Patent ID: 12217619

DESCRIPTION OF EMBODIMENT

Hereinafter, an embodiment according to an aspect of the present invention (which will hereinafter be stated as “the present embodiment”) will be described based on the drawings. In the following embodiment, parts having the same number are considered to perform similar operation, and therefore duplicate description thereof will be omitted.

[Overview]

First, usingFIG.1, an overview of a habit improving device of the present invention will be described.FIG.1schematically illustrates a habit improving device100according to an example of the overview, an activity amount meter110, a wristwatch-type wearable terminal120, a server130, a network140, and a GPS satellite150, as an example. The activity amount meter110measures an activity amount of a user while it is carried (or worn) by the user. For example, it calculates the activity amount by detecting change in movement or posture of a human body and identifying various activities of the user using an acceleration sensor and/or an air pressure sensor.

The activity amount is an amount of consumed energy, that is, the sum of energy consumed by the user during a certain period (for example, one day). The amount of consumed energy corresponds to the total energy consumption of the user during a certain period and is indicated by the sum of activity energy, basal metabolism, and diet-induced thermogenesis, for example. Activity energy is energy consumed through physical activities or exercise performed by the user. Basal metabolism is energy necessary for maintenance of life (maintaining the body temperature, moving the heart, and the like) and is energy consumed even if the user does not move his/her body. In addition, diet-induced thermogenesis is energy consumed due to activities of digestion, absorption, or the like of food after meals. For example, activity energy is calculated based on the product of a METs value, a weight, an exercise time, and a constant. A METs value is a value indicating a multiple of the amount of consumed energy through exercise with respect to that during rest and is indicated in units of METs. In addition, the METs value is determined by being subdivided for each of exercise and activities.

However, here, the activity amount may be defined as any one or more of activity energy, basal metabolism, and diet-induced thermogenesis and may adopt only activity energy or only activity energy and basal metabolism, for example. Moreover, since good approximation is achieved even if only the number of steps is adopted simply as the activity amount, the activity amount may be the number of steps.

The activity amount meter110measures any one or more of the amount of consumed energy or activity energy, basal metabolism, and diet-induced thermogenesis. However, for example, it may measure the number of steps, the number of fast steps, the number of steps during slow walking, or the number of steps of stair-climb or may simply measure only the number of steps. The wristwatch-type wearable terminal120can also measure an activity amount similar to that of the activity amount meter110.

In addition, the habit improving device100totals a distribution pattern of the user for each of first periods (for example, one hour) and calculates a distribution pattern throughout a second period (for example, one day) larger than this period based on the activity amount acquired from the activity amount meter110and/or the wristwatch-type wearable terminal120. Further, the habit improving device100sorts the calculated distribution pattern into a plurality of groups. Examples of a technique of sorting of groups include clustering and hierarchy sorting, but any technique can be adopted as long as grouping can be performed. After grouping is performed, the habit improving device100calculates a target pattern that becomes a target of the user for each of the groups based on attribute information of the user and external factor information on the user. Further, the habit improving device100generates advice information for the user by comparing the target pattern and a current distribution pattern to each other and presents the result to a user.

A distribution pattern is expressed as a pattern distributed in a two-dimensional space in which the horizontal axis indicates time and the vertical axis indicates activity amount. For example, the number of degrees indicating, for each of the first periods, the sum total of the activity amount within this period is associated with the time and is displayed in the two-dimensional space by the second period. Regarding a specific example, a distribution pattern is a pattern in which the sum total amount (every hour) of the activity amount measured during a period from when the user has woken up to bedtime is expressed with one bar graph and as many bar graphs as the wake-up periods are arranged. That is, a distribution pattern need only express change over time of the sum total of the activity amount for each certain period.

A program executed by the habit improving device100in order to calculate the target pattern for each of the groups and present the advice information by calculating the distribution pattern and grouping the distribution pattern is acquired from the server130providing this program via the network140, for example. The attribute information of the user may also be acquired from the server130having corresponding attribute information via the network140, and the user may input the attribute information using an input unit of the habit improving device100. This program may also be naturally stored in the habit improving device100in advance. In addition, external factor information is information other than information relating to the user himself/herself and is information relating to an external factor that is a factor affecting the user. Examples thereof include positional information, weather information, and schedule information of the user. The habit improving device100also acquires the external factor information from the server130via the network140. The positional information of the user is calculated when the habit improving device100receives a signal from the GPS satellite150, but the habit improving device100may receive information from a base station and/or a wireless LAN access point and the positional information may be corrected in accordance with this information.

When the activity amount detected by the activity amount meter110and/or the wristwatch-type wearable terminal120can be detected by the habit improving device100alone, the activity amount detected by the habit improving device100may be used without using the activity amount meter110or the like. In this case, the habit improving device100includes a device part for calculating the activity amount of the activity amount meter110and/or the wristwatch-type wearable terminal120. For example, since the habit improving device100is a wearable terminal device (for example, a smartphone), it generally includes an acceleration sensor and a pressure sensor so that the number of steps and the number of stairs can be measured, and thus the activity amount based on the number of steps and the number of stairs can be calculated.

As described above, according to the habit improving device of the present embodiment, the user can be informed of advice for coming closer to having ideal activity habits (for example, walking habits) also taking the external factor into account, and it is expected that the user easily behaves such that activity habits are executed. In addition, according to this habit improving device, a more detailed target pattern for each of the first periods for an individual can be calculated by sorting the activity amounts for each of the first periods through clustering or the like. In addition, it is possible to have an effect on presenting a target or health by taking an external factor with respect to the user into account. Moreover, instead of performing judgement simply based on only the average value of the activity amounts as in technology in the related art, since the pattern of the activity amount is grouped, the habit improving device of the present embodiment can find an abnormality in the current distribution pattern compared to a regular pattern, can inform the user of necessity of a different target setting, and can also present the advice information for setting this target.

[Example of Constitution]

(Constitution of Hardware)

<Habit Improving Device>

Next, usingFIG.2, an example of a constitution of hardware of the habit improving device100according to the present embodiment will be described.

As illustrated inFIG.2, the habit improving device100according to the present embodiment includes a computer in which a communication interface201, a storage unit202, an input device203, an output device204, a control unit205, a timepiece device206, a power source unit207, a GPS reception unit208, and an external interface209are electrically connected to each other. The habit improving device100according to the present embodiment corresponds to “the habit improving device” of the present invention. InFIG.2, the communication interface and the external interface are stated as “a communication I/F” and “an external I/F”, respectively.

For example, the communication interface201is a short-range wireless communication (for example, Bluetooth (registered trademark)) module, a wired local area network (LAN) module, a wireless LAN module, or the like and is an interface for performing wired communication or wireless communication via a network. The communication interface201is an interface for connecting the habit improving device100to an external device (for example, a computer or communication equipment on a network). The communication interface201is controlled by the control unit205to receive activity amount information from the activity amount meter110and/or the wristwatch-type wearable terminal120. Furthermore, the external factor information, the attribute information of the user, and/or the program executed by the habit improving device100are downloaded from the server130or the like via the network140.

Communication via this network may be either wireless communication or wired communication. The communication interface201may be able to transmit information to an external device via a network. A network may be an internetwork including the Internet, may be a network of other kinds such as an in-hospital LAN, or may be one-to-one communication using a universal serial bus (USB) cable or the like. The communication interface201may include a micro-USB connector.

The storage unit202is a medium for storing information such as a recorded program due to electrical action, magnetic action, optical action, mechanical action, or chemical action such that a computer, other devices, a machine, or the like can read information such as the program. For example, the storage unit202is an auxiliary storage device such as a hard disk drive, a solid-state drive, or the like and stores the activity amount information acquired from the activity amount meter110and/or the wristwatch-type wearable terminal120, pattern information (for example, distribution pattern information and target pattern information) calculated by the control unit205, the attribute information of the user input from the input device203or acquired via the network140, the external factor information of the user acquired via the network140, and/or a lifestyle habits improvement presenting execution program executed by the control unit205for presenting information for improvement in lifestyle habits to the user based on the acquired activity amount information. Regarding the activity amount information acquired by the activity amount meter110or the like, all the activity information from a certain time in the past may be stored in the storage unit202. In addition, the storage unit202may also store group information relating to sorted groups obtained from the distribution pattern. Moreover, the advice information may be stored by being associated with a difference pattern calculated by the execution program. This advice information and the difference pattern may be associated with each other in a table.

