JP2023012979A - Information processing device, program, and information processing method - Google Patents

Abstract

To provide an information processing apparatus, a program, and an information processing method capable of more accurately estimating the weight.SOLUTION: An information processing device is provided with a processing part. The processing part includes the steps of: acquiring calorie intake information on the calorie consumption of a user; acquiring calorie consumption information on the calorie consumption of the user; deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information; and adjusting the calculation algorithm so that a difference between the estimated weight and a measurement value of the weight is reduced when a calculation value of the user's weight measured by a scale.SELECTED DRAWING: Figure 8

Description

The present invention relates to an information processing device, a program, and an information processing method.

Conventionally, an information processing apparatus capable of providing weight management information for managing the weight of a user is known, and is used by users who are trying to lose weight for health management. This information processing device records the daily weight measurement values of the user, and includes, as weight management information, for example, changes in the weight measurement values, the difference between the weight measurement value and the target weight, and the difference within a set period. Various advices (for example, recommended calorie intake and amount of activity) for achieving weight loss are presented to the user. Further, Patent Literature 1 discloses a technique for predicting a change in body weight based on the measurement results of body weight and body composition and displaying it as weight management information.

JP-A-2009-50523

In any of the above-described conventional techniques, the user needs to measure his/her current weight each time and input it into the information processing apparatus in order to check the weight management information. Therefore, there is a problem that the weight management information cannot be confirmed when there is no weight scale at hand. In addition, there is a problem that the frequency of checking the weight management information is reduced or no longer checked due to the troublesomeness of measuring the weight, and the weight loss is often frustrated. As a method of avoiding these problems, it is conceivable to calculate an estimated weight value at a timing desired by the user and use the estimated value instead of the measured value.

However, in the method of estimating body weight by a uniform calculation formula based on input parameters such as calorie intake and calorie consumption, there is a large individual difference in the influence of parameters on body weight, so it is difficult to accurately estimate body weight. There is

SUMMARY OF THE INVENTION An object of the present invention is to provide an information processing apparatus, a program, and an information processing method capable of estimating body weight more accurately.

In order to solve the above problems, an information processing apparatus according to the present invention includes:
a processing unit,
The processing unit is
Acquiring calorie intake information related to the calorie intake of the user,
Acquiring calorie consumption information related to the user's calorie consumption,
deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
The calculation algorithm is adjusted so that a difference between the estimated weight and the measured weight is reduced when the measured weight of the user measured by a weight scale is acquired.

Further, in order to solve the above problems, the program according to the present invention is
to the computer,
A function of acquiring calorie intake information related to a user's calorie intake;
a function of acquiring calorie consumption information related to the user's calorie consumption;
a function of deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
a function of adjusting the calculation algorithm so that, when the measured value of the user's weight measured by a weight scale is acquired, the difference between the estimated weight and the measured value of the weight is reduced;
Realize

Further, in order to solve the above problems, an information processing method according to the present invention includes:
A computer-based information processing method comprising:
a step of acquiring calorie intake information related to the calorie intake of the user;
a step of acquiring calorie consumption information related to the user's calorie consumption;
deriving an estimated body weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
a step of adjusting the calculation algorithm so that, when the measured value of the user's weight measured by a weight scale is acquired, the difference between the estimated weight and the measured value of the weight is reduced;
including.

According to the present invention, body weight can be estimated more accurately.

It is a figure which shows an information processing system. 2 is a block diagram showing the functional configuration of a smart phone; FIG. 2 is a block diagram showing the functional configuration of an electronic timepiece; FIG. FIG. 10 is a diagram showing an example of a weight management screen including weight management information; 10 is a flowchart for explaining a control procedure of calorie intake data update processing; FIG. 10 is a flowchart for explaining a control procedure of calorie consumption data update processing; FIG. FIG. 10 is a flowchart for explaining a control procedure of stress data update processing; FIG. 4 is a flowchart for explaining a control procedure of weight management information display processing; It is a figure which shows a notification screen.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments according to the present invention will be described with reference to the drawings.

<Overview of information processing system>
FIG. 1 is a diagram showing an information processing system 1 of this embodiment. The information processing system 1 includes a smart phone 10 (information processing device) and an electronic timepiece 20 .

The smartphone 10 is a terminal device that is mainly carried and used by a user, and has a call function, a data communication function, and various information processing functions. An application program for managing the user's weight (hereinafter referred to as weight management application 121 (see FIG. 2)) is installed in the smartphone 10 . The smartphone 10 executes various types of information processing for weight management of the user on the weight management application 121, and displays weight management information including processing results on the display unit. In addition, the smartphone 10 can calculate (derive) the user's estimated weight on the weight management application 121 according to a predetermined calculation algorithm.

The electronic timepiece 20 is a wristwatch worn on the user's arm. The electronic watch 20 includes a sensor unit 26 (FIG. 3) that detects the exercise state of the device itself (and thus the user's exercise state), and can determine the exercise intensity based on the detection result of the user's exercise state. can. The electronic timepiece 20 is communicatively connected to the smartphone 10 by short-range wireless communication. Short-range wireless communication includes, but is not limited to, Bluetooth (registered trademark). In the following, the state in which the smartphone 10 and the electronic watch 20 are connected to each other via short-range wireless communication is also referred to as pairing. The determination result of the exercise intensity by the electronic watch 20 is transmitted to the smartphone 10 and used for weight management processing (specifically, calculation of calorie consumption) by the weight management application 121 .

<Smartphone configuration>
FIG. 2 is a block diagram showing the functional configuration of the smartphone 10. As shown in FIG.
The smartphone 10 includes a CPU 11 (Central Processing Unit), a memory 12 (storage unit), a display unit 13, an operation reception unit 14, a camera 15, a wireless communication unit 16, a telephone communication unit 17, and the like.

The CPU 11 is a processor that controls the operation of each part of the smartphone 10 by reading and executing programs stored in the memory 12 and performing various arithmetic processing. In this embodiment, the CPU 11 corresponds to the "processing unit". Note that the processing unit may have two or more circuit elements such as a CPU that performs arithmetic processing.

The memory 12 is a non-temporary recording medium readable by the CPU 11 as a computer, provides a working memory space to the CPU 11, and stores various data. The memory 12 includes, for example, RAM (Random Access Memory) and non-volatile memory. The RAM is used for arithmetic processing by the CPU 11 and stores temporary data. The non-volatile memory is, for example, a flash memory, and stores programs and various data. The programs are stored in memory 12 in the form of computer readable program code. Programs stored in the memory 12 include the weight management application 121 described above. Data stored in the memory 12 include calorie intake data 122 (calorie intake information), calorie consumption data 123 (calorie consumption information), personal data 124 (personal information), stress data 125 (stress information), and body weight data. 126, calculation algorithm data 127, and estimated weight data 128. Among them, the calorie intake data 122, the calorie consumption data 123, the personal data 124, and the stress data 125 are part of the calculation data D used for calculating the estimated body weight, which will be described later.

The calorie intake data 122 includes information on the calorie intake that the user of the smart phone 10 ingested through meals. The calorie intake is specified, for example, from the analysis result of the meal image captured by the camera 15 . Also, the calorie intake may be specified from the details of the meal (for example, the menu and amount of the meal) input by the user. Also, the calorie intake may be directly input by the user.

