angular velocity meter for sensing the spin speed of the drying chamber, an accelerometer for sensing weight unbalance within the drying chamber, and a motor ammeter for sensing the fabric amount of clothes and other items put in the drying chamber. 
     Input receiving unit  110  receives input from the user of dryer  10 . The input received by input receiving unit  110  includes operation conditions at the start of operation of dryer  10 . The operation conditions include, for example, the fabric amount of clothes and other items put in dryer  10  and an operation program to operate dryer  10 . It should be noted that in the description herein, input receiving unit  110  receives the amount of fabrics from the user. However, for instance, input receiving unit  110  may receive the amount of fabrics obtained by sensing unit  150  from sensing unit  150 , which is described later. 
     For instance, input receiving unit  110  includes a touch panel (not illustrated) and is caused to function by the processor of dryer  10  running one or more programs stored in the memory of dryer  10  to control the touch panel. 
     Operation controller  120  operates dryer  10  by controlling the operation of drying unit  130  on the basis of the input received by input receiving unit  110 . 
     For instance, operation controller  120  is caused to function by the processor of dryer  10  running one or more programs stored in the memory of dryer  10 . 
     For each of past operations performed by dryer  10 , past data accumulator  140  stores actual operation conditions and an actual operation time period associated with each other. 
     For instance, past data accumulator  140  includes a hard disk (not illustrated) and is caused to function by the processor of dryer  10  running one or more programs stored in the memory of dryer  10  to control the hard disk. 
     Sensing unit  150  controls the sensors of drying unit  130  to obtain sensing information on the state of dryer  10  in operation. The sensing information includes, for example, an outlet temperature, an inlet temperature, the spin speed of the drying chamber, and weight unbalance within the drying chamber. In addition, at the start of operation of dryer  10 , sensing unit  150  may obtain the fabric amount of clothes and other items put in the drying chamber by controlling the sensors of drying unit  130 . 
     Sensing unit  150  repeatedly obtains the sensing information during the operation of dryer  10 . For instance, sensing unit  150  obtains the sensing information each minute during the period from the start of the operation of dryer  10  until the end of the operation. 
     For instance, sensing unit  150  is caused to function by the processor of dryer  10  running one or more programs stored in the memory of dryer  10 . 
     Provision unit  160  provides server device  20  with the operation conditions received by input receiving unit  110 , pairs each including actual operation conditions and an actual operation time period associated with each other that are stored in past data accumulator  140 , and the sensing information obtained by sensing unit  150 , via communication unit  100 . More specifically, every time input receiving unit  110  receives operation conditions, provision unit  160  provides server device  20  with the operation conditions. Every time past data accumulator  140  updates the pairs each including actual operation information and an actual operation time period associated with each other, provision unit  160  transmits the updated pairs each including actual operation information and an actual operation time period associated with each other to server device  20 . Every time sensing unit  150  obtains sensing information, provision unit  160  provides server device  20  with the sensing information. 
     For instance, provision unit  160  is caused to function by the processor of dryer  10  running one or more programs stored in the memory of dryer  10 . 
     Obtainment unit  170  obtains first information (described later) indicating remaining drying time from server device  20  via communication unit  100 . More specifically, every time server device  20  transmits first information, obtainment unit  170  obtains the first information. 
     For instance, obtainment unit  170  is caused to function by the processor of dryer  10  running one or more programs stored in the memory of dryer  10 . 
     Display  180  displays information on the first information obtained by obtainment unit  170 . More specifically, every time obtainment unit  170  obtains first information, display  180  displays remaining drying time indicated by the first information. 
     For instance, display  180  includes a touch panel (not illustrated) and is caused to function by the processor of dryer  10  running one or more programs stored in the memory of dryer  10  to control the touch panel. 
       FIG.  5    is a block diagram illustrating a configuration example of server device  20 . 
     Server device  20  is connected to network  40  and has the function of predicting the remaining drying time of an operation being performed by dryer  10  to dry clothes and other items. 