The input device203is a device for receiving an input, and examples thereof include a touch panel, a physical button, a mouse, and a keyboard. The output device204is a device for performing outputting and outputs information by display, audio, or the like. Examples thereof include a display and a speaker.

The control unit205includes a central processing unit (CPU), a random access memory (RAM), a read-only memory (ROM), and the like and controls each of the constituent elements in accordance with information processing. The execution program for presenting improvement of lifestyle habits to the user based on the acquired activity amount information is stored in the storage unit202, and the control unit205reads out the execution program from the storage unit202and executes processing. In addition, the control unit205performs distance measurement computation based on a GPS signal received via the GPS reception unit208(which will be described below) and calculates current positional information of the habit improving device100, that is, a position of a person to be measured (user) wearing the activity amount meter110or the wristwatch-type wearable terminal120.

The timepiece device206is a device for measuring a time and can measure the date and the time. For example, the timepiece device206is a timepiece including a calendar and gives information of current year and month and/or date and time to the control unit205.

The power source unit207may be anything as long as it can supply electricity. Examples thereof include a rechargeable secondary battery or an AC power source which can be acquired from an ordinary power outlet. The power source unit207supplies electricity to each of the elements mounted in a main body of the habit improving device100. For example, the power source unit207supplies electricity to the communication interface201, the storage unit202, the input device203, the output device204, the control unit205, the timepiece device206, the GPS reception unit208, and the external interface209.

The GPS reception unit208receives GPS signals respectively transmitted from a plurality of GPS satellites and outputs received GPS signals to the control unit205. The GPS reception unit208may be included in the activity amount meter110and/or the wristwatch-type wearable terminal120instead of the habit improving device100. These may acquire a GPS signal, the habit improving device100may receive a GPS signal via the communication interface201, and the control unit205may calculate the positional information. In addition, the activity amount meter110and/or the wristwatch-type wearable terminal120may receive a GPS signal, calculate the positional information, and transmit the positional information to the habit improving device100.

The external interface209is an interface for mediating between the main body of the habit improving device100and outside. For example, it is a USB port or the like and is an interface for connection to an external device (for example, a memory or communication equipment). For example, the external interface209is an interface for connection to an external device such as an acceleration sensor, a gyro-sensor, and/or a magnetic field sensor.

(Constitution of Software)

<Presenting Lifestyle Habits Improvement Plan of Habit Improving Device100>

Next, usingFIG.3, an example of a constitution of software of the habit improving device100according to the present embodiment will be described.FIG.3illustrates a constitution of software executed by the control unit205of the habit improving device100for sorting the distribution pattern calculated based on the activity amount of the user into groups, calculating the target pattern for each of the groups, and executing a program for presenting the advice information to the user based on the target pattern and the current distribution pattern.

When a necessary program is executed, the control unit205of the habit improving device100deploys the execution program stored in the storage unit202for presenting the advice information relating to improvement in lifestyle habits to the user in the RAM based on the acquired activity amount information. Further, the control unit205controls each of the constituent elements by interpreting and executing the execution program deployed in the RAM for presenting the advice information relating to improvement in lifestyle habits to the user using the CPU. In this manner, as illustrated inFIG.3, the habit improving device100according to the present embodiment includesan activity amount acquisition unit301, a distribution pattern calculation unit302, a group sorting unit303, an external factor information acquisition unit304, an attribute information acquisition unit305, a target pattern calculation unit306, and an advice presenting unit307.

The activity amount acquisition unit301acquires desired activity amount information of the user from the external activity amount meter110or the like via the communication interface201. The activity amount acquisition unit301acquires the activity amount information acquired by an external activity amount sensor via the communication interface201for each of certain periods. For example, a certain period is every hour. However, the activity amount acquisition unit301may acquire the activity amount information by a shorter period, such as every minute or the like. In addition, the external activity amount sensor is the activity amount meter110, the wristwatch-type wearable terminal120, or the like. However, the habit improving device100may include these devices, and the activity amount acquisition unit301may acquire the activity amount information from these devices.

The distribution pattern calculation unit302totals (aggregates) the activity amount acquired by the activity amount acquisition unit301for each of the first periods and calculates the distribution pattern in which the first periods are united to form the second period. Hereinafter, a case in which the first period is one hour and the second period is one day will be described as a main example. When the activity amount is the number of steps, the activity amount acquisition unit301calculates the sum of the number of steps made by the user every hour in the time zone thereof. For example, the distribution pattern calculation unit302calculates this number of steps as much as 24 hours and obtains bar graphs having the horizontal axis as time and the vertical axis as number of steps. In this example, the distribution pattern calculation unit302obtains as many bar graphs as the number of steps for 24 (including zero steps) corresponding to the time zone as a distribution pattern for one day. The distribution pattern calculation unit302calculates the distribution pattern for a plurality of second periods and obtains a plurality of distribution patterns. For example, the distribution pattern calculation unit302calculates the distribution pattern for every day. However, when activity amount information in the past is present in the storage unit202at the time when the habit improving device100has started, the distribution pattern calculation unit302may go back to the past and calculate the distribution pattern until the present or may calculate the distribution pattern for each of the first periods.

The group sorting unit303sorts a plurality of distribution patterns calculated by the distribution pattern calculation unit302into one or more groups. The group sorting unit303sorts the groups such that a different group has a different feature. Regarding a technique of performing sorting, the group sorting unit303can adopt clustering, hierarchy sorting, or the like, and a technique of performing sorting is not particularly limited. A more specific example will be described below with reference toFIGS.7A,7B, and7C.

When the group sorting unit303sorts the groups through clustering, for example, a 24-dimensional space having 24 axes as axes indicating the number of steps for every hour is prepared. The group sorting unit303sorts the groups using a space in which the axes correspond to the number of steps of the user for every hour from 0:00 to 24:00. Moreover, the group sorting unit303calculates a position of one point within the space corresponding to the distribution pattern for every day and similarly calculates positions of a plurality of points corresponding to a plurality of calculated distribution patterns. For this reason, the distribution pattern calculation unit302obtains as many points as the number of distribution patterns within the space. The group sorting unit303performs clustering of the plurality of points into one or more groups based on the positions of the points distributed within the space. As a result, a clustering unit can sort the distribution pattern into groups assumed to have a similar distribution tendency. In addition, regarding another expression, a vector having the number of steps of the user for every hour as a component may be considered, and the points within the space laid in a basis determined from the component may correspond to this vector. For example, this vector indicates a position vector, and a starting point of the position vector corresponds to an origin at which the number of steps is zero in all the periods for every hour included in one day, and an ending point of the position vector corresponds to one distribution pattern.

When the group sorting unit303sorts the groups through hierarchy sorting, for example, the group sorting unit303sorts the groups based on whether the activity amount in a particular time zone is equal to or larger than a threshold. As an example, sorting of two groups is performed whether the activity amount is equal to or larger than the threshold in a time zone from 23:00 at late night until 2:00 before dawn of the next day. The threshold is provided for each of a plurality of time zones, the groups are divided whether the activity amount is equal to or larger than the threshold, and a plurality of groups is generated. Furthermore, the group sorting unit303may provide a plurality of thresholds for the same time zone, and grouping may be performed for three or more groups at this time zone based on the threshold. For example, the group sorting unit303performs sorting of three groups such as a group of steps fewer than 500 steps during a period from 10 a.m. to 12 a.m., a group of steps within a range of 500 steps to fewer than 1,000 steps, and a group of steps more than 1,000 steps. In this case, there are two thresholds for 500 steps and 1,000 steps.

The external factor information acquisition unit304acquires the external factor information relating to an external factor that is not directly related to the user, is information other than the attribute information of the user, and is a factor affecting the user from the network140or the like via the communication interface201. Examples of the external factor information include positional information indicating a current location of the user, the weather forecast information based on the positional information (for example, weather conditions for every hour from then on), and schedule information of the user (for example, a schedule for every hour from then on). In the present embodiment, this external factor information is information used for performing calculation by associating the target pattern that becomes a target for the user with the groups. In addition, positional history information in which history of this location is recorded may be included in addition to the positional information. Easiness of performing activities may be recorded or predicted in accordance with particular places and this may be included in the external factor information. The external factor information may be any information as long as it is an external factor affecting the user, and it is not limited to the matters described herein. In addition, the external factor information may be utilized for the advice presenting unit307to present appropriate advice information.