The calorie consumption data 123 includes information on the user's calorie consumption. The calorie consumption is calculated, for example, based on the user's basal metabolic rate and exercise intensity data relating to the user's exercise intensity transmitted from the electronic watch 20 . Of these, the basal metabolic rate can be calculated based on the user's height, weight, and age included in the personal data 124, for example.

The personal data 124 includes information related to the user's physique, age, attributes, etc., input by the user on the weight management application 121 . In this embodiment, the personal data 124 includes height, weight and age information of the user. The weight is the weight input by the user at the start of weight management (hereinafter referred to as "initial weight"). In addition, the content of the personal data 124 is not limited to this, and the personal data 124 may include part of the height, weight, and age information, or may include information other than the height, weight, and age. may be

The stress data 125 includes information representing the user's stress intensity (degree of stress). In this embodiment, the stress data 125 includes information on the secretion amount of stress markers in the user's body (for example, in body fluids such as blood, saliva, and sweat). Here, the stress marker is a chemical substance (biomarker) whose secretion amount changes according to the stress intensity, and corresponds to, for example, cortisol and catecholamine (noradrenaline and adrenaline). Among these, cortisol is used as a stress marker in the present embodiment. It is known that the higher the stress intensity, the higher the amount of cortisol secreted. Therefore, the user's stress intensity can be estimated from the cortisol secretion amount. The stress data 125 is generated based on the detection result of the cortisol secretion amount by the stress marker detection unit 27 in the electronic timepiece 20 and is transmitted to the smartphone 10 .

The weight measurement value data 126 includes information on the measurement value of the user's weight measured by a weight scale. The measured weight value may be directly input by the user on the weight management application 121 , or may be transmitted to the smartphone 10 from a weight scale (not shown) connected to the smartphone 10 for communication.

The calculation algorithm data 127 is data relating to a calculation algorithm, which is determined so as to be able to calculate the user's estimated weight based on the calculation data D. FIG. A calculation algorithm for the estimated body weight will be described later.

The estimated body weight data 128 is estimated body weight data calculated based on the calculation data D according to the calculation algorithm represented by the calculation algorithm data 127 .

Under the control of the CPU 11, the display unit 13 displays various information such as an operation screen of the weight management application 121 and weight management information. The display unit 13 is also used to give various notifications to the user, and corresponds to a "notification unit". As the display unit 13, for example, a liquid crystal display device that performs display using a dot matrix method can be used, but the present invention is not limited to this.

The operation reception unit 14 receives a user's input operation and outputs an input signal corresponding to the input operation to the CPU 11 . The operation reception unit 14 includes a touch panel provided so as to be superimposed on the display screen of the display unit 13, and the touch panel detects the touch of a user's finger or the like as an input operation. Further, the operation reception unit 14 may include a hardware button together with the touch panel or instead of the touch panel, and may be able to receive an input operation using the hardware button.

The camera 15 has a plurality of imaging elements that convert incident light into electrical signals corresponding to the intensity thereof, an optical system that guides the incident light to the imaging elements, and the like. Under the control of the CPU 11 , the camera 15 photographs a photographing range determined by the image sensor and the optical system, generates photographed image data, and stores the photographed image data in the memory 12 .

The wireless communication unit 16 performs wireless communication with the electronic timepiece 20, that is, transmits and receives data using radio waves. In this embodiment, the wireless communication unit 16 performs short-range wireless communication using Bluetooth with the electronic timepiece 20 to be paired. The wireless communication unit 16 also performs data communication with an external device via a wireless LAN access point or the like.

The telephone communication unit 17 communicates with a mobile telephone base station or the like, and transmits/receives voice data of telephone communication, packet data related to Internet connection, and the like.

<Structure of Electronic Clock>
FIG. 3 is a block diagram showing the functional configuration of the electronic timepiece 20. As shown in FIG.
The electronic timepiece 20 includes a CPU 21, a memory 22 (storage unit), a display unit 23, an operation reception unit 24, a timer unit 25, a sensor unit 26, a stress marker detection unit 27, a wireless communication unit 28, A satellite radio wave reception processing unit 29 and the like are provided.

The CPU 21 is a processor that reads out and executes a program 221 stored in the memory 22 and performs various arithmetic processing to control the operation of each part of the electronic timepiece 20 .

The memory 22 is a non-temporary recording medium readable by the CPU 21 as a computer, provides a working memory space to the CPU 21, and stores various data. Memory 22 includes, for example, RAM and non-volatile memory. The RAM is used for arithmetic processing by the CPU 21 and stores temporary data. The nonvolatile memory is, for example, a flash memory, and stores various data in addition to the program 221 . The program 221 is stored in the memory 22 in the form of computer-readable program code.

Under the control of the CPU 21, the display unit 23 displays various kinds of information such as the time, the detection result of the exercise state, and the detection result of the stress marker. As the display unit 23, for example, a liquid crystal display device that performs display using a dot matrix method can be used, but the present invention is not limited to this.

The operation reception unit 24 has a plurality of operation buttons. The operation accepting unit 24 accepts a user's input operation (for example, pressing operation) on the operation button and outputs an input signal corresponding to the input operation to the CPU 21 . The operation reception unit 24 may have a touch panel provided over the display screen of the display unit 23 instead of or together with the operation buttons.

The timekeeping unit 25 includes an oscillation circuit, a frequency dividing circuit, a timekeeping circuit, and the like. The clock unit 25 counts and holds the current date and time by dividing the frequency of the clock signal generated by the oscillation circuit with the frequency divider circuit and counting the frequency-divided signal with the clock circuit.

The sensor unit 26 includes a 3-axis acceleration sensor 261 , a 3-axis gyro sensor 262 and a 3-axis geomagnetic sensor 263 . The three-axis acceleration sensor 261 detects, at a predetermined sampling frequency, acceleration in each axial direction applied to the electronic timepiece 20 according to the motion of the user, and outputs acceleration data to the CPU 21 as a detection result. The 3-axis gyro sensor 262 detects angular velocities around each axis applied to the electronic timepiece 20 according to the motion of the user at a predetermined sampling frequency, and outputs angular velocity data to the CPU 21 as detection results. The triaxial geomagnetic sensor 263 detects the direction of geomagnetism passing through the electronic timepiece 20 at a predetermined sampling frequency, and outputs geomagnetism data to the CPU 21 as the detection result. Data output from the three-axis acceleration sensor 261, the three-axis gyro sensor 262, and the three-axis geomagnetic sensor 263 include signal components for three axes orthogonal to each other. The sensor unit 26 includes an amplifier (not shown) that amplifies the analog signals output from the triaxial acceleration sensor 261, the triaxial gyro sensor 262, and the triaxial geomagnetic sensor 263, and converts the amplified analog signals into digital data. An AD converter (not shown) for outputting to the CPU 21 is provided.

The stress marker detection unit 27 detects cortisol as a stress marker secreted in the user's body, and outputs the detection result related to the amount of secretion of cortisol to the CPU 21 . The stress marker detection unit 27 includes a light emitting unit that emits infrared light and a light receiving unit that receives the infrared light emitted from the light emitting unit and reflected by the body (for example, blood). Since cortisol absorbs infrared rays of a specific wavelength, the amount of cortisol secreted in the blood (for example, concentration) can be specified based on the amount of infrared rays absorbed at that wavelength. The stress marker detection method by the stress marker detection unit 27 is not limited to the optical method as described above. For example, a method of collecting sweat of the user and detecting stress markers contained in the sweat may be used.