     As illustrated in  FIG.  5   , server device  20  includes communication unit  200 , past data accumulator  205 , sensing information obtainment unit  210 , operation condition obtainment unit  215 , first actual operation time period obtainment unit  220 , second actual operation time period obtainment unit  225 , standardization unit  230 , representative value calculator  235 , predictor  240 , and outputting unit  250 . Here, predictor  240  includes learned model  245 . 
     Communication unit  200  is connected to network  40  and communicates with external devices connected to network  40 , via network  40 . The external devices include dryers  10  and smartphones  30   
     For instance, communication unit  200  includes an input-output interface (not illustrated) and is caused to function by the processor (not illustrated) of server device  20  running one or more programs stored in the memory (not illustrated) of server device  20  to control the input-output interface. 
     Sensing information obtainment unit  210  obtains sensing information from provision unit  160  via communication unit  200 . Every time provision unit  160  provides sensing information, sensing information obtainment unit  210  obtains the sensing information. 
     For instance, sensing information obtainment unit  210  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
     Operation condition obtainment unit  215  obtains operation conditions from provision unit  160  via communication unit  200 . Every time provision unit  160  provides operation conditions, operation condition obtainment unit  215  obtains the operation conditions. 
     For instance, operation condition obtainment unit  215  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
     Past data accumulator  205  obtains actual operation conditions and an actual operation time period associated with each other from provision unit  160  via communication unit  200  and stores the actual operation conditions and the actual operation time period associated with each other. Every time provision unit  160  provides actual operation conditions and an actual operation time period associated with each other, past data accumulator  205  obtains and stores the actual operation conditions and the actual operation time period associated with each other. 
     That is, for each of past operations performed by all dryers  10  included in drying time prediction system  1 , past data accumulator  205  stores actual operation conditions and an actual operation time period associated with each other. 
     For instance, past data accumulator  205  includes a hard disk (not illustrated) and is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20  to control the hard disk. 
     For each of past operations performed by dryer  10  (hereinafter, also referred to as first dryer  10 ) during a past predetermined period, first actual operation time period obtainment unit  220  obtains actual operation conditions (hereinafter, also referred to as first actual operation conditions) and an actual operation time period (hereinafter, also referred to as a first actual operation time period) associated with each other from among pairs each including actual operation conditions and an actual operation time period associated with each other that are stored in past data accumulator  205 . 
     Here, the predetermined period may be from the date and time a predetermined number of days ago up to now or may be the same season as the current season (for instance, if it is April now, since April is spring, the predetermined period may be current and past spring (for instance, March, April, and May). That is, the predetermined period may be a period during which the types of items put in dryer  10  tend not to change significantly. 
     For instance, first actual operation time period obtainment unit  220  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
     For each past operation performed by at least any one of dryers  10  (hereinafter, also referred to as at least one second dryer  10 ), second actual operation time period obtainment unit  225  obtains actual operation conditions (hereinafter, also referred to as second actual operation conditions) and an actual operation time period (hereinafter, also referred to as a second actual operation time period) associated with each other from among the pairs each including actual operation conditions and an actual operation time period associated with each other that are stored in past data accumulator  205 . In the description herein, at least one second dryer  10  includes first dryer  10 , that is, at least one second dryer  10  includes all dryers  10 . However, at least one second dryer  10  does not have to include first dryer  10 . 
     For instance, second actual operation time period obtainment unit  225  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20   
     Standardization unit  230  calculates a standardized actual operation time period for each first actual operation time period by standardizing each first actual operation time period according to the distribution of at least one second actual operation time period associated with second actual operation conditions corresponding to first actual operation conditions associated with the first actual operation time period. 
     Here, standardization unit  230  calculates a standardized actual operation time period for each first actual operation time period, using Expression 1 described below. However, calculation does not necessarily have to be limited to the following example. 
         n =( t −mean (Tc))/std (Tc)   (Expression 1)
 
     In Expression 1, n denotes a standardized actual operation time period, t denotes a first actual operation time period, Tc denotes a set of second actual operation time periods associated with second operation conditions corresponding to first operation conditions associated with the first actual operation time period, mean (Tc) denotes the mean value of all the second actual operation time periods included in set Tc, and std (Tc) denotes the variance of all the second actual operation time periods included in set Tc. Here, all the second actual operation time periods included in set Tc may be one second actual operation time period. 