The attribute information acquisition unit305acquires the attribute information that is information relating to an attribute of the user from the storage unit202. In addition, the attribute information is information used when a target pattern of the user is calculated. Examples thereof include physical information (for example, a gender, a height, and a weight) necessary when the activity amount of the user is measured, residence information for being associated with an external factor, occupation information such as occupation and a workplace, information regarding hobbies relating to activities, and information relating to favorite food and drink utilized when eating out. In addition, the attribute information may be utilized by the advice presenting unit307presenting appropriate advice information.

The target pattern calculation unit306calculates the target pattern that becomes a target of the user based on the attribute information and the external factor information for each of the groups sorted by the group sorting unit303. The target pattern calculation unit306calculates a target pattern desirable for the user in accordance with the attribute information of the user and an external factor affecting the user while indirectly reflecting the features of the groups. Moreover, the target pattern calculation unit306may calculate a target pattern from then on with reference to the distribution pattern which is currently in progress and is not completed yet and/or the distribution pattern before the preceding day, and the target pattern (and/or the advice information).

The advice presenting unit307generates the advice information for the user and gives it to the output device204based on the distribution pattern which is acquired from the distribution pattern calculation unit302and is currently in progress, and the target pattern corresponding to the distribution pattern currently in progress of the target pattern calculated for each of the groups by the target pattern calculation unit306. The advice information is generated every time a target pattern is calculated and is generated every time a target pattern is changed. In addition, the latest advice information may be stored in the storage unit202at all times such that the advice information can be presented to the user based on the target pattern which is effective at that time when the user performs a predetermined operation. When the advice information is received from the advice presenting unit307, the output device204presents this information to the user. A pattern which is being currently acquired has a content corresponding to the target pattern, and each of the first period and the second period in which the distribution pattern is acquired has the same length as the target pattern, and the kind of the activity amount is also the same as each other. Examples of the kind of the activity amount include the amount of consumed energy, activity energy, and the number of steps. The advice presenting unit307stores the distribution pattern and the target pattern during the second period in the storage unit202as history information. Moreover, the advice presenting unit307may also store the advice information in the storage unit202in association with the distribution pattern and the target pattern. In addition, the difference pattern of the distribution pattern and the target pattern and the advice information may be stored in the storage unit202in association with each other (for example, as a table).

<Others>

Operation of the habit improving device100will be described in detail in an operational example which will be described below. In the present embodiment, all the control units205of the habit improving device100may be realized by a general-purpose CPU. However, a portion or all of the foregoing operation (or functions) may be realized by one or a plurality of dedicated processors. In addition, regarding a constitution of the habit improving device100, omission, replacement, and addition may be suitably performed in accordance with the embodiment.

Next, usingFIG.4, an example of behavior of the user in one day when the habit improving device100is used, and the advice information of the habit improving device100will be displayed and described.FIG.4is a time-chart schematically showing an example of behavior of the user in one day and an example of operation and display of the habit improving device100.

(0:00 a.m.) The user is scheduled to go to bed before 0:00 a.m. and wake up at 8:00 a.m. The user wears the activity amount meter110and/or the wristwatch-type wearable terminal120during sleep so that this equipment detects the activity amount, and the activity amount acquisition unit301of the habit improving device100acquires this activity amount information. In addition, the activity amount meter110and the wristwatch-type wearable terminal120may be turned off so that the user may not wear these at the time of sleeping, and the habit improving device100may also be turned off.

(8:00 a.m.; waking up in the morning) The user wakes up at 8:00 a.m. Thereafter, the user inputs the attribute information of the user, the external factor information, the schedule information, and the like to the habit improving device100using the input device203. However, in the foregoing information, information which can be acquired in advance (for example, the attribute information of the user and the schedule information) may be stored in the storage unit202of the habit improving device100in advance by the user. In addition, after the user has woken up, the output device204of the habit improving device100displays a daily target pattern of today for the user. The target pattern is calculated by the control unit205of the habit improving device100based on the activity amount before waking up in the morning. Particularly, when there is the advice information associated with the target pattern, the output device204displays the advice information. In the example ofFIG.4, the target pattern is displayed together with a wording of “this for today”. Naturally, a wording may be output by the output device204in audio. In addition, immediately after waking up or after a while, the advice information may be presented to the user in display or audio such that the user wears the activity amount meter110and/or the wristwatch-type wearable terminal120. Thereafter, the user wears the activity amount meter110or the like, checks the target pattern, and starts behavior of a day.

(12:00 a.m.; interim check) The habit improving device100calculates the target pattern. Moreover, it generates advice information based on the distribution pattern indicating the activity amount in the morning and the target pattern (retracement (1)). The output device204displays the target pattern and the distribution pattern in the morning, and the advice information is also presented to the user by the output device204in audio or an image. In the example ofFIG.4, since the activity amount in the morning is smaller than the activity amount based on the target pattern, the output device204presents advice information such as “a little short, take a walk a bit more later!” to the user. Furthermore, the habit improving device100presents the distribution pattern together with the target pattern to the user utilizing the output device204.

(23:00 p.m.; final check) Similar to the interim check, the habit improving device100calculates the target pattern. Moreover, it generates the advice information based on a daily distribution pattern and a daily target pattern of today (retracement (2)). The output device204displays the distribution pattern indicating the target pattern of today and the activity amount of today, and the advice information is also presented to the user by the output device204in audio or an image. For example, as illustrated inFIG.4, the habit improving device100displays the target pattern of today and the distribution pattern of today, generates the advice information (in the example ofFIG.4, “Let's walk while going to work tomorrow early in the morning”) regarding whether the activity amount is insufficient, and calculates the target pattern based on these patterns and/or the advice information when the target pattern calculation unit306calculates the target pattern after the next day.

(24:00 p.m.; bedtime) The habit improving device100may present the target pattern of the next day to the user using the output device204before the user goes to bed. The user is informed of the target pattern of the next day in advance so that it is expected that the user can smoothly perform activities of the next day. In addition, the output device204may present the advice information of “Let's walk while going to work tomorrow early in the morning” in the example ofFIG.4to the user before going to bed.

[Operational Example: Entirety]

Next, usingFIG.5, an overview of the operational example of the habit improving device100will be described.

FIG.5is a flowchart showing an example of a processing procedure of the habit improving device100. The processing procedure described below is merely an example, and each step of the processing may be changed as much as possible. In addition, regarding the processing procedure described below, steps can be suitably omitted, replaced, and added in accordance with the embodiment.

(Starting)

First, the user starts the habit improving device100via the input device203. Moreover, inputs such as setting are received. The control unit205of the habit improving device100proceeds the processing in accordance with the following processing procedure.

(Step S501)

In Step S501, the control unit205operates as the attribute information acquisition unit305and acquires the attribute information of the user from the storage unit202. For example, this attribute information is stored in the storage unit202in advance via the input device203or the communication interface201. The user stores the attribute information of himself/herself in the storage unit202using the input device203. In addition, the attribute information of the user may be present on a cloud, and the habit improving device100may acquire the attribute information therefrom via the communication interface201and store the acquired information in the storage unit202. The attribute information is used in Step S504. Therefore, this Step S501may be executed any time before Step S504.

(Step S502)

In Step S502, the control unit205operates as the activity amount acquisition unit301, acquires the activity amount information (for example, data of the number of steps) from the activity amount meter110and/or the wristwatch-type wearable terminal120via the communication interface201, and stores the activity amount information in the storage unit202. For example, the activity amount acquisition unit301acquires the activity amount information for every hour and stores this activity amount information in the storage unit202together with time zone information.

(Step S503)

In Step S503, the control unit205operates as the distribution pattern calculation unit302and the group sorting unit303. The distribution pattern calculation unit302acquires, from the storage unit202, the activity amount information for every hour acquired by the activity amount acquisition unit301and calculates the distribution pattern in which this activity amount is made into a graph. Further, the group sorting unit303sorts a plurality of distribution patterns calculated by the distribution pattern calculation unit302into one or more groups. Further, the calculated distribution patterns are associated with the groups and stored in the storage unit202.