The wireless communication unit 28 wirelessly communicates with the smartphone 10, that is, transmits and receives data using radio waves. In the present embodiment, the wireless communication unit 28 performs short-range wireless communication using Bluetooth with the smartphone 10 to be paired.

A satellite radio wave reception processing unit 29 receives radio waves transmitted from positioning satellites of the Global Navigation Satellite System (GNSS) to acquire GPS data, and calculates the current position and date and time based on the GPS data. It is a module that The satellite radio wave reception processing unit 29 calculates the current position and the date and time under the control of the CPU 21 and outputs the result to the CPU 21 . The global positioning satellite system used is not particularly limited, but may be GPS (Global Positioning System), GLONASS, or a quasi-zenith satellite system using the quasi-zenith satellite "MICHIBIKI," for example.

<Operation of information processing system>
Next, the operation of the information processing system 1 will be described with a focus on the calculation of the estimated weight.

As described above, the CPU 11 of the smartphone 10 executes the weight management application 121 to acquire, record, process, and display various information for weight management of the user on the display unit 13 as weight management information.

FIG. 4 is a diagram showing an example of a weight management screen 30 including weight management information.
A weight management screen 30 is displayed on the display unit 13 of the smartphone 10 . At the top of the weight management screen 30, the initial weight of the user stored in the personal data 124, the weight of the user at the time the weight management information is displayed (hereinafter referred to as "current weight 31"), and the weight set by the user. The calculated target weight, the difference between the current weight 31 and the target weight, and the like are displayed. A target graph 32 and a performance graph 33 are displayed at the bottom of the weight management screen 30 . The target graph 32 represents the transition of the ideal body weight from the initial body weight to the target body weight in the case of weight loss during the set weight loss period. A performance graph 33 represents the transition of the actual body weight from the start of weight reduction to the present. Contents of the weight management information displayed on the weight management screen 30 are not limited to those shown in FIG. amount) may be included. By displaying the weight management screen 30 and checking the weight management information on a daily basis, the user can grasp the achievement status of the weight loss plan, and can recognize the actions required to achieve the weight loss plan. can. Note that although the case where the purpose of weight management is weight loss is exemplified here, the purpose is not limited to this, and the purpose of weight management may be maintenance of a proper weight, weight increase, or the like. Also, the weight management screen 30 can be used simply for the purpose of knowing the weight and its transition.

When the weight management information is displayed, if the weight measurement value (weight measurement value data 126) measured by the weight scale is input by the user, the weight management information includes the measurement value as the current weight 31. Is displayed. However, there is a demand for reducing the frequency of weight measurement because it is troublesome for the user to measure with a weight scale each time. Also, when the user wants to check the weight management information, the weight scale is not always at hand.

Therefore, in the information processing system 1 of the present embodiment, the estimated weight of the user is calculated when the measured value of the weight measured by the weight scale is not input. Then, instead of the measured weight, the estimated weight is displayed as the current weight 31 in the weight management information. Thereby, the user can confirm the weight management information including the current weight 31 without measuring the weight every time.

The estimated weight is calculated according to the calculation algorithm represented by the calculation algorithm data 127 based on the calculation data D. As described above, the calculation data D includes the calorie intake data 122, the calorie consumption data 123, the personal data 124, and the stress data 125. FIG. Hereinafter, the time point at which the initial weight is input (that is, the time point at which weight control is started) is denoted as t ₀ , the time point at which the estimated weight is calculated is t _e , and the period from time t ₀ to time t _e is denoted as T _e . Time _t0 corresponds to a "fixed reference time". Also, the initial weight corresponds to "a reference measurement value of the user's weight measured by a weight scale at the reference point in time".

The estimated body weight calculation algorithm of the present embodiment calculates the estimated body weight by the following multiple regression equation (1).
y _e = w ₁ x ₁ + w ₂ x ₂ + w ₃ x ₃ + w ₄ x ₄ + w ₅ x ₅ + w ₆ x ₆ + w ₀ x ₀ (1)
Here, y _e is the estimated weight (objective variable) at time t _e , x ₀ to x ₆ are explanatory variables, and w ₀ to w ₆ are coefficients of the explanatory variables. The coefficients w ₀ to w ₆ can also be rephrased as weights of the explanatory variables x ₀ to x ₆ . Note that the explanatory variable x ₀ is a constant “1” and the term “w ₀ x ₀ ” is equal to the coefficient w ₀ . Therefore, the _coefficient w0 corresponds to the bias in multiple regression equation (1). The values of coefficients w ₀ to w ₆ are stored in calculation algorithm data 127 .

_The explanatory variable x1 is the value of the calorie intake (or the value corresponding to the value of the calorie intake) in the period T _e derived from the calorie intake data 122 . _The explanatory variable x2 is the calorie consumption value (or the value corresponding to the calorie consumption value) in the period T _e derived from the calorie consumption data 123 . The explanatory variable _x3 is the height value (or the value corresponding to the height value) in the personal data 124 . The explanatory variable _x4 is the initial weight value (or the value corresponding to the initial weight value) in the personal data 124 . The explanatory variable _x5 is an age value (or a value corresponding to the age value) in the personal data 124 . The explanatory variable x ₆ is the stress intensity value (or the value corresponding to the stress intensity value) in the period T _e derived from the stress data 125 . Thus, the multiple regression equation (1) includes at least values derived from the calorie intake data 122 and values derived from the calorie consumption data 123 as explanatory variables. "Value corresponding to calorie intake", "value corresponding to calorie consumption", "value corresponding to height", "value corresponding to initial weight", "value corresponding to age" The “corresponding value” and the “value corresponding to the stress intensity value” may be, for example, values obtained by converting each value according to a predetermined conversion rule. The explanatory variables are not limited to x ₀ to x ₆ described above, and instead of some of the explanatory variables x ₀ to x ₆ or together with the explanatory variables x ₀ to x ₆ , any variable that is directly or indirectly correlated with body weight The amount of may be used as an explanatory variable. For example, at least one of a parameter representing sleep time and a parameter representing sleep quality may be added as an explanatory variable. Among these, the parameter representing sleep quality is a parameter derived based on sleep time, REM sleep time, non-REM sleep time, etc. For example, a score or index (A, B, C, etc.) representing the degree of quality ).

The CPU 11 acquires the values of the coefficients w ₀ to w ₆ from the calculation algorithm data 127, acquires the values of the explanatory variables x ₀ to x ₆ from the calculation data D, and calculates the estimated weight y _e according to the multiple regression equation (1). Calculate

Also, at time points t ₁ , t ₂ , . . . , and _tn after time point t ₀ , it is assumed that weight measurement values _{Y 1} , Y ₂ , . In this case, the coefficients w ₀ to w ₆ are adjusted by a well-known method so that the objective function L represented by the following equation (2) is minimized at time t _n .
L=(Y ₁ −y ₁ ) ² +(Y ₂ −y ₂ ) ² + . . . +(Y _n −y _n ) ² (2)
_{where y 1 , y 2 , . _ _} is. After the coefficients w ₀ to w ₆ are adjusted, the coefficients w ₀ to w ₆ in the calculation algorithm data 127 are updated to the adjusted values. At time t _n , the difference between the estimated weight calculated based on the coefficients w ₀ to w ₆ after adjustment and the measured value Y _n is the estimated weight calculated based on the coefficients w ₀ to w ₆ before adjustment. , is smaller than the difference between the measured value _Yn . That is, when the measured weight _Yn of the user is obtained, the calculation algorithm is adjusted so that the difference between the estimated weight and the measured weight _Yn is reduced. Further, the CPU 11 acquires the calorie intake data 122 and the calorie consumption data 123 each time the measured value _Yn of the user's weight is acquired after time _t0 , derives the estimated body weight, and The calculation algorithm is adjusted so that the difference between the body weight and the obtained weight measurement value _Yn is reduced.

