JP2023020789A - Program, method, and system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a meal suggestion program, a method for suggesting a meal, and a meal suggestion system that can easily figure out what a user should take.

SOLUTION: The program causes a processor of a computer to perform the steps of: continuously receiving blood sugar data acquired from a measurement instrument attached to a part of the body of a user; receiving an input of a meal record from the user; generating a blood sugar curve G changing with time, from the acquired blood sugar data; suggesting a nature B of a meal, which is a desirable meal B for the next meal estimated from the meal record and the blood sugar curve; and determining whether the user was able to follow the suggested desirable nature of meal.

SELECTED DRAWING: Figure 9

COPYRIGHT: (C)2023,JPO&INPIT

Description

The present disclosure relates to a meal suggestion program, a meal suggestion method, and a meal suggestion system for proposing appropriate meal content to a user.

Conventionally, systems have been proposed that detect changes in the blood sugar levels of subjects to be evaluated and contribute to the management of their health conditions.
For example, Patent Literature 1 discloses a system that points out lack of exercise and excessive food intake based on changes in the blood sugar level of the person to be evaluated that are continuously obtained.

JP 2019-164608 A

However, in the system described in Patent Literature 1, the user can be conscious of refraining from overeating, but there is no mention of specifically what kind of meal should be taken and at what timing. Therefore, it is difficult for the user to decide what to eat.

Therefore, an object of the present disclosure is to provide a meal suggestion system that allows a user to easily grasp what to specifically ingest.

A program according to one aspect of the present disclosure includes a step of continuously accepting blood glucose level data obtained from a measuring device worn on a part of a user's body, and a step of accepting an input of a meal record from the user. and, from the obtained blood sugar level data, a step of generating a blood sugar level curve that changes along the time series, and a step of suggesting a recommended meal mode for the next meal estimated from the meal record and the blood sugar level curve. and determining whether the user was able to comply with the suggested and recommended dietary pattern.

According to the present disclosure, it is possible for the user to easily grasp what should be specifically ingested.

BRIEF DESCRIPTION OF THE DRAWINGS It is a figure explaining the outline|summary of the meal suggestion system of this invention. 3 is a block diagram showing a functional configuration of a user terminal; FIG. It is a block diagram which shows the functional structure of a processing server. It is a figure explaining the blood glucose level data which a memory|storage part memorize|stores. It is a figure explaining the user data, schedule data, and activity record data which a memory|storage part memorize|stores. FIG. 4 is a diagram for explaining meal record data and calculation index data stored in a storage unit; BRIEF DESCRIPTION OF THE DRAWINGS It is a figure explaining the outline|summary of this embodiment. It is a figure explaining processing of a meal suggestion system. It is a figure which shows the example of a screen of an operating terminal. FIG. 9 is a block diagram showing the functional configuration of a processing server of the meal suggestion system according to the second embodiment; It is a figure which shows the tendency of a user, the aspect of recommended meal, and the graph of the said user's blood glucose level curve.

<First Embodiment>
A meal proposal system 1 according to a first embodiment of the present disclosure will be described below with reference to the drawings. It should be noted that the embodiments described below do not unduly limit the content of the present disclosure described in the claims. Moreover, not all the configurations described in the embodiments are essential constituent elements of the present disclosure. Further, in all the drawings for explaining the embodiments, common constituent elements are given the same reference numerals, and repeated explanations are omitted.

<Overview>
FIG. 1 is a diagram explaining an outline of a meal suggestion system 1 of the present invention.
As shown in FIG. 1, a meal suggestion system 1 according to the present embodiment is a system that suggests an appropriate mode of meal based on changes in information about a user's blood sugar level.

In this description, information about blood sugar levels includes blood sugar levels and glucose levels. The blood sugar level is a value indicating the glucose concentration in blood flowing through blood vessels. A glucose level is, for example, a value indicating the glucose concentration in interstitial fluid measured subcutaneously. It has been confirmed that there is a high correlation between the blood sugar level and the glucose level. Specifically, the glucose level changes so as to follow changes in the blood sugar level. Therefore, by evaluating the relatively easily detectable glucose level, changes in the blood sugar level can be evaluated.

In addition, the mode of eating in this description is a concept that includes the content of meals and the timing of meals. The contents of the meal may be the menu of the meal, the name of the food, or the types and amounts of nutrients contained in the meal. The timing of meals may be the time zone in which meals should be taken and the order of intake of each food item (nutrient).