(Step S504)

In Step S504, the control unit205operates as the external factor information acquisition unit304, the attribute information acquisition unit305, and the target pattern calculation unit306. The external factor information acquisition unit304acquires the positional information of the user, the external factor information, and the schedule information of the user from the GPS reception unit208and the communication interface201. The attribute information acquisition unit305acquires the attribute information of the user. The target pattern calculation unit306calculates the target pattern for each of the groups based on the attribute information of the user, the positional information, the external factor information, and the schedule information. The target pattern calculation unit306compares the calculated target pattern and the distribution pattern currently in progress to each other. Moreover, the target pattern may be updated. That is, for example, it may be compared to the target pattern every time the number of steps is acquired every hour, the target pattern may be reviewed every hour, and a target pattern for achieving a target number for the number of steps of one day may be generated based on the attribute information of the user, the external factor information, and the like.

(Step S505)

In Step S505, the control unit205operates as the advice presenting unit307, compares the target pattern calculated by the target pattern calculation unit306and the latest distribution pattern calculated by the distribution pattern calculation unit302to each other, generates the advice information in accordance with the degree of achievement of the target, and presents the advice information to the user via the output device204.

[Operational Example: S502]

Next, usingFIGS.6A and6B, an example of Step S502inFIG.5will be described.

(Step S601)

In Step S601, the control unit205operates as the activity amount acquisition unit301and acquires the activity amount information (for example, here, data of the number of steps) from the activity amount meter110and/or the wristwatch-type wearable terminal120via the communication interface201.

(Step S602)

In Step S602, the control unit205operates as the activity amount acquisition unit301and stores information of the number of steps (acquired activity amount) in the storage unit202. For example, the activity amount acquisition unit301acquires the activity amount information for every hour and begins to store this information in the storage unit202. As illustrated inFIG.6B, the activity amount acquisition unit301acquires a user ID (User ID) that can identify the user, the date when the activity amount is detected, and the day and the time from the activity amount meter110and/or the wristwatch-type wearable terminal120and stores information associated with each other together with the activity amount (this will be referred to as measurement information) in the storage unit202. The table shown inFIG.6Bincludes an example of measurement information.

When the activity amount meter110and/or the wristwatch-type wearable terminal120transmits the activity amount to the habit improving device100substantially at the same time as the activity amount is detected, the date, the day, and the time may be the date, the day, and the time when the activity amount acquisition unit301acquires the activity amount information via the communication interface201. In this case, the habit improving device100acquires the date and the like using the timepiece device206.

[Operational Example: S503]

Next, usingFIG.7A, an example of Step S503inFIG.5will be described.

(Step S701)

In Step S701, the control unit205operates as the distribution pattern calculation unit302, and the distribution pattern calculation unit302acquires measurement information including the activity amount information for every hour acquired by the activity amount acquisition unit301from the storage unit202and calculates the distribution pattern in which this measurement information can be made into a graph.

(Step S702)

In Step S702, the control unit205operates as the group sorting unit303, and the group sorting unit303sorts a plurality of distribution patterns calculated by the distribution pattern calculation unit302into one or more groups. Further, the group sorting unit303associates the calculated distribution patterns (for example, data of the number of steps for every hour for one day) with the groups and stores them in the storage unit202. The group sorting unit303sorts a plurality of distribution patterns during a particular period in the past. The distribution pattern is data of the number of steps for each day. For example, the group sorting unit303sorts the data of the number of steps for one month in the past.

[Operational Example: S701and S702]

Next, usingFIG.7B, an example of detailed operation of Steps S701and S702inFIG.7Awill be described.

(Step S711)

In Step S711, the control unit205operates as the distribution pattern calculation unit302and acquires the activity amount information of the user for every hour from the storage unit202via the communication interface201through the activity amount meter110and/or the wristwatch-type wearable terminal120.

(Step S712)

In Step S712, the control unit205operates as the distribution pattern calculation unit302. Regarding the activity amount information acquired in Step S711, the control unit205calculates the distribution pattern in accordance with the date and the time when the activity amount meter110and/or the wristwatch-type wearable terminal120detects the activity amount or the date and the time when the habit improving device100acquires the activity amount. As a result, for example, the distribution pattern indicating change over time in the activity amount with respect to the time of one day from the activity amount for one day is calculated.

(Step S713)

In Step S713, the control unit205operates as the group sorting unit303and sorts a plurality of distribution patterns calculated by the distribution pattern calculation unit302into one or more groups. For example, the group sorting unit303performs mapping of the distribution pattern of the activity amount for 24 hours in a 24-dimensional space. In this 24-dimensional space, each axis corresponds to the activity amount for every hour. In other words, this space is a 24-dimensional space laid in a basis determined from vectors having the activity amount for every hour as components (24). Therefore, the distribution pattern of the activity amount in a certain day corresponds to one point within the 24-dimensional space. That is, a plurality of points corresponding to all the distribution patterns calculated by the distribution pattern calculation unit302are distributed in the 24-dimensional space. Regarding this point, it is assumed that even if only one of the activity amounts corresponding to the axes differs, the positions of the points in the 24-dimensional space differ, and the distribution patterns of the points become more similar to each other as the distance therebetween within the 24-dimensional space becomes shorter.

(Step S714)

In Step S714, the control unit205operates as the group sorting unit303and judges the way of grouping the points mapped in the 24-dimensional space. In the example ofFIG.7B, the control unit205judges whether the points mapped by the group sorting unit303is grouped by a technique based on clustering. Regarding this judgement, whether grouping is performed by a technique based on clustering is ordinarily set in advance by the user or at the time of design of the habit improving device100. Whether the user uses clustering using the input device203of the habit improving device100may be able to be set, and it may judge whether clustering is executed by this setting in Step S714.

(Step S715)

In Step S715, the control unit205operates as the group sorting unit303, groups the points distributed within the 24-dimensional space using a technique of clustering, and generates one or more groups. Here, description will be given on the assumption of hierarchical clustering that is one of the techniques of clustering. However, clustering is not limited to this technique, and division optimization clustering or the like may be used (however, a procedure for a change is generated).

(Step S716)

In Step S716, the control unit205operates as the group sorting unit303and judges whether the number of groups of the group generated in Step S715corresponds to a desired hierarchy of the clusters. In Step S716, when it is judged that the number of groups corresponds to the desired hierarchy, the process proceeds to Step S717. On the other hand, when it is not judged in Step S716that the number of groups corresponds to the desired hierarchy, the process returns to Step S714. In the case of an aggregation-type hierarchical clustering, from a state in which each of the points within the space is an individual cluster, these clusters are sequentially combined, and a hierarchy of the clusters is generated, and ultimately they are combined to one cluster. Here, a hierarchy indicates a state in which two clusters are combined, and the hierarchy changes as combining proceeds. For example, if the hierarchy of the clusters varies, the number of points included in the clusters varies.

In the case of a division-type hierarchical clustering, contrary to an aggregation-type hierarchical clustering, it is clustering in which dividing begins from one cluster. Dividing starts from a state in which an aggregation of data in its entirety is one cluster, these are sequentially divided, and the hierarchy of the clusters is generated.

In addition, Step S716, the group sorting unit303may judge whether the number of groups has reached a desired number. When it is judged that the number of groups has reached the desired number, the process proceeds to Step S717. On the other hand, when it is judged the number of groups has not yet reached the desired number, the process proceeds to Step S714.

(Step S717)

In Step S717, the control unit205operates as the group sorting unit303and determines the number of groups corresponding to the desired hierarchy judged in Step S716and the groups corresponding to this number of groups.

(Step S718)

In Step S718, the control unit205operates as the group sorting unit303, and each of the points in the space is sorted into any one of the groups determined in Step S717.

(Step S719)

In Step S719, the control unit205operates as the group sorting unit303and copes with a case in which one or more points distributed in the 24-dimensional space are grouped using a technique different from clustering (for example, hierarchy sorting). Herein, as an example, in order to perform sorting based on time zones at which the activity amount is large (and/or small), these time zones are extracted. The technique in Step S719and thereafter differs from a technique based on clustering in that the features of the groups are set in advance and the points within the space are sorted based on the features.