     As described in Expression 1, if the first actual operation time period is identical to mean (Tc), that is, the mean value of the actual operation time periods of operations performed by all dryers  10  under the corresponding actual operation conditions, the value of the standardized actual operation time period is zero. 
     In addition, if the first actual operation time period is greater than mean (Tc), that is, the mean value of the actual operation time periods of the operations performed by all dryers  10  under the corresponding actual operation conditions, the first actual operation time period tends to be longer than the actual operation time periods of the operations performed by all dryers  10  under the corresponding actual operation conditions. In this case, the value of the standardized actual operation time period is a positive value. 
     In addition, if the first actual operation time period is smaller than mean (Tc), that is, the mean value of the actual operation time periods of the operations performed by all dryers  10  under the corresponding actual operation conditions, the first actual operation time period tends to be shorter than the actual operation time periods of the operations performed by all dryers  10  under the corresponding actual operation conditions. In this case, the value of the standardized actual operation time period is a negative value. 
     Thus, the standardized actual operation time period calculated by standardization unit  230  reflects whether the first actual operation time period tends to be longer or shorter when seen in the distribution of at least one second actual operation time period associated with second actual operation conditions corresponding to first actual operation conditions associated with the first actual operation time period. 
       FIG.  6    schematically illustrates an example in which standardization unit  230  calculates standardized actual operation time periods. 
     The upper graph in  FIG.  6    illustrates the first actual operation time periods of operations performed by first dryer  10 . The horizontal axis denotes the date and time of each operation, and the vertical axis denotes the first actual operation time period. The lower graph in  FIG.  6    illustrates standardized actual operation time periods calculated for the respective operations performed by first dryer  10 . The horizontal axis denotes the date and time of each operation, and the vertical axis denotes the standardized actual operation time period. 
     In the upper graph, first actual operation time periods  601  to  606  are the first actual operation time periods of the operations performed by first dryer  10  during the predetermined period. 
     Here, first actual operation time period  605  is the actual operation time period of an operation performed under the actual operation conditions identical to operation conditions A (e.g., 2 kg of fabrics, an operation program for a high level of dryness). First actual operation time periods  602  and  604  are the actual operation time periods of operations performed under the actual operation conditions identical to operation conditions B (e.g., 2 kg of fabrics, an operation program for a normal level of dryness). First actual operation time periods  601  and  603  are the actual operation time periods of operations performed under the actual operation conditions identical to operations conditions C (e.g., 3 kg of fabrics, the operation program for a high level of dryness). First actual operation time period  606  is the actual operation time period of an operation performed under the actual operation conditions identical to operation conditions D (e.g., 3 kg of fabrics, the operation program for a normal level of dryness). 
     Standardization unit  230  standardizes first actual operation time period  605  according to the distribution of at least one second actual operation time period associated with the actual operation conditions identical to operation conditions A to calculate standardized actual operation time period  655 . Standardization unit  230  standardizes each of first actual operation time periods  602  and  604  according to the distribution of at least one second actual operation time period associated with the actual operation conditions identical to operation conditions B to calculate standardized actual operation time periods  652  and  654 . Standardization unit  230  standardizes each of first actual operation time periods  601  and  603  according to the distribution of at least one second actual operation time period associated with the actual operation conditions identical to operation conditions C to calculate standardized actual operation time periods  651  and  653 . Standardization unit  230  standardizes first actual operation time period  606  according to the distribution of at least one second actual operation time period associated with the actual operation conditions identical to operation conditions D to calculate standardized actual operation time period  656 . 
     In the example illustrated in  FIG.  6   , the values of standardized actual operation time periods  651  to  654  and standardized actual operation time period  656  are smaller than zero. Thus, each of first actual operation time periods  601  to  604  and first actual operation time period  606  tends to be shorter than the actual operation time periods of operations performed by all dryers  10  under the corresponding actual operation conditions. On the other hand, the value of standardized actual operation time period  606  is greater than zero. Thus, first actual operation time period  605  tends to be longer than the actual operation time periods of operations performed by all dryers  10  under the corresponding actual operation conditions. 