In the following description, the configuration of the meal proposal system 1 will be described first, and then each process performed by the meal proposal system 1 will be described in detail.

<Overall composition>
The overall configuration of the meal proposal system 1 according to this embodiment will be described.
As shown in FIG. 1, the meal proposal system 1 includes a plurality of operation terminals (in FIG. 1, a user terminal 10A used by a user P1, a supervisor terminal 10B used by a supervisor P2, hereinafter collectively referred to as "operation terminals 10 ), the processing server 20, and the food database 30. The plurality of operation terminals 10 and the processing server 20 are connected via a network 80 so as to be able to communicate with each other. The network 80 is composed of a wired or wireless network.

The operation terminal 10 is a device operated by each user P1 and a supervisor P2 who supervises meals for each user. The operation terminal 10 is a smart phone or a tablet terminal. In addition, the operation terminal 10 is implemented by a stationary PC (Personal Computer), a laptop PC, or the like.

The operation terminal 10 is communicably connected to the processing server 20 via the network 80 . The operation terminal 10 is a wireless base station 81 compatible with communication standards such as 5G and LTE (Long Term Evolution), and a wireless LAN (Local Area Network) standard such as IEEE (Institute of Electrical and Electronics Engineers) 802.11. It is connected to the network 80 by communicating with a communication device such as a wireless LAN router 82 . As shown in FIG. 1 , the operation terminal 10 includes a communication IF (Interface) 12 , an input device 13 , an output device 14 , a memory 15 , a storage section 16 and a processor 19 .

The communication IF 12 is an interface for inputting and outputting signals so that the operation terminal 10 communicates with an external device.
The input device 13 is a device (for example, a keyboard, a touch panel, a touch pad, a pointing device such as a mouse, etc.) for receiving an input operation from a user.

The output device 14 is a device (display, speaker, etc.) for presenting information to the user.
The memory 15 temporarily stores programs and data processed by the programs, and is a volatile memory such as a DRAM (Dynamic Random Access Memory).

The storage unit 16 is a storage device for storing data, and is, for example, a flash memory or a HDD (Hard Disc Drive).
The processor 19 is hardware for executing an instruction set described in a program, and is composed of arithmetic units, registers, peripheral circuits, and the like.

The processing server 20 is a device that analyzes changes in the user's blood sugar level and behavior patterns such as diet and exercise, and proposes to the user an appropriate diet for the user. Appropriate diet for the user refers to diet necessary to minimize the risk of contracting obesity, hypertension, swelling, irregular menstruation, and other lifestyle-related diseases. That is, various meals are included to help the user maintain good health.

The processing server 20 is a computer connected to the network 80 . The processing server 20 includes a communication IF 22 , an input device 23 , an output device 24 , a processor 25 , a memory 26 and a storage 27 .

The communication IF 22 is an interface for inputting and outputting signals so that the processing server 20 communicates with an external device.
The input device 23 is a device (for example, a keyboard, a touch panel, a touch pad, a pointing device such as a mouse, etc.) for receiving an input operation from an administrator.
The output device 24 is a device (display, speaker, etc.) for presenting information to the administrator.

The processor 25 is hardware for executing an instruction set described in a program, and is composed of arithmetic units, registers, peripheral circuits, and the like.
The memory 26 temporarily stores programs and data processed by the programs, and is a volatile memory such as a DRAM (Dynamic Random Access Memory).
The storage 27 is a storage device for storing data, such as a flash memory or HDD (Hard Disc Drive).

The food database 30 is a database that accumulates information on various foods. The food database 30 stores, for example, various menus and the amount of each nutrient per unit mass contained in the food. In addition, in the food database 30, the amount of nutrients related to prepared dishes that are generally widely distributed, and the amount of nutrients related to food products with high distribution sold at food retail stores such as supermarkets and convenience stores are stored for each product. You may remember.
The food database 30 does not need to be composed of one database as shown in the figure, and may be composed of a plurality of servers, or may be a virtual server managed on the cloud.