(Step S720)

In Step S720, the control unit205operates as the group sorting unit303and generates one or more groups having characteristic time zones of the activity amount. For example, when the user has set such that the distribution patterns are considered to be the same during weekdays, when the group sorting unit303has a sorting criterion for the groups, such as the activity amount in the time zones at the daytime being equal to or larger than a first threshold and the activity amount in other time zones being equal to or smaller than a second threshold, for example, the distribution patterns belong to the same group. In addition, being different from this, for example, the average value (in addition, for example, a dispersion or a standard deviation may be calculated) for each time zone during weekdays is calculated from data of the distribution pattern of the user in the past. When a point corresponding to a certain distribution pattern is positioned within a space having a confidence level within 95% centering around the average value thereof, the group sorting unit303may sort this distribution pattern such that it belongs to the group during weekdays.

The groups generated by group sorting unit303correspond to a particular region within the space (for example, a 24-dimensional space), and the groups do not overlap each other. In addition, the group sorting unit303can make groups having various other features. For example, similar to that described above, the group sorting unit303can generate corresponding groups by setting a sorting criterion for capturing the features of the activity amount during a holiday.

(Step S721)

In Step S721, the control unit205operates as the group sorting unit303and sorts the points within the space into one or more groups generated in Step S720.

(Step S722)

In Step S722, the control unit205operates as the group sorting unit303, and it is judged whether each of all the points distributed within the space belongs to any one of the groups generated in Step S721and there is no unsorted point. When the group sorting unit303judges that there is an unsorted point in the groups, the process proceeds to Step S723. On the other hand, when it is judged that there is no unsorted point in the groups, the process proceeds to Step S718.

(Step S723)

In Step S723, the control unit205operates as the group sorting unit303, and it is judged whether there is any of the groups generated in Step S720within a distance set in advance from a point that is judged to be unsorted in any group. When there are two or more corresponding groups, the unsorted point is sorted into the closest groups (short distance) therein, and when there is one corresponding group, the unsorted point is sorted into the group.

The distance from the point within the space to the group has various definitions, and any definition may be adopted as long as it is uniquely determined and rational. For example, this distance is considered to be the shortest distance of the distances from the points within the space to the points belonging to the desired groups. In addition, the boundary of the group may be defined, and the distance from the points within the space to the boundary of the group may be defined as the distance between the point and the group. Here, the former definition will be employed. In Step S723, the group sorting unit303searches for the points at the shortest distance of the distances from the points judged to be unsorted in any group to the point belonging to a certain group and judges whether this distance is within the distance set in advance. When this distance is within the distance set in advance, this point judged to be unsorted is considered to belong to the group, and it is judged whether there is any other unsorted point.

(Step S724)

In Step S724, the control unit205operates as the group sorting unit303and judges whether or not there are any unsorted points at a distance larger than the distance set in advance in Step S723. When it is judged that there are no unsorted points at a distance larger than this distance, the process proceeds to Step S718. On the other hand, when there are unsorted points at a distance larger than this distance, the process proceeds to Step S725. Since unsorted points do not belong to any group, there is a need generate a new group including the unsorted points.

(Step S725)

In Step S725, the control unit205operates as the group sorting unit303and generates a group to include the points judged as unsorted points in Step S724, and the process returns to Step S721. For example, this generated group is a multi-dimensional sphere (for example, a 24-dimensional sphere) having the distance set in advance in Step S723by the group sorting unit303(or a length shorter than this distance) as a radius and having dimensions of a space, and the points present in a region within this multi-dimensional sphere may be considered to be in the same group. If a group is generated in this manner, a generated group does not overlap other groups in region.

Here, an example in which the method of grouping one or more points within a space uses only one technique of a technique based on clustering and a technique not based on clustering such as hierarchy sorting, but both the techniques may be used. For example, there is a technique in which a plurality of groups in which the activity amount regarding one or more particular time zones is regulated is set in advance, and the points present in a region of the space not belonging to the groups are sorted into one or more groups through clustering.

Next, usingFIG.7C, for example, a specific example of results when the distribution pattern is sorted into groups using the flowchart shown inFIG.7Bwill be described.FIG.7Cillustrates the distribution pattern of the number of steps for three weeks united for each day, and this example is a case in which the distribution pattern is sorted into four groups.

InFIG.7C, a different group is illustrated in a pattern of a different bar graph. In the example ofFIG.7C, it is sorted into the groups of four kinds, such as (1) a bar graph which has a contour surrounded by a line segment and of which the inner part is indicated by oblique lines, (2) a bar graph which has a contour surrounded by a dotted line and of which the inner part is indicated by a white space, (3) a bar graph which has a contour surrounded by a line segment and of which the inner part is indicated by a black space, and (4) a bar graph which has a contour surrounded by a line segment and of which the inner part is indicated by a white space. This example is not necessarily limited to having the same sorting results in a strict sense for those sorted through clustering, hierarchy sorting, or any other technique, but such results can be substantially obtained by any technique.

The bar graphs for five days during weekdays surrounded by a solid line frame751are in the same category such as weekdays but have different distribution patterns. Therefore, the distribution pattern from Monday to Thursday (corresponding to (1)) and the distribution pattern for Friday (corresponding to (2)) are sorted such that they belong to the different groups. For example, this grouping is judged depending on whether there is more activity amount than a predetermined value in the time zone set in advance as described in Steps S719, S720, and S721. In this case, for example, whether there is the activity amount (the number of steps) after 23:00 is one of the judgement criteria. That is, the number of steps on Friday within the solid line frame751is 500 steps or more after 23:00, and the number of steps from Monday to Thursday within the solid line frame751after 23:00 is fewer than 500 steps (zero steps in this example). Regarding this sorting, there is a probability that similar sorting is performed through clustering if the hierarchy of the clusters determined the number of groups is adjusted.

The bar graphs for four days surrounded by a single dashed line frame752are in the same day but have different distribution patterns. Therefore, it is clear that even if the day is the same, they are sorted to belong to the different groups. This grouping can also be performed by a technique similar to that of the foregoing grouping within the solid line frame751. For example, the graph on the upper left and the graph on the lower right within the single dashed line frame752have a certain distribution pattern (corresponds to (1)) in which the number of steps after 23:00 is fewer than 500 steps, and the graph on the upper right and the graph on the lower left within the single dashed line frame752have a certain distribution pattern (corresponds to (2)) in which both the numbers of steps after 23:00 are 500 steps or more.

The bar graphs for six days surrounded by a double dashed line frame753are the same in the meaning of weekends, but since Saturday and Sunday have the different activity amounts in a particular time zone, the distribution pattern for Saturday (corresponds to (3)) and the distribution pattern for Sunday (corresponds to (4)) are sorted such that they belong to the different groups. This grouping can also be performed by a technique similar to that of the foregoing grouping within the solid line frame751. For example, the number of steps on Saturday is 100 steps or more after 0:00, and the total number of steps from 10:00 to 13:00 is 1,000 steps or more. On the other hand, the number of steps on Sunday after 0:00 is fewer than 100 steps (zero steps), and the total number of steps from 10:00 to 13:00 is fewer than 1,000 steps.

[Operational Example: S504]

Next, usingFIG.8A, an example of Step S504inFIG.5will be described.

(Step S801)

In Step S801, the control unit205operates as the external factor information acquisition unit304and the attribute information acquisition unit305and acquires the external factor information of the user and the attribute information of the user from the storage unit202, the GPS reception unit208, and/or the communication interface201for each user.

(Step S802)

In Step S802, the control unit205operates as the target pattern calculation unit306, identifies a model pattern (or a basic pattern) typical for the group for each of the groups from the distribution pattern sorted into the group in Step S503in accordance with the sorting technique through clustering, hierarchy sorting, or the like, and calculates the target pattern for each user for each of the groups based on the attribute information and the external factor information for each user acquired in Step S801from this model pattern. When grouping is performed through a technique such as clustering, hierarchy sorting, or the like, a pattern typical for the group will be referred to as a model pattern. However, in the embodiment, for the sake of convenient identification, when grouping is performed when the number of distribution patterns which will become samples is equal to or smaller than the threshold, a pattern typical for the group will be referred to as a basic pattern.

[Operational Example: S802]

Next, usingFIG.8B, an example of Step S802inFIG.8Awill be described.