     Once again with reference to  FIG.  5   , a continued description of server device  20  is provided. 
     For instance, standardization unit  230  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
     Representative value calculator  235  calculates the representative value of standardized actual operation time periods calculated by standardization unit  230 . Here, the representative value of the standardized actual operation time periods means the representative value of at least one standardized actual operation time period. 
     Here, the representative value of the standardized actual operation time periods, calculated by representative value calculator  235  is described as the mean value of the standardized actual operation time periods. That is, in the description herein, representative value calculator  235  calculates the mean value of the standardized actual operation time periods calculated by standardization unit  230 . However, the representative value of the standardized actual operation time periods, calculated by representative value calculator  235  does not necessarily have to be the mean value of the standardized actual operation time periods. For instance, the representative value of the standardized actual operation time periods, calculated by representative value calculator  235  may be the maximum, minimum, median, or mode (most frequent) value of the standardized actual operation time periods. 
     The mean value of the standardized actual operation time periods, calculated by representative value calculator  235  reflects whether the actual operation time periods of operations performed by first dryer  10  under all the actual operation conditions during the past predetermined period tend to be longer or shorter. 
     For instance, representative value calculator  235  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
     Learned model  245  is a machine learning model pre-trained to output operation finish time information in response to input of operation conditions, sensing information, the mean value of standardized actual operation time periods, the operation finish time information being related to the operation finish time of an operation stared under the operation conditions. Here, the operation finish time information is described as the remaining drying time until the end of the operation started under the operation conditions. 
     For instance, learned model  245  is created by a neural network built on server device  20  by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
     Learned model  245  is created by, for example, training a machine learning model by using, as input data, operation conditions, sensing information, and the mean value of standardized actual operation time periods and, as labeled data, remaining drying time periods pre-created according to actual values obtained through actual operation of dryer  10 . 
     Predictor  240  obtains the operation finish time information from learned model  245  by inputting, to learned model  245 , the operation conditions obtained by operation condition obtainment unit  215 , the sensing information obtained by sensing information obtainment unit  210 , and the mean value calculated by representative value calculator  235 . More specifically, every time sensing information obtainment unit  210  obtains sensing information, predictor  240  inputs the operation conditions, the sensing information, and the mean value to learned model  245  to obtain operation finish time information from learned model  245 . 
     Then, every time predictor  240  obtains operation finish time information, predictor  240  calculates, according to the obtained operation finish time information, first information indicating the remaining drying time of the operation being performed by first dryer  10  to dry clothes and other items. 
     For instance, predictor  240  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
     Outputting unit  250  outputs the first information calculated by predictor  240  to first dryer  10  and smartphone  30  associated with first dryer  10  via communication unit  200 . More specifically, every time predictor  240  calculates first information, outputting unit  250  transmits the first information to first dryer  10  and smartphone  30  associated with first dryer  10  via communication unit  200 . 
     For instance, outputting unit  250  is caused to function by the processor of server device  20  running one or more programs stored in the memory of server device  20 . 
       FIG.  7    is a block diagram illustrating a configuration example of smartphone  30 . 
     Smartphone  30  is connected to network  40  and associated with at least one of dryers  10  and has the function of displaying information transmitted by server device  20 . 
     As illustrated in  FIG.  7   , smartphone  30  includes communication unit  300 , obtainment unit  310 , and display  320 . 
     Communication unit  300  is connected to network  40  and communicates with external devices connected to network  40 , via network  40 . The external devices include dryers  10  and server device  20 . 
     For instance, communication unit  300  includes an input-output interface (not illustrated) and is caused to function by the processor (not illustrated) of smartphone  30  running one or more programs stored in the memory (not illustrated) of smartphone  30  to control the input-output interface. 
     Obtainment unit  310  obtains first information from server device  20  via communication unit  300 . More specifically, every time server device  20  transmits first information, obtainment unit  310  obtains the first information. 
     For instance, obtainment unit  310  is caused to function by the processor of smartphone  30  running one or more programs stored in the memory of smartphone  30 . 
     Display  320  displays information on the first information obtained by obtainment unit  310 . More specifically, every time obtainment unit  310  obtains first information, display  320  displays remaining drying time indicated by the first information. 