<Configuration of operation terminal 10>
FIG. 2 is a block diagram showing the functional configuration of the operation terminal 10 that constitutes the meal suggestion system 1. As shown in FIG. As a situation in which the operation terminal 10 is operated, for example, in daily life, a scene in which a user who considers the contents of the meal of the day operates a smartphone used by the user is assumed.
As shown in FIG. 2, operation terminal 10 includes a plurality of antennas (antenna 111 and antenna 112), wireless communication units (first wireless communication unit 121 and second wireless communication unit 122) corresponding to each antenna, input It includes reception unit 130 (including keyboard 131 and display 132 ), audio processing unit 140 , microphone 141 , speaker 142 , camera 150 , storage unit 160 , and control unit 170 .

The operation terminal 10 also has functions and configurations not particularly shown in FIG. 2 (for example, a battery for retaining power, a power supply circuit for controlling power supply from the battery to each circuit, etc.). As shown in FIG. 2, each block included in the operation terminal 10 is electrically connected by a bus or the like.

The antenna 111 radiates a signal emitted by the operation terminal 10 as radio waves. Antenna 111 also receives radio waves from space and provides a received signal to first wireless communication section 121 .

The antenna 112 radiates a signal emitted by the operation terminal 10 as radio waves. Antenna 112 also receives radio waves from space and provides a received signal to second wireless communication section 122 .

The first wireless communication unit 121 performs modulation/demodulation processing and the like for transmitting and receiving signals via the antenna 111 so that the operation terminal 10 communicates with other wireless devices. The second wireless communication unit 122 performs modulation/demodulation processing for transmitting and receiving signals via the antenna 112 so that the operation terminal 10 communicates with other wireless devices. The first radio communication unit 121 and the second radio communication unit 122 are communication modules including a tuner, an RSSI (Received Signal Strength Indicator) calculation circuit, a CRC (Cyclic Redundancy Check) calculation circuit, a high frequency circuit, and the like. The first wireless communication unit 121 and the second wireless communication unit 122 perform modulation/demodulation and frequency conversion of wireless signals transmitted/received by the operation terminal 10 and provide received signals to the control unit 170 .

The input receiving unit 130 has a mechanism for receiving user input operations. Specifically, input accepting unit 130 includes a keyboard 131 and a display 132 . Note that the input reception unit 130 may be configured as a touch screen that detects the user's touch position on the touch panel by using, for example, a capacitive touch panel.

The keyboard 131 accepts input operations of the user of the operation terminal 10 . The keyboard 131 is a device for inputting characters, and outputs input character information to the control unit 170 as an input signal.

The display 132 displays data such as images, moving images, and text under the control of the control unit 170 . That is, the display 132 also functions as an output unit that outputs information to the user. The display 132 is implemented by, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display.

The audio processing unit 140 modulates and demodulates an audio signal. Audio processing section 140 modulates the signal supplied from microphone 141 and supplies the modulated signal to control section 170 . Also, the audio processing unit 140 provides an audio signal to the speaker 142 .
The audio processing unit 140 is implemented by, for example, a processor for audio processing. Microphone 141 receives a voice input and provides voice signal corresponding to the voice input to voice processing section 140 . The speaker 142 converts the audio signal given from the audio processing unit 140 into audio and outputs the audio to the outside of the operation terminal 10 .

The camera 150 is a device for receiving light with a light receiving element and outputting it as a photographed image. The camera 150 is, for example, a depth camera that can detect the distance from the camera 150 to the subject.

The storage unit 160 is configured by, for example, a flash memory or the like, and stores data and programs used by the operation terminal 10 . In one aspect, storage unit 160 stores user information 161 .

The user information 161 is information of a user who utilizes the meal suggestion system 1 using the operation terminal 10 . The user information 161 includes the name of the user, contact information such as the user's e-mail address, information used for network communication such as the IP address of the user terminal, and the like.

The control unit 170 reads programs stored in the storage unit 160 and executes commands included in the programs, thereby controlling operations of the operation terminal 10 . The control unit 170 is, for example, an application program installed in the operation terminal 10 in advance. The control unit 170 functions as an operation reception unit 171 , a transmission/reception unit 172 , a data processing unit 173 , and a display processing unit 174 by operating according to programs.

The operation accepting unit 171 performs processing for accepting a user's input operation to the input device such as the keyboard 131 .

The transmission/reception unit 172 performs processing for the operation terminal 10 to transmit/receive data to/from an external device such as the processing server 20 according to a communication protocol.

The data processing unit 173 performs processing according to a program to perform computation on data input by the operation terminal 10 and outputs the computation result to a memory or the like.