(Step S811)

In Step S811, the control unit205operates as the target pattern calculation unit306and judges whether the number of distribution patterns calculated in Step S503is larger than the threshold. This threshold is a criterion whether the number of distribution patterns is sufficiently large to the extent that the features of the groups can be sorted and is ordinarily a numerical value set in advance. As the value of the threshold becomes large, it is assumed that the number of distribution patterns judged to have YES in this Step S811increases and the feature of each of the groups becomes noticeable. Therefore, a more accurate target pattern can be obtained. For example, when the distribution pattern of the number of steps is acquired every day as inFIG.7C, there is an example in which the threshold is set to 6, the process proceeds to Step S815during first one week after starting to collect the samples of the number of steps, and the process proceeds to Step S812from the second week.

In Step S812, the control unit205operates as the target pattern calculation unit306and identifies a model pattern that becomes a model of the group for each of the groups obtained through clustering, hierarchy sorting, or the like. That is, in the present embodiment, as the number of distribution patterns increases, the group can be sorted with high accuracy through clustering or the like and a more accurate target pattern can be obtained. When the number of distribution patterns which will become samples is larger than the threshold, a model pattern is acquired for each of the groups obtained by the technique through clustering or the like. Regarding identification of a model pattern, for example, a distribution pattern corresponding to a certain point at a position of the center or the center of gravity of the group from the pattern belonging to each of the groups is calculated as a model pattern by the target pattern calculation unit306. In addition, being different from, the target pattern calculation unit306may randomly select a pattern from the distribution pattern belonging to the group and adopt this as a model pattern of a corresponding group.

In Step S813, the control unit205operates as the attribute information acquisition unit305and the target pattern calculation unit306and acquires the attribute information of the user from the storage unit202.

In Step S814, the control unit205operates as the external factor information acquisition unit304and the target pattern calculation unit306and acquires the external factor information from the communication interface201and the GPS reception unit208.

In Step S815, the control unit205acquires the attribute information of the user in a manner similar to that in Step S813.

In Step S816, the control unit205identifies one or more groups corresponding to the attribute information acquired in Step S815. Examples thereof include the physical information of the user, the residence information, the hobby information, and occupation information of occupation and a workplace. In addition to this, for example, if the user is a Japanese and an office worker, it is assumed that the group becomes different during weekdays and weekends. Therefore, three groups may be adopted for the attribute information of the user, as weekdays, Saturday, and Sunday. Moreover, when the user is regularly attending lessons, a sports gym, or the like, these can also be taken into account and the groups may be further increased.

In Step S817, the control unit205operates as the target pattern calculation unit306, selects one pattern from the distribution pattern assumed for each of the groups identified in Step S816, takes this as the basic pattern, and identifies one basic pattern for each of the groups. An assumed distribution pattern may be able to be freely set by the user or the like as long as it belongs to the group and is set in accordance with the attribute information of the user. In addition, the target pattern calculation unit306may automatically generate the distribution patterns in accordance with the attribute information of the user and may take each of these distribution patterns as the basic pattern of the group.

In Step S818, the control unit205operates as the target pattern calculation unit306and generates a target pattern of the user from a model pattern of the user or the basic pattern based on the attribute information of the user and the external factor information.

Next, with reference toFIGS.8C and8D, particular steps inFIG.8Bwill be described.FIG.8Cillustrates the model pattern for each of the groups obtained by sorting the distribution patterns, or the basic pattern for each of the groups set based on the attribute information or the like of the user, and the target pattern for each user generated in accordance with the external factor information from the model pattern or the basic pattern.FIG.8Dillustrates an example of a case in which the activity amount is increased and decreased based on the schedule information of the user from the model pattern or the basic pattern or the activity time zone is shifted.

The model pattern identified in Step S812or the basic pattern identified in Step S817becomes as the distribution pattern illustrated on the left side inFIG.8C, for example. That is, the target pattern calculation unit306identifies the model pattern or the basic pattern in a certain day based on the attribute information of the user.

Further, the target pattern calculation unit306acquires the external factor information as in Steps S814and S818and generates a target pattern based on this information.

In the example of (a) ofFIG.8C, the target pattern calculation unit306increases and decreases the activity amount based on the range of the user's behavior such as residence and a workplace of the user. In (a) thereof, in the area of this range of the user's behavior, it is already known that a precipitation is large in a particular time zone. Therefore, the activity amount (in this example, the number of steps) is reduced from the basic pattern or the model pattern in the particular time zone (from 12:00 to 14:00 in this example). Further, the reduced activity amount is allocated to another time zone (15:00 to 18:00 in this example from).

In the example of (b) ofFIG.8C, based on the schedule information of the external factor information, since this day is a day for early attendance at the office, the basic pattern or the model pattern is set for the entirety and the schedule is advanced in accordance with the time to attend the office. Further, the target pattern calculation unit306generates a target pattern of this day based on the basic pattern or the model pattern which have been advanced and further based on the external factor information or the attribute information.

In the example ofFIG.8D, as illustrated on the left side in the diagram, based on the basic pattern or the model pattern of today, the target pattern calculation unit306changes the activity time zone and the activity amount in accordance with the scheduled information of the user. The example in the upper right part ofFIG.8Dis a case in which a party is scheduled from 19:00 today, and since the activity amount decreases at the time of the party, the target pattern calculation unit306generates a target pattern to increase the number of steps through extra walking or light jogging as much as the decreased activity amount at the time of going to the office in the morning.

In the example of on the lower right inFIG.8D, since exercise at the sports gym is scheduled at 19:00 today, the target pattern calculation unit306generates a target pattern to increase the activity amount at the sports gym. In addition, since there is no need to reduce the activity amount other than the sports gym, the target pattern calculation unit306generates a target pattern on the assumption that there is no problem for other activities with the basic pattern or the model pattern.

Next, usingFIG.8E, a case in which when the user behaves differently from the scheduled information, the target pattern calculation unit306changes the target pattern in accordance with the behavior will be described.

The target pattern calculation unit306judges whether the user properly manages the schedule and changes the target pattern based on the behavior when the user behaves differently from the schedule. For example, the target pattern calculation unit306acquires the current activity amount from the activity amount acquisition unit301, acquires the positional information and the schedule information of the user from the external factor information acquisition unit304, and monitors the behavior of the user.

In addition, regarding activity amount information which is currently in progress, recent activity amount information, and/or current activity amount information, the activity amount information acquired by the activity amount acquisition unit301may be stored in the storage unit202, and the target pattern calculation unit306may acquire the activity amount information thereof via the storage unit202. The target pattern calculation unit306monitors the degree of achievement of the user in target pattern. When there is separation between the target pattern and the actual distribution pattern, the target pattern may be changed to ensure the activity amount. In addition, even when the user suddenly changes the schedule, the target pattern calculation unit306may monitor the schedule and the activity amount thereof at all times. A case in which there is separation between the target pattern and the actual distribution pattern is a case, for example, in which the difference between the activity amounts of the target pattern and the actual distribution pattern in a certain time zone significantly deviates from the threshold.

The example in the upper part ofFIG.8Eis an example in which the user is scheduled to go to the sports gym from 19:00 but the user has changed his/her mind and goes to play a pinball game. In this case, the target pattern calculation unit306judges that the user does not behave as scheduled based on the positional information and the schedule information of the user from the GPS reception unit208. In this manner, the target pattern calculation unit306can automatically detect that the user is playing a pinball game based on the positional information, the activity amount information of the user, or the like. In this case, the target pattern calculation unit306changes the schedule from the sports gym to the pinball game and reschedules the reduced activity amount by transferring it to the schedule for the next day, for example.

The example in the lower part ofFIG.8Eis an example in which the user is scheduled to go to a party from 19:00 and attends the party as scheduled. The target pattern calculation unit306checks for whether the user behaves as scheduled based on the positional information and the schedule information of the user from the GPS reception unit208. In this case, since the user behaves as scheduled, the target pattern calculation unit306judges that there is no need to change the target pattern.

[Operational Example: S505]

Next, usingFIG.9A, an example of Step S505inFIG.5will be described.