     For instance, display  320  includes a touch panel (not illustrated) and is caused to function by the processor of smartphone  30  running one or more programs stored in the memory of smartphone  30  to control the touch panel. 
     [2. Operation] 
     Hereinafter, operation of drying time prediction system  1  having the above configuration is described. 
     Drying time prediction system  1  implements drying time prediction processing for predicting the drying time of a drying operation being performed by first dryer  10  to dry clothes and other items put in first dryer  10 . 
       FIG.  8    is a flowchart illustrating the drying time prediction processing. 
     The drying time prediction processing is started by first dryer  10  starting a drying operation. 
     When first dryer  10  starts a drying operation, provision unit  160  of first dryer  10  provides server device  20  with operation conditions received by input receiving unit  110  of first dryer  10 . Operation condition obtainment unit  215  then obtains the operation conditions (step S 5 ). 
     When operation condition obtainment unit  215  obtains the operation conditions, first actual operation time period obtainment unit  220  checks whether there are pairs each including first actual operation conditions and a first actual operation time period associated with each other for operations performed by first dryer  10  during a past predetermined period among pairs each including actual operation conditions and an actual operation time period associated with each other that are stored in past data accumulator  205  (step S 10 ). 
     In step S 10 , if there are one or more applicable pairs each including first actual operation conditions and a first actual operation time period (Yes in step S 10 ), drying time prediction system  1  implements standardization processing for each first actual operation time period to standardize the first actual operation time period according to the distribution of at least one second actual operation time period associated with second actual operation conditions corresponding to first actual operation conditions associated with the first actual operation time period (step S 15 ). 
       FIG.  9    is a flowchart illustrating the standardization processing. 
     When the standardization processing is started, first actual operation time period obtainment unit  220  obtains first actual operation conditions and a first actual operation time period associated with each other for each operation performed by first dryer  10  during the past predetermined period from among the pairs each including actual operation conditions and an actual operation time period associated with each other that are stored in past data accumulator  205  (step S 110 ). In addition, second actual operation time period obtainment unit  225  obtains second actual operation conditions and a second actual operation time period associated with each other for each operation performed by at least one second dryer  10  during the past predetermined period from among the pairs each including actual operation conditions and an actual operation time period associated with each other that are stored in past data accumulator  205 . 
     After step S 110  is complete, or if in step S 170 , which is described later, there are one or more first actual operation time periods yet to be selected (Yes in step S 170 ), first actual operation time period obtainment unit  220  selects a first actual operation time period yet to be selected (step S 120 ). Here, the one or more first actual operation time periods yet to be selected are one or more first actual operation time periods yet to be selected in the loop processing created by selecting Yes in steps S 120  to S 170 , among the first actual operation time periods obtained in step S 110 . 
     When a first actual operation time period yet to be selected is selected, standardization unit  230  checks whether there are second actual operation time periods associated with second actual operation conditions corresponding to first actual operation conditions associated with the selected first actual operation time period, among the second actual operation time periods obtained by second actual operation time period obtainment unit  225  (step S 130 ). 
     In step S 130 , if there is at least one applicable second actual operation time period (Yes in step S 130 ), standardization unit  230  obtains the at least one applicable second actual operation time period (step S 140 ) and standardizes the selected first actual operation time period according to the distribution of the at least one applicable second actual operation time period to calculate a standardized actual operation time period (step S 150 ). 
     In step S 130 , if there are no applicable second actual operation time periods (No in step S 130 ), standardization unit  230  determines a predetermined default value as a standardized actual operation time period (step S 160 ). 
     After step S 150  or S 160  is complete, first actual operation time period obtainment unit  220  checks whether there are first actual operation time periods yet to be selected (step S 170 ). 
     In step S 170 , if there are one or more first actual operation time periods yet to be selected (Yes in step S 170 ), the processing of drying time prediction system  1  returns to step S 120 . 
     In step S 170 , if there are no first actual operation time periods yet to be selected (No in step S 170 ), drying time prediction system  1  ends the standardization processing. 