The display processing unit 174 performs processing for presenting information to the user. The display processing unit 174 performs processing for displaying a display image on the display 132, processing for outputting sound to the speaker 142, processing for causing the camera 150 to vibrate, and the like.

<Functional Configuration of Processing Server 20>
FIG. 3 is a block diagram showing the functional configuration of the processing server 20 that constitutes the meal proposal system 1. As shown in FIG.
As shown in FIG. 3 , the processing server 20 functions as a communication section 201 , a storage section 202 and a control section 203 .

The communication unit 201 performs processing for the processing server 20 to communicate with an external device.

The storage unit 202 stores data and programs used by the processing server 20 . The storage unit 202 stores at least user data, schedule data, activity record data, meal record data, and calculation index data.

The blood sugar level data is data relating to the user's blood sugar level obtained from a measuring device attached to a part of the user's body. The meter is a sensor that measures the glucose level of interstitial fluid in the user's subcutaneous cells. The measuring instrument is provided with a thin core-shaped probe. The probe has an electrode that detects, as a current value, electrons generated by reacting glucose contained in subcutaneous interstitial fluid with an enzyme. The measuring device detects the glucose level by transmitting a signal according to the magnitude of the detected current value. The measurement principle is not limited to such an enzyme electrode method, and an affinity sensor using a glucose-binding substance may be used, and the detection method may also be based on optical signals.
The measuring device transmits acquired blood sugar level data to the processing server 20 via the operation terminal 10 by performing short-range wireless communication with the operation terminal 10 .

As the blood sugar level data, the glucose level may be used as it is, or a value predicted from the glucose level may be approximated to the blood sugar level based on the correlation between the change characteristics of the glucose level and the blood sugar level.
FIG. 4A is a diagram showing an example of blood sugar level data. FIG. 4B is a diagram showing an example of a blood sugar level curve generated from blood sugar level data.

As shown in FIG. 4A, the blood sugar level data is a data group obtained by measuring blood sugar levels that change every moment based on a predetermined sampling period such as every minute or every 10 minutes. The blood sugar level data includes at least a user ID for identifying a user, a measured (or approximated) blood sugar level value, and an acquisition time. Note that the sampling period can be set arbitrarily.

As shown in FIG. 4B, the blood sugar level curve is a curve that geometrically expresses changes in blood sugar levels defined by arranging blood sugar levels in chronological order. In this figure, the dashed-dotted line indicates the average blood sugar level (average blood sugar level) indicated by the blood sugar level curve. The blood glucose level curve is constantly updated during measurement by the meter, and by shortening the sampling period, continuously expresses changes in the blood glucose level.

The user data is a data group related to user's personal information. User data is input by a user's input operation. FIG. 5A is a diagram showing an example of a user database that stores user data.
The user database shown in FIG. 5A includes a "user ID" field, a "user name" field, an "attribute" field, a "physical information" field, a "health information" field, a "residence" field, and " a contact field;

The "user ID" field stores an ID that identifies a user.

The "user name" field stores the name of the user corresponding to the user ID.

The "attribute" field stores information about age and gender as attributes of the user corresponding to the user ID. The attribute field may include information such as date of birth, nationality, place of birth, native language, and the like.

In the "physical information" field, information such as height, weight, body fat percentage, BMI value, and bone density is stored as the physical information of the user corresponding to the user ID. The physical information may include items related to the user's body, such as muscle mass, menstrual cycle, and frequency of regular bowel movements, as well as information related to appropriate sleeping hours perceived by the user himself/herself.

In the "health information" field, health information of the user corresponding to the user ID is stored, for example, medical history, medication history, lifestyle habits, and action targets indicating future health status targets. Note that the health information field may include information about the tendency of mood changes that the user perceives (easy to get depressed, easy to recover, etc.).

The "place of residence" field stores information about the place of residence of the user corresponding to the user ID.

The "contact" field stores a communication address used for contacting the user, such as the user's email address or SNS user ID.
Note that the user database may include other information as the user's personal information.

The schedule data is a data group regarding the user's schedule. The schedule data is input by a user, by cooperation with a schedule management tool used by the user on the operation terminal 10, by synchronization of account information, or the like. FIG. 5B is a diagram showing an example of a schedule database that stores schedule data.

The schedule database shown in FIG. 5B includes a "task ID" field, a "user ID" field, a "date" field, a "task name" field, an "scheduled start time" field, and an "scheduled end time" field. , an "importance" field and an "attention level" field.