The adjustment of the coefficients w ₀ to w ₆ in the multiple regression equation (1) is performed by learning a machine learning model using the weight measurement value Y _n and the explanatory variables x ₀ to x ₆ as teacher data (generating classifiers by machine learning , update process). In this case, the calculation algorithm data 127 corresponds to a machine learning model (classifier). By inputting the explanatory variables x ₀ to x ₆ to the learned machine learning model, the estimated weight y _e can be obtained as an output.

Instead of using the multiple regression equation (1) with the estimated weight y _e as the objective variable, the body weight variation Δy _e from the point in time when the last weight measurement value was obtained (time point t _x ) is used as the objective variable. A multiple regression equation may be used. In this case, the estimated body weight is calculated by adding the measured weight value specified at time _tx to the amount of variation Δy _e calculated by the multiple regression equation. In addition, when the amount of change in body weight Δy _e is used as the objective variable, the value of the calorie intake from time t _x to time t _e may be used as the explanatory variable x ₁ , and the explanatory variable x 2 may be used as the explanatory variable x ₂ at time t A value of _calorie consumption from _x to time te may be used. Further, when the weight measurement value Y _n is obtained, the difference ΔY _n between the weight measurement value Y _n and the previous weight measurement value Y _n−1 can be used as teacher data.

Next, details of various processes executed for displaying weight management information in the information processing system 1 will be described. In the information processing system 1, a calorie intake data update process, a calorie consumption data update process, a stress data update process, and a weight management information display process are executed. The calorie intake data update process is executed to identify the calorie intake of the user and update the calorie intake data 122 . The calorie consumption data update process is executed to identify the calorie consumption of the user and update the calorie consumption data 123 . The stress data update process is executed to identify the stress intensity of the user and update the stress data 125 . The weight management information display process is executed to display the weight management information, and includes the estimated weight calculation process described above. A control procedure for each process will be described below.

FIG. 5 is a flowchart for explaining the control procedure of the calorie intake data update process.
The calorie intake data update process is executed by the CPU 11 of the smart phone 10 . The calorie intake data update process is started, for example, when the weight management application 121 is executed on the smartphone 10 and the user performs an instruction to input the calorie intake. Here, as a method of inputting the calorie intake in the weight management application 121, a method of specifying the calorie intake from the analysis result of the meal image captured by the camera 15, a method of specifying the calorie intake from the content of the meal input by the user, Also, a case where there is a method for the user to directly input the calorie intake will be described as an example.

When the calorie intake data update process is started, the CPU 11 determines whether or not an instruction to photograph a meal using the camera 15 has been issued (step S101). YES"), the camera 15 is caused to take a picture, and the image data of the meal is acquired and stored in the memory 12 (step S102).

The CPU 11 performs image recognition processing on the image data of the meal to specify the content (menu and amount) of the meal included in the image (step S103). Note that the image data of the meal may be transmitted to an external server capable of executing the image recognition process, and data relating to the content of the specified meal may be received from the external server.

The CPU 11 identifies the calorie intake based on the content of the identified meal (step S104). Here, the CPU 11 refers to a database in which the calorie intake is associated with each menu to specify the calorie intake of the meal. The database may be stored in the memory 12, or may be stored in an external device. In addition, when the above-described external server also has a function of specifying the calorie intake corresponding to the content of the meal, the data related to the content of the meal and the calorie intake may be collectively received from the external server.

When the calorie intake is specified, the CPU 11 updates the calorie intake data 122 (step S105). For example, the identified calorie intake value is added to the calorie intake data 122 in association with the meal date and time. Therefore, the calorie intake data 122 records the calorie intake from daily meals from the start of weight management. Also, based on the calorie intake data 122, the total calorie intake from the start of weight management can be specified.

On the other hand, if it is determined in step S101 that the camera 15 is not instructed to photograph the meal ("NO" in step S101), the CPU 11 determines whether or not the user has input the details of the meal ( step S106). The method of inputting the meal content is not particularly limited, but for example, a method in which menu candidates are displayed according to the keyword input by the user, and when one menu is selected, the menu is entered as the meal content. may be used.

When it is determined that the meal content has been input ("YES" in step S106), the CPU 11 advances the process to step S104 and specifies the calorie intake based on the input meal content.

When it is determined that the meal content has not been input ("NO" in step S106), the CPU 11 receives input of calorie intake from the user (step S107). Here, the CPU 11 causes the display unit 13 to display a text box for entering the calorie intake, and specifies the numeric value entered in the text box as the calorie intake. Such a method of directly inputting the calorie intake can be suitably used, for example, when the calorie is clearly indicated on the menu of a restaurant or on the packaging of food. When the calorie intake is input, the CPU 11 advances the process to step S105 to update the calorie intake data 122 .

When the process of step S105 ends, the CPU 11 ends the calorie intake data update process.
The calorie intake data update process is desirably performed for each meal. Therefore, when the calorie intake data update process has not been executed at each predetermined time in the morning, noon, and night, a notification prompting the user to input the calorie intake (for example, a display on the display unit 13 ) may be performed.

FIG. 6 is a flowchart for explaining the control procedure of the calorie consumption data update process.
The calorie consumption data update process is executed by the CPU 11 of the smart phone 10 and the CPU 21 of the electronic timepiece 20 . FIG. 6 also shows calorie consumption data update processing on the smartphone 10 side (processing executed by the CPU 11) and calorie consumption data update processing on the electronic timepiece 20 side (processing executed by the CPU 21). The calorie consumption data update process is performed, for example, when it is detected that the user wears the electronic watch 20, or when a user operation instructing detection of exercise intensity is performed on the smartphone 10 or the electronic watch 20. be started.

When the calorie consumption data update process is started, the CPU 21 of the electronic timepiece 20 acquires measurement data (detection results) of the sensor section 26 (step S201). Here, the CPU 21 acquires measurement data detected at a predetermined sampling frequency by the triaxial acceleration sensor 261, triaxial gyro sensor 262, and triaxial geomagnetic sensor 263, and stores it in the memory 22. FIG.

The CPU 21 determines whether measurement data for the determination period has been accumulated (step S202). The determination period may be a fixed period such as 10 minutes or 1 hour, or a period until the end of exercise by the user (for example, until the acceleration detected by the triaxial acceleration sensor 261 stabilizes at a predetermined value or less). period).

The CPU 21 determines the exercise intensity of the user based on the measurement data for the determination period (step S203). The method of determining the exercise intensity is not particularly limited, but for example, the type of exercise (walking, running, etc.) and its mode (moving speed, duration, etc.) are specified based on the measurement data of each sensor of the sensor unit 26. Then, a method of determining the exercise intensity based on the specified type and mode of exercise may be used.

The CPU 21 transmits exercise intensity data related to the determined exercise intensity to the smartphone 10 (step S204).