(Step S901)

In Step S901, the control unit205operates as the advice presenting unit307and calculates the difference pattern by taking the difference between the distribution pattern currently in progress (for example, the distribution pattern from 0:00 a.m. until the present today) and the target pattern generated in Step S802from the distribution pattern calculation unit302. This difference pattern is a pattern in which the differences between the activity amounts in the distribution patterns and the activity amounts in the target patterns for each time are subjected to time distribution.

(Step S902)

In Step S902, the control unit205operates as the advice presenting unit307, extracts the advice information from a table in the storage unit202, in which the advice information corresponding to the difference pattern calculated in Step S901is listed, and presents it to the user via the output device204.

[Operational Example: S901and S902]

Next, usingFIG.9B, an example of Steps S901and S902inFIG.9Awill be described.

(Step S911)

In Step S911, the control unit205operates as the advice presenting unit307and acquires the distribution pattern currently in progress calculated by the distribution pattern calculation unit302from the activity amount information acquired by the activity amount acquisition unit301via the communication interface201. For example, this distribution pattern currently in progress is today's distribution pattern and is a distribution pattern for a period from the first time when the activity amount is acquired today until the present.

(Step S912)

In Step S912, the control unit205operates as the advice presenting unit307and acquires the target pattern of today from the target pattern calculation unit306. The target pattern calculation unit306calculates the target pattern based on the external factor information, the attribute information, and the like of the user today.

(Step S913)

In Step S913, the control unit205operates as the advice presenting unit307, obtains the difference between the activity amounts for each time zone by calculating the difference between the distribution pattern currently in progress today and the target pattern calculated in Step S912for each of the time zones (the first period; for example, one hour), and calculates the difference pattern in which the differences are arranged throughout all the time zones.

(Step S914)

In Step S914, the control unit205operates as the advice presenting unit307and generates the advice information for the user based on the difference pattern calculated in Step S913and/or the external factor information. For example, this advice information is stored in the storage unit202in association with the difference pattern. In addition, the advice presenting unit307may generate the advice information based on the state of the difference pattern. For example, the advice information is “a little short, take a walk a bit more later!” or the like when the difference pattern indicates a negative value (specifically, when the totaled amount of the activity amount of the target pattern is smaller than the totaled amount of the activity amount in the time zone corresponding to the distribution pattern) and is presented to the user in display, audio, or the like. Regarding another example of the advice information, when a party is scheduled in the schedule information from 19:00 today, an example of a wording presented to the user after waking up in the morning is “Refrain from drinking and increase a morning walk”. Regarding another example, when attendance at the sports gym is scheduled in the schedule information from 19:00 today, there is a message of encouragement, such as recommendation for exercise at the sports gym.

In addition, the advice presenting unit307may generate the advice information for the user based on the external factor information. In the external factor information, for example, it is mainly assumed that the advice information is presented using the schedule information of the user and a weather forecast based on the positional information and the schedule information of the user. The target pattern calculation unit306gives this external factor information to the advice presenting unit307together with the reason for reference of the information which is referred to when the target pattern is calculated by the target pattern calculation unit306. For example, when the weather forecast for the scheduled time zone at the scheduled site for running today is rainy, the target pattern calculation unit306calculates the target pattern such that the scheduled activity amount in the scheduled time zone for running is decreased and the activity amounts in other time zones are increased. In this case, the target pattern calculation unit306gives this information to the advice presenting unit307, and the advice presenting unit307generates the advice information corresponding to this information and presents it to the user.

Furthermore, regarding an example of the advice information based on the schedule information, when a party is scheduled from 19:00 today, when attendance at the sports gym is scheduled from 19:00 today, or the like as described above, when the target pattern is calculated based on this schedule information, the target pattern calculation unit306gives reflecting information indicating how the schedule information is reflected in the target pattern to the advice presenting unit307. The advice presenting unit307generates the advice information based on this reflecting information and presents it to the user.

Next, usingFIG.9C, an example of the advice information presented by the advice presenting unit307will be described by describing the target pattern calculated by the target pattern calculation unit306and an actual distribution pattern currently in progress today.

The target pattern calculation unit306acquires the distribution pattern of the activity amount before 18:00 from the storage unit202or the activity amount acquisition unit301. Moreover, the target pattern calculation unit306makes the target pattern at the current time (18:00 in the example ofFIG.9C) based on information from the group sorting unit303and the external factor information acquisition unit304. Since the activity amount before the current time in the target pattern inFIG.9Cis smaller than the activity amount of the target pattern, it is recommended to go home early and go to the sports gym or the like for exercise from 20:00 (reflecting information). The target pattern calculation unit306gives this reflecting information to the advice presenting unit307, and the advice presenting unit307generates the advice information such as “Cheer up a little more for exercise from 20:00! Let's leave the office early today”, for example, based on this reflecting information and presents it to the user. In addition, reflecting information and the advice information may be stored in the storage unit202in association with each other, and the advice presenting unit307may acquire the advice information from the storage unit202based on the reflecting information received from the target pattern calculation unit306.

[Operation and Effects]

As above, the habit improving device100of the present embodiment records the information of the number of steps for every hour acquired in Step S502and calculates the distribution pattern of the number of steps of one day based on the information of the number of steps in Step S503. Further, the habit improving device100sorts a plurality of distribution patterns calculated by the distribution pattern calculation unit302into groups through clustering, hierarchy sorting, and/or the like by the group sorting unit303. Moreover, in Step S504, the target pattern that becomes a target desirable for the user for each of the groups is calculated by the target pattern calculation unit306based on the attribute information of the user and the external factor information. This target pattern is calculated based on the attribute information of the user and the external factor information. Therefore, since the features of the user are sorted based on the groups, and furthermore, the attribute information of the user from which information is not easily extracted simply with the distribution pattern is included, the habit improving device100can calculate a target pattern effective for the user. Moreover, the target pattern is also calculated based on the external factor information of the user and is calculated based on an external factor such as an environment other than the user himself/herself. For this reason, since calculation is performed including the external factor information which cannot be extracted simply with the distribution pattern and which cannot be extracted even if the attribute information of the user and the distribution pattern are combined, the habit improving device100can further calculate a target pattern effective for the user with higher accuracy.

Moreover, in Step S505, the advice presenting unit307can present appropriate advice information to the user in association with the schedule information of the user, for example, based on the calculated target pattern. Moreover, the target pattern calculation unit306can revise and/or change the target pattern as required based on the distribution pattern currently in progress and the target pattern corresponding to this distribution pattern, and when the target pattern is revised and/or changed, the advice presenting unit307presents the advice information corresponding to the revision and the change to the user. As a result, according to the habit improving device100, the user can appropriately receive the advice information regarding activities at a suitable timing, and thus a possibility of achieving the target pattern can be enhanced.

Modification Example

Hereinabove, the embodiment of the present invention has been described in detail, but the foregoing description is merely an example of the present invention in every respect. It goes without saying that various improvements or modifications can be performed without departing from the scope of the present invention. For example, the following changes can be made. In addition, when the present invention is performed, a specific constitution according to the embodiment may be suitably employed. Hereinafter, similar reference signs are used for constituent elements similar to those in the foregoing embodiment, and description of points similar to those in the foregoing embodiment is suitably omitted. The following modification examples can be suitably combined.

<1>

The external factor information acquisition unit304may acquire the attribute information of another user other than the user (as described above, for example, information including age, residence, a gender, and/or the like) and a target pattern of this user and may match the attribute information of the user himself/herself acquired by the attribute information acquisition unit305and the attribute information of another user (for example, executed by the target pattern calculation unit306). When the degree of matching is higher than the judgement criterion set in advance, this target pattern of another user may be employed.

Therefore, since effects similar to those in the case in which a number of similar distribution patterns are sampled can be achieved by referring to the similar attribute information of another user, a target pattern of the user can be accurately calculated. As a result, the habit improving device of the present embodiment can present more appropriate advice information to the user.

Moreover, in this case, if there is an actual sample in which another user has achieved the target pattern, it is desirable to make the target pattern more likely to be employed (for example, increase the weighting). It is possible to expect that the user is more likely to achieve this target pattern compared to a case of employing other target patterns which has not been achieved.