The "task ID" field stores an ID that identifies a task registered as a schedule.

The "user ID" field stores the user ID of the user whose schedule the task corresponding to the task ID is registered.

The "date" field stores the date of the task corresponding to the task ID.

The "task name" field stores the name (content) of the task corresponding to the task ID. For example, task names include meeting, exercise, field work, driving, and the like.

The "scheduled start time" field stores the scheduled start time of the task corresponding to the task ID.

A task time corresponding to a task ID is stored in the "scheduled end time" field.

The "importance" field stores the importance of the task corresponding to the task ID. As the degree of importance, for example, categories indicating the degree of importance such as "high", "middle", and "low" are stored. For example, a variable dental check-up may be of "low" importance and an important work meeting may be of "high" importance.

The "attention level" field stores the level of attention required for the task corresponding to the task ID. As the attentiveness level, for example, a category indicating the degree of the required attentiveness level, such as "high", "middle", and "low", is stored. For example, the level of attention required for the task of watching a movie is "low" and the level of attention required for the task of traveling by driving is "high".
Note that the schedule database may include other information as information on the user's schedule.

The activity record data is a data group regarding various activities in the user's life. The activity record data is input by a user or sensing by a sensing device such as a wearable terminal. FIG. 5C is a diagram showing an example of an activity record database that stores activity record data.

The activity record database shown in FIG. 5C includes an "activity log ID" field, a "user ID" field, a "activity content" field, a "vital" field, a "number of steps" field, a "distance traveled" field, It includes a "calorie consumption" field, an "exercise intensity" field, and a "recording date and time" field. Note that the value of each field may store only input or sensed data.

The "activity log ID" field stores an ID that identifies an activity log.

The "user ID" field stores the user ID of the user who performed the activity log corresponding to the activity log ID.

The "activity content" field stores the content of the activity log corresponding to the activity log ID. Contents of the activity log include, for example, running, walking, exercising, eating, and sleeping.

The "vital" field stores information about vital signs in the activity log corresponding to the activity log ID. Specifically, it includes pulse, heart rate, respiration, blood pressure, body temperature, etc., and may be an average value or a value of changes over time.

The "number of steps" field stores the number of steps of the user in the activity log corresponding to the activity log ID.

The "distance moved" field stores the distance moved in the activity log corresponding to the activity log ID.

The "calorie consumption" field stores the estimated calorie consumption in the activity log corresponding to the activity log ID.

The “exercise intensity” field stores the exercise intensity in the activity log corresponding to the activity log ID. The exercise intensity may be a value set using the exercise intensity in a resting state as a unit intensity, such as the METs index.

The "recorded date and time" field stores the date and time when the activity log corresponding to the activity log ID was input or sensed.
Note that the activity record database may include other information as information on various activities in the user's life.

Meal record data is a data group related to meals of the user. The meal record data includes information input by the user and information output as the calculation result of the processing server 20 . FIG. 6A is a diagram showing an example of a meal record database that stores meal record data.

The meal record database shown in FIG. 6A includes a "meal log ID" field, a "user ID" field, a "date" field, a "recommended meal" field, a "taken meal" field, and a "determination" field. field and . It should be noted that the "meal taken" field stores only data properly entered by the user.

The “meal log” field stores a meal log ID that identifies the meal log.

The "user ID" field stores the user ID of the user who recorded the meal log corresponding to the meal log ID.

The "date" field stores the date when the meal log corresponding to the meal log ID was recorded.

In the "recommended meal" field, as processing of the processing server 20, information relating to a recommended mode of meal proposed to the user is stored. Specifically, the "recommended meal" field includes a "nutrition" subfield, a "food name" subfield, and a "recommended timing" subfield.

The “nutrition” subfield stores recommended dietary nutrients proposed to the user from the processing server 20 . Nutrients include their types and amounts.

The “food name” subfield stores the recommended food name proposed to the user from the processing server 20 . The food name includes the name and its quantity. The food name may be a menu name or a food product name such as vegetables or chicken. Also, the food name may be the name of a product distributed as food. The "nutrition" subfield and the "food name" subfield may be stored together, or only one of them may be stored.