On the other hand, when the calorie consumption data update process is started, the CPU 11 of the smartphone 10 calculates the user's basal metabolic rate based on the personal data 124 (step S301). The basal metabolic rate can be calculated, for example, from the Harris-Benedict equation representing the relationship between body weight, height, age, and basal metabolic rate.

Upon receiving the exercise intensity data from the electronic timepiece 20, the CPU 11 calculates the calorie consumption based on the basal metabolic rate calculated in step S301 and the received exercise intensity data, and updates the calorie consumption data 123 (step S302). ). The calorie consumption is obtained by adding the calorie consumed by the activity to the basal metabolic rate, and can be calculated by multiplying the basal metabolic rate by a coefficient corresponding to the exercise intensity. It is known that the calorie consumption is about basal metabolic rate x 1.2 in a state of almost no exercise, and about basal metabolic rate x 1.9 in a case of very vigorous exercise. The CPU 11 converts the exercise intensity data into the above coefficient and multiplies it by the basal metabolic rate to calculate the calorie consumption. If the exercise intensity data is not received, it is estimated that the patient was in a resting state during the period in which the exercise intensity data was not received, and a coefficient ("1.2") corresponding to a state of almost no exercise is applied. You may The identified calorie consumption may be added to the calorie consumption data 123 in association with the date and time each time it is identified, or may be added to the calorie consumption data 123 by summing up the calorie consumption per day. good. According to such calorie consumption data 123, the total calorie consumption from the start of weight management can be specified.

When step S302 ends, the CPU 11 determines whether or not an instruction to end the calorie consumption data update process has been issued (step S303). The instruction may be an operation by the user to instruct the end of the calorie consumption data update process, an operation to turn off the power, or the like. If the CPU 11 determines that the above instruction has not been issued ("NO" in step S303), the CPU 11 returns the process to step S302, and if it determines that the above instruction has been issued ("NO" in step S303). YES"), terminate the calorie consumption data update process.

Further, when step S204 ends, the CPU 21 determines whether or not an instruction to end acquisition of measurement data and generation of exercise intensity data has been issued (step S205). The instruction may be an operation by the user to instruct acquisition of measurement data and generation of exercise intensity data, an operation to turn off the power, or the like. Alternatively, when the end of the user's exercise is detected based on the detection result by the sensor unit 26, it may be considered that an instruction to end the detection has been issued. If the CPU 21 determines that the above instruction has not been issued ("NO" in step S205), the CPU 21 returns the process to step S201, and if it determines that the above instruction has been issued ("NO" in step S205). YES"), terminate the calorie consumption data update process.

FIG. 7 is a flowchart for explaining a control procedure for stress data update processing.
The stress data update process is executed by the CPU 11 of the smart phone 10 and the CPU 21 of the electronic timepiece 20 . In FIG. 7, the stress data update process (process executed by the CPU 11) on the smartphone 10 side and the stress data update process (process executed by the CPU 21) on the electronic timepiece 20 side are shown together. The stress data update process is started when a predetermined stress data update timing comes while the electronic timepiece 20 is worn by the user. The stress data update timing is not particularly limited, but is the timing when a predetermined period of time (for example, one hour, one day, etc.) has passed since the last time the stress data 125 was updated, or the timing when the user wears the electronic timepiece 20. and so on.

When the stress data update process is started, the CPU 21 of the electronic timepiece 20 acquires the measurement data of the stress marker detector 27 (step S401). The CPU 11 also determines the stress intensity based on the measurement data, and transmits stress intensity data including the determination result to the smartphone 10 (step S402). The stress intensity determination result may be expressed as a numerical value representing the stress intensity. Alternatively, the secretion amount (for example, concentration) of cortisol may be used as it is as the determination result of the stress intensity.

Upon receiving the stress intensity data, the CPU 11 of the smartphone 10 updates the stress data 125 by reflecting the content of the stress intensity data in the stress data 125 (step S501). The content of the stress intensity data is added to the stress data 125 in association with the received date and time. Therefore, based on the stress data 125, it is possible to specify the transition of the stress intensity from the start of weight management.

When the process of step S401 ends, the CPU 21 ends the stress data update process. Further, when the process of step S501 ends, the CPU 11 ends the stress data update process.

FIG. 8 is a flowchart for explaining the control procedure of weight management information display processing.
The weight management information display process is executed by the CPU 11 of the smart phone 10 . The weight management information display process is started, for example, when the weight management application 121 is executed on the smartphone 10 and the user performs an instruction to display the weight management information.

When the weight management information display process is started, the CPU 11 determines whether or not it is time to notify the user to input a measured weight value (step S601). Notification timing is set in step S607, which will be described later. When it is determined that it is the notification timing (step S602), the CPU 11 causes the display unit 13 to display the notification screen 40 for prompting the input of the measured weight value. Note that even if step S607 has not yet been executed and the notification timing has not been set, the CPU 11 determines that it is the notification timing and executes step S602. The display of the notification screen 40 by the display unit 13 corresponds to "notification for prompting the user to input the measured weight value". Further, the control by which the CPU 11 causes the display unit 13 to display the notification screen 40 corresponds to "notification control". Note that the notification is not limited to the display by the display unit 13, and may be, for example, a notification by voice guidance.

FIG. 9 is a diagram showing the notification screen 40. As shown in FIG.
The notification screen 40 displays a text box 41 for inputting a measured weight value and a registration button 42 . By performing an operation of selecting the registration button 42 with the measured weight value entered in the text box 41 , the measured weight value is stored in the weight measurement value data 126 .

Returning to FIG. 8, when step S602 ends, or when it is determined that the notification timing is not reached in step S601, the CPU 11 uses the calorie intake data 122, the calorie consumption data 123, the personal data 124, and the stress data 125 to A value used for calculating the estimated weight is acquired (step S603). For example, the CPU 11 acquires the value of the total calorie intake from the start of weight control from the calorie intake data 122 as the explanatory variable x1 of the multiple regression equation ( ₁ ). Further, the CPU 11 acquires the total calorie consumption value from the start of weight management from the calorie consumption data 123 as the explanatory variable x2 of the multiple regression equation ( ₁ ). The CPU 11 also acquires the user's height, initial weight, and age from the personal data 124 as the explanatory variables x ₃ to x ₅ of the multiple regression equation (1). The CPU 11 also obtains from the stress data 125 a value representing the cumulative value of stress intensity from the start of weight management as the explanatory variable x6 of the multiple regression equation ( ₁ ). The cumulative value of stress intensity may be, for example, the sum of representative values (for example, average values) of stress intensity for each day.

The CPU 11 calculates the estimated body weight _ye based on the calculation algorithm data 127 (step S604). That is, the CPU ₁₁ calculates the multiple _{regression equation} ( ₁ ) to calculate the estimated body weight y _e . The CPU 11 stores the calculated estimated body weight in the memory 12 as estimated body weight data 128 .

The CPU 11 determines whether step S602 has been executed and a weight measurement value has been input (step S605). If it is determined that the measured weight value has been input ("YES" in step S606), the CPU 11 determines that the difference between the estimated weight ye calculated in step _S604 and the input measured weight value is Calculation algorithm data 127 is modified so as to be reduced (step S606). That is, as described above, the coefficients w ₀ to w ₆ of the multiple regression equation (1) are adjusted so that the objective function (2) is minimized, and the values of the coefficients w ₀ to w ₆ in the calculation algorithm data 127 are changed to , update to the adjusted value.