     Once again with reference to  FIG.  8   , a continued description of the drying time prediction processing is provided. 
     After the standardization processing is complete, representative value calculator  235  calculates the mean value of standardized actual operation time periods calculated in the standardization processing (step S 20 ). 
     In step S 10 , if there are no applicable pairs each including first actual operation conditions and a first actual operation time period (No in step S 10 ), representative value calculator  235  determines a predetermined default value as the mean value (step S 25 ). 
     After step S 20  or S 25  is complete, sensing information obtainment unit  210  checks whether sensing information has been received from provision unit  160  of first dryer  10  (step S 30 ). 
     In step S 30 , if sensing information is not provided (No in step S 30 ), sensing information obtainment unit  210  waits for sensing information to be provided (No is repeated in step S 30 ). 
     In step S 30 , if sensing information is provided (Yes in step S 30 ) or if new sensing information is provided in step S 60 , which is described later (Yes in step S 60 ), sensing information obtainment unit  210  obtains the sensing information (step S 35 ). 
     When sensing information obtainment unit  210  obtains the sensing information, predictor  240  inputs, to learned model  245 , the operation conditions obtained in step S 5 , the mean value calculated in step S 20 , and the sensing information obtained in step S 35  (step S 40 ) to obtain operation finish time information from learned model  245  (step S 45 ). 
     When obtaining the operation finish time information, predictor  240  calculates first information indicating the remaining drying time of the operation being performed by first dryer  10  to dry clothes and other items. Outputting unit  250  then outputs the first information indicating the remaining drying time to first dryer  10  and smartphone  30  associated with first dryer  10  (step S 50 ). 
     When outputting unit  250  outputs the first information, obtainment unit  170  of first dryer  10  obtains the first information, and display  180  of first dryer  10  displays the remaining drying time indicated by the first information (step S 55 ). In addition, obtainment unit  310  of smartphone  30  associated with first dryer  10  obtains the first information, and display  320  of smartphone  30  associated with first dryer  10  displays the remaining drying time indicated by the first information. 
     Meanwhile, sensing information obtainment unit  210  checks whether new sensing information has been received from provision unit  160  of first dryer  10  (step S 60 ). 
     In step S 60 , if new sensing information is not provided (No in step S 60 ), unless the operation of first dryer  10  is complete (No in step S 65 ), sensing information obtainment unit  210  waits for new sensing information to be provided (No is repeated in step  60  and in step S 65 ). 
     In step S 60 , if new sensing information is provided (Yes in step S 60 ), the processing of drying time prediction system  1  returns to step S 35 . 
     While waiting for new sensing information to be provided, if the operation of first dryer  10  is complete (Yes in step S 65 ), drying time prediction system  1  ends the drying time prediction processing. 
     [3. Discussion] 
     As illustrated in the upper graph in  FIG.  6   , in many cases, operations performed by first dryer  10  during a past predetermined period are operations performed under various actual operation conditions. In addition, the actual operation time periods of operations started under different actual operation conditions usually vary. Thus, just calculating the mean value of the actual operation time periods of operations performed by first dryer  10  during a past predetermined period may not be enough in order for the mean value to sufficiently reflect whether the operations performed by first dryer  10  during the past predetermined period tend to take longer or shorter. 
     In addition, as illustrated in the upper graph in  FIG.  6   , the number of operations performed by first dryer  10  under particular actual operation conditions during a past predetermined period is usually limited. Thus, the statistically effective number of operations may not be performed. That is, just calculating the mean value of the actual operation time periods of the operations performed by first dryer  10  under the particular actual operation conditions during the past predetermined period may not be enough for the mean value to sufficiently reflect whether the operations performed by first dryer  10  during the past predetermined period tend to take longer or shorter. 
     In contrast, in drying time prediction system  1  having the above configuration, as described above, the mean value of standardized actual operation time periods, calculated by representative value calculator  235  reflects whether the actual operation time periods of operations performed by first dryer  10  under all the actual operation conditions during a past predetermined period tend to be longer or shorter. 
     Accordingly, drying time prediction system  1  can predict drying time more accurately than conventional methods. 