<2>

With reference to the distribution pattern of another user having similar attribute information and the schedule information, in order to come closer to the attribute which has already been realized by another user, it is considered that the user himself/herself efficiently realizes a desired attribute using the distribution pattern and/or the target pattern of another user. In order to realize this, first, an attribute which the user himself/herself intends to realize is set, and another user already having this attribute intended to be realized (for example, a blood pressure value or a BMI value) and having other attributes similar to the attributes of the user himself/herself is found from a database or the like. For example, finding the attributes of another user is realized by searching for the attributes using the attribute information acquisition unit305connected to the server130via the communication interface201. In this case, the server130may store the attribute information, the external factor information, and information of the distribution pattern and the target pattern from a number of users.

It is effective for the user to simply read and refer to the distribution pattern and/or the target pattern which has been searched for in this manner. However, moreover, this pattern may be employed as a target pattern, the target pattern calculation unit306may calculate the target pattern as described in the foregoing embodiment, and furthermore, the advice presenting unit307may present the advice information to the user.

If there is such a system, in order for the user to realize a target attribute (for example, the user realizes an ideal body type), an actual distribution pattern (a distribution pattern of another user) for a distribution pattern to be realized can be adopted as a standard, and thus a possibility of being capable of accurately calculating the target pattern to be realizing can be increased.

In addition, it is desirable that the difference between the attribute information of the user of the target pattern before being realized by the user and the attribute information of the user after the target pattern is realized be stored in a server or the like for each user as the attribute information for each item of the attribute information. In this case, another user having the attribute information desired by the user himself/herself can be efficiently found from the attribute information similar to that of the user himself/herself based on the current attribute information of the user himself/herself and the attribute information intended to be realized, by searching for similar attribute information of another user with reference to the server. Accordingly, since the user himself/herself can efficiently find and utilize an effective target pattern of another user, it is easy for the user himself/herself to reach a desired attribute. According to this constitution, for example, since a person in his/her forties desiring to have a slender build has a number of such lifestyle habits (corresponding to the attribute information, and the distribution pattern and/or the target pattern), it is possible to appropriately present to the user that it is better to have different exercise habits (corresponding to the distribution pattern).

<3>

For example, in Step S719and thereafter inFIG.7B, the points within the space may be sorted into groups in the following manner.

For example, when it becomes clear based on the attribute information of the user that the user is at work during weekdays and spends a particular time zone at a particular place, the group sorting unit303estimates that substantially the same distribution pattern is indicated during weekdays, calculates the average value for each time zone during weekdays, and sets the distribution pattern having the average value as one of the basic patterns. The group sorting unit303can make a basic pattern having various other features. The group sorting unit303can generate the basic pattern for a holiday in a manner similar to that described above.

For example, the group sorting unit303may sort the distribution patterns by making the groups as follows even if a basic pattern is not introduced. For example, the group sorting unit303divides the groups depending on whether the activity amount at late night (for example, after 23:00 until 2:00 the next day) during weekdays is larger than the first threshold (for example, a value larger than the average value of the activity amount during weekdays by 50%). Regarding the activity amount and the time zone thereof, many criteria are set, and a plurality of groups is sorted using these criteria at the same time. For example, when sorting is performed with criteria of two ways such as whether the entire activity amount is smaller than the second threshold (for example, 50% of the activity amount during weekdays), and whether the activity amount at late night during weekdays is larger than the first threshold, sorting of 2×2=4 groups can be performed due to the first and second thresholds. A different criterion other than these criteria for the activity amount may be introduced, and the points within the space may be sorted including these criteria. For example, a criterion for distinguishing weekdays (from Monday through Friday), weekends, and national holidays may be introduced. In addition, when it becomes clear that behavior on Friday differs from that during weekdays, Friday may be sorted into a group different from that for weekdays.

<4>

The group sorting unit303may perform sorting of groups based on the plurality of model patterns which is a typical pattern of the group and set in advance. In this constitution, the group sorting unit303sorts a plurality of distribution patterns into groups using a model pattern indicating the features of the groups thereof for each of the groups and set in advance. The feature is that a model pattern is set in advance, and a setter such as a user can set a model pattern in advance. Therefore, when a plurality of distribution patterns of the user is sorted into groups, it is possible to realize grouping in which a significance is given clearly.

<5>

Although it is not illustrated, the activity amount meter110, the wristwatch-type wearable terminal120, and/or the habit improving device100may include an acceleration sensor, a pressure sensor, a gyro-sensor, and/or a magnetic field sensor.

An acceleration sensor is a sensor for detecting an acceleration. For example, it is a three-axis acceleration sensor and detects an acceleration of the sensor regarding linear independent three axes (for example, three axes orthogonal to each other). Further, the acceleration sensor outputs an acceleration signal expressing accelerations in three directions to the control unit205. The acceleration sensor can obtain a roll angle and a pitch angle from a value of an acceleration at the time of a standstill.

A pressure sensor is a general sensor for detecting a pressure. For example, according to the pressure sensor, an elevation of the user can be detected by measuring an air pressure.

A gyro-sensor is a general sensor capable of detecting an angular speed of the sensor. For example, it is a three-axis gyro-sensor and detects an angular speed of the sensor regarding linear independent three axes. The gyro-sensor outputs an angular speed signal expressing angular speeds in three directions to the control unit205.

A magnetic field sensor is a general sensor and is used for judging a posture of the user. For example, the magnetic field sensor is a three-axis magnetic field sensor and detects the intensity of geomagnetism around the sensor regarding three axes including the direction and the intensity (magnitude).

The control unit205may obtain an angle by integrating an angular speed from initial posture information of the user using a technique of correcting an error due to a drift for the angular speed obtained from the gyro-sensor from information of the acceleration sensor and the magnetic field sensor, and may obtain a posture angle for each of the sensors at a desired time elapsed from the initial time. Regarding an initial posture, the roll angle and the pitch angle can be obtained using the acceleration sensor. A yaw angle can be obtained using the magnetic field sensor.

Three-dimensional components of a magnetic field in which a tilt error is corrected are calculated from the three-dimensional components of a magnetic field obtained by the magnetic field sensor, and the roll angle and the pitch angle which have been previously obtained. A yaw angle can be calculated from an x component and a y component of the three-dimensional components of the magnetic field in which this tilt error is corrected. When the user has moved from the initial posture, an angle is obtained by performing time integration of the angular speed, and thus a posture angle for each of the sensors at an arbitrary time can be obtained based on the initial posture and the elapsed time period.

Based on change in posture angle, movement (for example, movement of an arm and/or a leg) when the user is at a standstill can be detected. For example, a position of the user seldom changes but performs exercise (for example, dance or exercise at the sports gym), the position can be more correctly detected by comparing the case in which these are not detected.

<6>

The device of the present invention can also be realized by a computer and a program. The program can be recorded in a recording medium (or a storage medium) and can also be provided through a network.

In addition, each of the devices and device parts thereof described above can be operated by any of a constitution of hardware or a combined constitution of a hardware resource and software. Regarding software of a combined constitution, a program for causing the computer to realize operation (or functions) of each of the devices is used by being installed in a computer in advance from a network or a computer readable recording medium (or a storage medium) and executed by a processor of the computer.

<7>

This invention is not limited to the foregoing embodiment as it stands, and the constituent elements can be modified and specified in the performing stage within a range not departing from the gist thereof. In addition, various inventions can be formed by a suitable combination of a plurality of constituent elements disclosed in the foregoing embodiment. For example, some constituent elements may be removed from all the constituent elements described in the embodiment. Moreover, constituent elements in a different embodiment may be suitably combined.

In addition, “and/or” denotes one or more arbitrary items of items enumerated with “and/or” therebetween. Regarding a specific example, “x and/or y” denotes any element of an aggregation {(x), (y), (x, y)} constituted of three elements. Regarding another specific example, “x, y, and/or z” denotes any element of an aggregation {(x), (y), (z), (x, y), (x, z), (y, z), and (x, y, z)} constituted of seven elements.

APPENDIX 1

There is provided a habit improving device (100) including first calculation units (301and302) which acquire, for each of first periods, a user's activity amount totaled for each of the first periods and calculate a distribution pattern indicating change over time in the activity amount during a second period including the first periods; a sorting unit (303) which sorts a plurality of the distribution patterns into one or more groups; second calculation units (304,305, and306) which calculate, based on a factor other than the activity amount, a target pattern that becomes a target for the user in association with the groups; and a presenting unit (307) which presents advice information to the user based on the current distribution pattern and the target pattern.