The CPU 11 sets notification timing for displaying the notification screen 40 next time (step S607). Here, the notification timing is set such that the smaller the difference between the estimated weight ye calculated in step _S604 and the input measurement value of the weight, the lower the display frequency of the notification screen 40 . _The estimated weight _{y e} , the difference from the input measured weight value becomes smaller, and the estimation accuracy of the estimated weight y _e increases. The higher the estimation accuracy of the estimated weight y _e , the longer the period during which an appropriate estimated weight y _e (estimated weight y _e in which the error from the measured value is within a predetermined allowable range) can be calculated. That is, the display frequency of the notification screen 40 can be reduced. For example, if the adjustment of the coefficients w ₀ to w ₆ progresses sufficiently, the estimated weight y _e can be obtained with sufficient accuracy to be used for the purpose of weight management even if the measured weight value is input about once a month. can be calculated.

When step S607 ends, the CPU 11 causes the display unit 13 to display weight management information (for example, the weight management screen 30 shown in FIG. 4) including the measured weight as the current weight 31 (step S608). Here, a sign indicating that the current weight 31 is a measured value (actual value) may be displayed together with the current weight 31 .

On the other hand, if it is determined in step S605 that the measured weight value has not been input ("NO" in step S605), the CPU 11 sets the current weight 31 to the estimated weight y _e calculated in step S604. is displayed on the display unit 13 (for example, the weight management screen 30 shown in FIG. 4) (step S609). Here, an indicator indicating that the current weight 31 is an estimated value may be displayed together with the current weight 31 .

When step S608 or step S609 ends, the CPU 11 ends the weight management information display process.

The weight management information display process may be automatically started when the weight management application 121 is launched, or may be automatically started at predetermined processing timing in the weight management application 121 . Here, the predetermined processing timing may be when the calorie intake data update process, the calorie consumption data update process, or the stress data update process is executed. By executing the weight management information display process together with each of these update processes and displaying the weight management information, it is possible to show the user in real time the change in weight at the timing when the calorie intake and calorie consumption are updated. Therefore, it is possible to check in real time a change in body weight as a result of actions such as refraining from calorie intake or increasing the frequency of exercise, and it becomes easier to maintain motivation. In addition, by being able to check the current weight in a timely manner, it is possible to appropriately determine the next action for weight management.

<Modification>
In the above embodiment, the estimated body weight y _e is calculated according to a calculation algorithm that uses the stress intensity of the stress data 125. Alternatively, the estimated weight y _e is calculated according to a calculation algorithm that does not use the stress intensity. The weight y _e may be corrected based on the stress intensity of the stress data 125 . Hereinafter, the estimated body weight after correction based on the stress data 125 is referred to as "corrected estimated body weight". It is known that even when weight loss is performed by the same method, the higher the stress intensity, the lower the effect of weight loss. Therefore, by correcting the estimated body weight _ye so that the amount of weight decrease decreases as the stress intensity indicated by the stress data 125 increases, a highly accurate corrected estimated body weight that takes into account the stress intensity can be obtained. By correcting the estimated weight calculated according to the calculation algorithm based on the stress data 125 ( In other words, when the stress intensity is the main error factor in the estimated body weight, the adjustment of the calculation algorithm described in the above embodiment may be omitted.

In addition, in the weight management information display process, when the measured weight value measured by the weight scale is input, the measured weight value, the corrected estimated weight after correction based on the stress data 125, A correction algorithm based on the stress data 125 may be adjusted so that the difference between . For example, in the case of a correction algorithm that multiplies the estimated weight calculated according to the calculation algorithm by a coefficient corresponding to the stress data 125, the correction algorithm coefficient may be adjusted. It is known that there are individual differences in the effect of stress intensity on the amount of weight fluctuation. can be calculated.

The smartphone 10 as an information processing device according to the present modification includes a CPU 11 as a processing unit. Obtaining calorie consumption data 123 as calorie consumption information, deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data D including the calorie intake data 122 and the calorie consumption data 123, and calculating the stress intensity of the user. The stress data 125 as stress information related to the weight is acquired, and the derived estimated body weight is corrected based on the stress data 125 .

<effect>
As described above, the smartphone 10 as an information processing device according to the present embodiment includes the CPU 11 as a processing unit. The calorie consumption data 123 is acquired, the user's estimated weight is derived according to a predetermined calculation algorithm based on the calculation data D including the calorie intake data 122 and the calorie consumption data 123, and the weight of the user measured by a weight scale. is obtained, the calculation algorithm is adjusted so that the difference between the estimated weight and the measured weight is reduced. According to this, it is possible to adjust the calculation algorithm so that an estimated weight that is closer to the measured weight can be derived. Therefore, the weight can be estimated more accurately than the method of estimating the weight using a uniform formula each time. In addition, the accuracy of weight estimation is improved as the calculation algorithm is adjusted each time a user's weight measurement is taken, thereby customizing the calculation algorithm to more accurately estimate the weight of that particular user. can be progressively improved. As a result, even if the frequency of weight measurement is reduced, the accuracy of weight estimation can be maintained. Therefore, the user can accurately grasp the current weight from the estimated weight while reducing the time and effort of measuring the weight using the weight scale. Therefore, weight control can be performed without difficulty.

The CPU 11 also obtains stress data 125 relating to the stress intensity of the user, and derives an estimated body weight based on calculation data D including the stress data 125 according to a calculation algorithm using the stress data 125 . As a result, the weight can be estimated with high accuracy, taking into account the effect of stress intensity on weight fluctuation. In addition, it is known that the magnitude of the effect of stress intensity on body weight fluctuations varies greatly among individuals. The calculation algorithm can be modified to reflect individual differences. _In the example of the above embodiment, the coefficient w6 of the explanatory variable x6 related to the stress intensity in the multiple regression equation ( ₁ ) is adjusted so that the difference between the estimated weight and the measured weight is reduced. The absolute value of the coefficient _w6 increases as the user is more likely to be affected by . According to the multiple regression equation (1) in which the coefficient _w6 is adjusted in this way, it is possible to appropriately estimate body weight by considering individual differences in the influence of stress intensity.

Further, in the modified example, the CPU 11 acquires stress data 125 relating to the stress intensity of the user, and corrects the derived estimated body weight based on the stress data 125 . In this way, even with the method of correcting the derived estimated weight ex post facto, it is possible to estimate the weight with high accuracy in consideration of the stress intensity.

The stress data 125 also includes information on the secretion amount of stress markers in the user's body. Thereby, regardless of whether the user is aware of the stress, the weight can be estimated with high accuracy in consideration of the stress intensity.

Also, by using cortisol as a stress marker, it is possible to appropriately identify the user's stress intensity from the amount of cortisol secreted.

In addition, the CPU 11 acquires personal data 124 including at least one of the user's height, weight (initial weight), and age, and based on the calculation data D including the personal data 124, according to a calculation algorithm using the personal data 124, Derive an estimated weight. The magnitude of the influence of calorie intake and calorie consumption on body weight changes depending on factors such as the user's height, initial weight, and age. can do.

In addition, the calculation algorithm derives the estimated body weight by a multiple regression equation including the values derived from the calorie intake data 122 and the values derived from the calorie consumption data 123 as explanatory variables. The coefficient of the explanatory variable in the multiple regression equation is adjusted so that the difference between the estimated weight and the measured weight is reduced when the value is obtained. By adjusting the coefficients of the explanatory variables in this way, a multiple regression equation reflecting individual differences in the influence of each explanatory variable on the estimated body weight can be obtained. Further, by repeating adjustment of the coefficient (learning of the machine learning model), the accuracy of weight estimation can be progressively improved. In addition, it is possible to analyze the magnitude of the effect of each explanatory variable on the estimated body weight from the adjusted coefficients.