     (Supplement) 
     Thus, the drying time prediction system according to one aspect of the present disclosure is described on the basis of the embodiment. However, the present disclosure is not limited to the description in the embodiment. One or more embodiments of the present disclosure may include an embodiment obtained by making various changes envisioned by those skilled in the art to the embodiment and an embodiment obtained by combining structural elements described in different embodiments unless such embodiments do not depart from the scope of the present disclosure. 
     (1) In the embodiment, server device  20  includes predictor  240 . However, as long as it is possible to obtain a function similar to that of drying time prediction system  1 , server device  20  does not necessarily include predictor  240 . For instance, one of dryers  10  or one of smartphones  30  may include predictor  240 . 
     Likewise, as long as it is possible to obtain a function similar to that of drying time prediction system  1 , for instance, at least one of dryers  10  or at least one of smartphones  30  may include at least a part of the structural elements of server device  20 . In addition, for instance, server device  20  or at least one of smartphones  30  may include at least a part of the structural elements of dryer  10 . Furthermore, for instance, server device  20  or at least one of dryers  10  may include at least a part of the structural elements of smartphone  30 . 
     (2) In the embodiment, drying time prediction system  1  includes dryers  10 . In contrast, in another configuration example, dryers  10  may be external devices for drying time prediction system  1 . In this case, each of dryers  10  provides drying time prediction system  1  with operation conditions received by input receiving unit  110 , pairs each including actual operation conditions and an actual operation time period associated with each other that are stored in past data accumulator  140 , and sensing information obtained by sensing unit  150  to obtain first information indicating remaining drying time from drying time prediction system  1 . 
     (3) In the embodiment, drying time prediction system  1  includes smartphones  30 . However, smartphones  30  do not necessarily have to be the essential structural elements of drying time prediction system  1 . In another configuration example, drying time prediction system  1  may not include smartphones  30 . 
     (4) In the embodiment, learned model  245  is a machine learning model pre-trained to output operation finish time information in response to input of operation conditions, sensing information, and the mean value of standardized actual operation time periods, the operation finish time information being related to the finish time of an operation started under the operation conditions. In contrast, in another configuration example, learned model  245  may be a machine learning model pre-trained to output operation finish time information in response to input of operation conditions, sensing information, and standardized actual operation time periods, the operation finish time information being related to the finish time of an operation started under the operation conditions. In this case, predictor  240  obtains the operation finish time information from learned model  245  by inputting, to learned model  245 , the operation conditions obtained by operation condition obtainment unit  215 , the sensing information obtained by sensing information obtainment unit  210 , and the standardized actual operation time periods calculated by standardization unit  230 . In still another configuration example, learned model  245  may be a machine learning model pre-trained to output operation finish time information in response to input of operation conditions and standardized actual operation time periods, the operation finish time information being related to the finish time of an operation started under the operation conditions. In this case, predictor  240  obtains the operation finish time information from learned model  245  by inputting, to learned model  245 , the operation conditions obtained by operation condition obtainment unit  215  and the standardized actual operation time periods calculated by standardization unit  230 . 
     (5) In the embodiment, predictor  240  includes one learned model  245 . In contrast, in another configuration example, predictor  240  may include learned models  245 . For instance, predictor  240  may include pre-trained learned models  245  appropriate to the elapsed time since the start of operation of dryer  10 . In this case, predictor  240  selects appropriate learned model  245  according to the elapsed time since the start of operation of first dryer  10  from among learned models  245  and inputs, to selected learned model  245 , operation conditions obtained by operation condition obtainment unit  215 , sensing information obtained by sensing information obtainment unit  210 , and a mean value calculated by representative value calculator  235 . In this manner, predictor  240  obtains operation finish time information from selected learned model  245 . 
     (6) The present disclosure can be embodied not only as a system or a device but also as a method including, as steps, processing performed by the elements of the system or the device, a program run by a computer to implement the steps, a computer-readable recording medium storing the program, such as CD-ROM, or information, data, or a signal indicating the program. The program, information, data, and signal may be distributed via a communication network, such as the Internet. 
     INDUSTRIAL APPLICABILITY 
     The present disclosure can be widely used in, for example, a system and a device for predicting drying time.