In addition, the CPU 11 acquires the initial weight as a reference measurement value of the user's weight measured by a weight scale at time t ₀ as a fixed reference time, and calculates the initial weight based on the calculation data D including the initial weight. Derive the estimated weight of the user according to a computational algorithm that uses As a result, the estimated body weight can be derived by estimating the amount of change (increase or decrease) in body weight from the initial body weight at time _t0 with high accuracy.

Further, each time the CPU 11 acquires the measured value of the weight of the user after the time point _t0 , the CPU 11 acquires the calorie intake data 122 and the calorie consumption data 123 to derive an estimated weight, and , the calculation algorithm is adjusted so that the difference from the obtained weight measurement value is reduced. As a result, it is possible to progressively improve the accuracy of estimating the body weight variation from the initial body weight at time _t0 .

In addition, the CPU 11 executes notification control to display a notification screen 40 on the display unit 13 to prompt the user to input the measured weight value. Perform notification control such that As a result, it is possible to minimize the frequency of the user's weight measurement while ensuring the accuracy of weight estimation.

In addition, the weight management application 121 as a program according to the present embodiment has a function of acquiring calorie intake data 122 related to the user's calorie intake, and acquiring calorie consumption data 123 related to the user's calorie consumption. a function of deriving an estimated weight of the user according to a predetermined calculation algorithm based on the calculation data D including the calorie intake data 122 and the calorie consumption data 123; and a function of adjusting the calculation algorithm so that the difference between the estimated weight and the measured weight is reduced when the measured value is obtained. According to this, it is possible to adjust the calculation algorithm so that an estimated weight that is closer to the measured weight can be derived. Therefore, the weight can be estimated more accurately than the method of estimating the weight using a uniform formula each time. In addition, the accuracy of weight estimation is progressive, as the calculation algorithm is adjusted each time a weight measurement is taken, customizing the calculation algorithm to more accurately estimate the weight of that particular user. can be substantially improved.

In addition, the information processing method according to the present embodiment includes steps of acquiring calorie intake data 122 relating to the calorie intake of the user, acquiring calorie consumption data 123 relating to the calorie consumption of the user, and calculating the calorie intake data 122 and the calorie consumption. a step of deriving an estimated weight of the user according to a predetermined calculation algorithm based on the calculation data D including the calorie data 123; and adjusting the calculation algorithm so that the difference with the weight measurement is reduced. According to this, it is possible to adjust the calculation algorithm so that an estimated weight that is closer to the measured weight can be derived. Therefore, the weight can be estimated more accurately than the method of estimating the weight using a uniform formula each time. In addition, the accuracy of weight estimation is progressive, as the calculation algorithm is adjusted each time a weight measurement is taken, customizing the calculation algorithm to more accurately estimate the weight of that particular user. can be substantially improved.

<Others>
It should be noted that the description in the above embodiment is an example of the information processing apparatus, program, and information processing method according to the present invention, and the present invention is not limited to this.
For example, the smartphone 10 may have a plurality of processors (for example, a plurality of CPUs), and the plurality of processors may perform the plurality of processes performed by the CPU 11 of the smartphone 10 of the above embodiment. In this case, the multiple processors correspond to the "processing unit". Further, the electronic timepiece 20 may have a plurality of processors (for example, a plurality of CPUs), and the plurality of processors execute the plurality of processes executed by the CPU 21 of the second device 20 of the above embodiment. good too. In these cases, multiple processors may be involved in a common process, or multiple processors may independently execute different processes in parallel.

Moreover, although the smart phone 10 was illustrated as an information processing apparatus, it is not restricted to this, For example, a tablet terminal, PC (Personal Computer), etc. may be sufficient as an information processing apparatus.

Any wearable device (for example, a health care device such as an activity meter) that can identify the exercise intensity of the user may be used instead of the electronic watch 20 . In addition, in the case where the smartphone 10 is provided with a sensor unit capable of detecting the exercise state of the own device, and in the case where the exercise intensity of the user can be specified by the smartphone 10, the electronic clock 20 is omitted and the electronic The smartphone 10 may execute the processing that was executed by the watch 20 . Alternatively, the smartphone 10 may be omitted, and the electronic timepiece 20 may execute the processing that was executed by the smartphone 10 in the above embodiment. In this case, the electronic timepiece 20 corresponds to the "information processing device". Alternatively, the electronic timepiece 20 may be provided with a camera, and the calorie intake may be specified from the analysis result of the meal image captured by the camera.

Further, the method of specifying the exercise intensity is not limited to the method of specifying based on the detection result of the sensor unit 26 of the electronic timepiece 20 . For example, a method of specifying the exercise intensity based on the content and time of exercise input by the user, a method of directly inputting the exercise intensity level by the user, or the like may be used. Alternatively, the user's walking or running distance and pace may be specified based on the transition of the current position specified by the satellite radio wave reception processing unit 29, and the exercise intensity may be specified from the specified result. Further, when the exercise intensity is specified by the smartphone 10 using the current position, a satellite radio wave reception processing unit may be provided in the smartphone 10 and the current position may be acquired by the satellite radio wave reception processing unit.

Also, as a method of specifying the calorie consumption, the method of calculating from the basal metabolic rate and the exercise intensity was exemplified, but the calorie consumption is not limited to this, and any method that can specify the calorie consumption can be used.

In addition, in the above-described embodiment, as stress information, information related to the secretion amount of stress markers detected by the stress marker detection unit 27 of the electronic timepiece 20 was exemplified, but the present invention is not limited to this. The stress information may be generated based on the user's behavior (for example, sleeping hours and working hours) input on the weight management application 121 . For example, the stress information may be generated by specifying the stress intensity such that the stress intensity increases as the sleep time deviates from the appropriate range. Alternatively, the stress intensity may be directly input by the user's self-report.

Further, in the above-described embodiment, a machine learning model that uses multiple regression analysis is exemplified, but the present invention is not limited to this, and any machine learning model that outputs an estimated weight by inputting input data that correlates with body weight. can be used. For example, a machine learning model using a neural network or support vector machine may be used.

In addition, in the weight management information display process (FIG. 8) of the above-described embodiment, the user inputs the measured weight value in response to the display of the notification screen 40 for prompting the input of the measured weight value. However, the present invention is not limited to this, and the user may be able to voluntarily input the weight measurement value when the notification screen 40 is not displayed. Also, the weight measurement value may be input irregularly.

Also, in the above description, an example of using the memory 12 as a computer-readable medium for the program according to the present invention has been disclosed, but the present invention is not limited to this example. As other computer-readable media, it is possible to apply information recording media such as HDDs, SSDs, flash memories, and CD-ROMs. A carrier wave is also applied to the present invention as a medium for providing program data according to the present invention via a communication line.

Further, the detailed configurations and detailed operations of the components of the smartphone 10 and the electronic timepiece 20 in the above-described embodiments can of course be changed as appropriate without departing from the gist of the present invention.

Although embodiments of the present invention have been described, the scope of the present invention is not limited to the above-described embodiments, but includes the scope of the invention described in the claims and equivalents thereof.
The invention described in the scope of claims originally attached to the application form of this application is additionally described below. The claim numbers in the appendix are as in the claims originally attached to the filing of this application.
[Appendix]
<Claim 1>
a processing unit,
The processing unit is
Acquiring calorie intake information related to the calorie intake of the user,
Acquiring calorie consumption information related to the user's calorie consumption,
deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
Adjusting the calculation algorithm so that a difference between the estimated weight and the measured weight value is reduced when the measured weight value of the user measured by a weight scale is obtained;
Information processing equipment.
<Claim 2>
The processing unit is
Acquiring stress information related to the user's stress intensity,
Deriving the estimated weight according to the calculation algorithm using the stress information based on the calculation data including the stress information;
The information processing device according to claim 1 .
<Claim 3>
The processing unit is
Acquiring stress information related to the user's stress intensity,
correcting the derived estimated weight based on the stress information;
The information processing device according to claim 1 .
<Claim 4>
4. The information processing apparatus according to claim 2, wherein said stress information includes information relating to a secretion amount of a stress marker in said user's body.
<Claim 5>
5. The information processing apparatus according to claim 4, wherein said stress marker is cortisol.
<Claim 6>
The processing unit is
obtaining personal information including at least one of height, weight and age of the user;
Deriving the estimated weight according to the calculation algorithm using the personal information, based on the calculation data including the personal information;
The information processing apparatus according to any one of claims 1 to 5.
<Claim 7>
The calculation algorithm derives the estimated body weight by a multiple regression equation including a value derived from the calorie intake information and a value derived from the calorie consumption information as explanatory variables,
The processing unit adjusts the coefficient of the explanatory variable in the multiple regression equation so that the difference between the estimated weight and the measured weight is reduced when the measured weight is obtained.
The information processing apparatus according to any one of claims 1 to 6.
<Claim 8>
The processing unit is
obtaining a reference measurement value of the user's weight measured by a weight scale at a certain reference time;
Based on calculation data including the reference measurements, deriving an estimated weight of the user according to the calculation algorithm using the reference measurements.
The information processing apparatus according to any one of claims 1 to 7.
<Claim 9>
The processing unit is
each time a measured value of the user's weight is obtained after the reference time point, the calorie intake information and the calorie consumption information are obtained to derive the estimated weight, and the derived estimated weight and the adjusting the calculation algorithm so that the difference from the obtained weight measurement value is reduced;
The information processing apparatus according to claim 8 .
<Claim 10>
The processing unit is
executing notification control for causing a notification unit to perform notification for prompting the user to input the measured weight value;
executing the notification control such that the frequency of the notification decreases as the difference between the estimated weight and the measured weight value decreases;
The information processing apparatus according to any one of claims 1 to 9.
<Claim 11>
to the computer,
A function of acquiring calorie intake information related to a user's calorie intake;
a function of acquiring calorie consumption information related to the user's calorie consumption;
a function of deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
a function of adjusting the calculation algorithm so that, when the measured value of the user's weight measured by a weight scale is acquired, the difference between the estimated weight and the measured value of the weight is reduced;
A program that realizes
<Claim 12>
An information processing method by a computer,
a step of acquiring calorie intake information related to the calorie intake of the user;
a step of acquiring calorie consumption information related to the user's calorie consumption;
deriving an estimated body weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
a step of adjusting the calculation algorithm so that, when the measured value of the user's weight measured by a weight scale is acquired, the difference between the estimated weight and the measured value of the weight is reduced;
A method of processing information, comprising:

1 information processing system 10 smart phone (information processing device)
11 CPU (processing unit)
12 memory 121 weight management application (program)
122 calorie intake data (calorie intake information)
123 calorie consumption data (calorie consumption information)
124 Personal Data (Personal Information)
125 stress data (stress information)
126 Weight measurement value data 127 Calculation algorithm data 128 Estimated weight data 13 Display unit (notification unit)
14 Operation reception unit 15 Camera 16 Wireless communication unit 17 Telephone communication unit 20 Electronic clock 21 CPU
22 memory 221 program 23 display unit 24 operation reception unit 25 clock unit 26 sensor unit 27 stress marker detection unit 28 wireless communication unit 29 satellite radio wave reception processing unit 30 weight management screen 40 notification screen D calculation data

Claims (12)

a processing unit,
The processing unit is
Acquiring calorie intake information related to the calorie intake of the user,
Acquiring calorie consumption information related to the user's calorie consumption,
deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
Adjusting the calculation algorithm so that a difference between the estimated weight and the measured weight value is reduced when the measured weight value of the user measured by a weight scale is obtained;
Information processing equipment. The processing unit is
Acquiring stress information related to the user's stress intensity,
Deriving the estimated weight according to the calculation algorithm using the stress information based on the calculation data including the stress information;
The information processing device according to claim 1 . The processing unit is
Acquiring stress information related to the user's stress intensity,
correcting the derived estimated weight based on the stress information;
The information processing device according to claim 1 . 4. The information processing apparatus according to claim 2, wherein said stress information includes information relating to a secretion amount of a stress marker in said user's body. 5. The information processing apparatus according to claim 4, wherein said stress marker is cortisol. The processing unit is
obtaining personal information including at least one of height, weight and age of the user;
Deriving the estimated weight according to the calculation algorithm using the personal information, based on the calculation data including the personal information;
The information processing apparatus according to any one of claims 1 to 5. The calculation algorithm derives the estimated body weight by a multiple regression equation including a value derived from the calorie intake information and a value derived from the calorie consumption information as explanatory variables,
The processing unit adjusts the coefficient of the explanatory variable in the multiple regression equation so that the difference between the estimated weight and the measured weight is reduced when the measured weight is obtained.
The information processing apparatus according to any one of claims 1 to 6. The processing unit is
obtaining a reference measurement value of the user's weight measured by a weight scale at a certain reference time;
Based on calculation data including the reference measurements, deriving an estimated weight of the user according to the calculation algorithm using the reference measurements.
The information processing apparatus according to any one of claims 1 to 7. The processing unit is
each time a measured value of the user's weight is obtained after the reference time point, the calorie intake information and the calorie consumption information are obtained to derive the estimated weight, and the derived estimated weight and the adjusting the calculation algorithm so that the difference from the obtained weight measurement value is reduced;
The information processing apparatus according to claim 8 . The processing unit is
executing notification control for causing a notification unit to perform notification for prompting the user to input the measured weight value;
executing the notification control such that the frequency of the notification decreases as the difference between the estimated weight and the measured weight value decreases;
The information processing apparatus according to any one of claims 1 to 9. to the computer,
A function of acquiring calorie intake information related to a user's calorie intake;
a function of acquiring calorie consumption information related to the user's calorie consumption;
a function of deriving an estimated weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
a function of adjusting the calculation algorithm so that, when the measured value of the user's weight measured by a weight scale is acquired, the difference between the estimated weight and the measured value of the weight is reduced;
A program that realizes A computer-based information processing method comprising:
a step of acquiring calorie intake information related to the calorie intake of the user;
a step of acquiring calorie consumption information related to the user's calorie consumption;
deriving an estimated body weight of the user according to a predetermined calculation algorithm based on calculation data including the calorie intake information and the calorie consumption information;
a step of adjusting the calculation algorithm so that, when the measured value of the user's weight measured by a weight scale is acquired, the difference between the estimated weight and the measured value of the weight is reduced;
A method of processing information, comprising:

Images