The “recommended timing” subfield stores the recommended timing of ingestion proposed to the user from the processing server 20 . The timing of intake may be in the form of specifying a time such as "9:00 to 10:00" or in the form of not specifying a specific time period such as "breakfast". Also, the "recommended timing" may include information specifying the order of eating for each suggested nutrient or food name. In addition, the recommended timing may be set with a time period during which the intake is possible in consideration of the schedule information.

The “meal taken” field stores information about the meal taken by the user, which is input by the user. Specifically, the "meal taken" field includes a "nutrition" subfield, a "food name" subfield, a "recommended time" subfield, and an "image data" subfield.

The "Nutrients" subfield stores information about the nutrients in the meals ingested by the user. The "nutrition" subfield may be a value estimated from the name and amount of the menu or food item entered in the "food name" subfield described later.

The "food name" subfield stores the name of the food ingested by the user. The food name includes the name and its quantity. The food name may be a menu name or a food product name such as vegetables or chicken. Also, the food name may be the name of a product distributed as food.

The "nutrition" subfield and the "food name" subfield may be stored together, or only one of them may be stored. In the "nutrient" subfield and the "food name" subfield, for example, the user reads a two-dimensional code attached to a commercially available food product with the operation terminal 10, thereby making an inquiry about nutrients to the food database 30. may be entered with

The time at which the user ingested the meal is entered in the "time of intake" subfield. The time may be entered just before eating or just after eating. Instead of the intake time, information including the start and end of intake and the time required for intake (meal time) may be used.

The “image data” subfield stores image data of meals that the user has taken (or will take). For example, the user captures a meal using the camera 150 of the operation terminal 10 and uploads the captured meal image to the processing server 20 from application software related to the meal proposal system, thereby inputting the image of the meal. Image analysis is performed on the uploaded meal images, and processing such as identifying food names, nutrient names, and their amounts can also be performed. As a result, the input of the meal record from the user can be completed by uploading the meal image, and the convenience is improved.

The “judgment” field stores the judgment result of the user's eating behavior pattern. Specifically, the "judgment" field includes a "record" subfield and a "compliance" subfield.

The "record" subfield stores information indicating whether the user properly recorded the meal. Specifically, when the meal record was properly performed, "O" was attached, and when it was not properly performed, "X" was attached.

The “compliance” subfield stores information indicating whether or not the user has complied with the meal content suggested by the processing server 20 . Specifically, if the user has complied with the meal content proposed by the processing server 20, "O" is displayed. is marked with an "x".
Note that the meal record database may include other information as the information on the user's meal.

The calculated index data is a group of data relating to various indices calculated by processing by the processing server 20 . The calculation index data is information output as a calculation result of the processing server 20 . FIG. 6B is a diagram showing an example of a calculation index database that stores calculation index data.

The calculation index database shown in FIG. 6B includes a "calculation log ID" field, a "user ID" field, an "area value" field, a "first coefficient" field, a "slope" field, and a "second coefficient". field, an "average blood sugar level" field, a "third coefficient" field, an "evaluation index" field, and a "calculation date and time" field.

A calculation log ID for identifying a calculation log is stored in the "calculation log ID" field.

The “user ID” field stores the user ID of the user who is the subject of evaluation of the calculation log corresponding to the calculation log ID.

The “area value” field stores the area value related to the blood glucose level curve calculated by the processing server 20 . Details of the area value will be described later.

The “first coefficient” field stores the first coefficient (area coefficient) used for calculating the evaluation index in the processing server 20 . Details of the first coefficient will be described later.

The “slope” field stores the slope value for the blood glucose level curve calculated by the processing server 20 . Details of the inclination will be described later.

The “second coefficient” field stores the second coefficient (slope coefficient) used for calculating the evaluation index in the processing server 20 . Details of the first coefficient will be described later.

The "average blood sugar level" field stores the average blood sugar level measured by the user during the evaluation period.

The “third coefficient” field stores the third coefficient (average coefficient) used for calculating the evaluation index in the processing server 20 . Details of the average value coefficient will be described later.

The “evaluation index” field stores the evaluation index calculated by the processing server 20 . Details of the evaluation index will be described later.

The date and time when the calculation log corresponding to the calculation log ID was calculated is stored in the "calculation date and time" field.

The control unit 203 shown in FIG. 3 includes various modules such as a transmission/reception control module 2031, a blood sugar level acquisition module 2032, an activity recording module 2033, a meal recording module 2034, and a It functions as an index calculation module 2035, a meal suggestion module 2036, and an action evaluation module 2037.

The transmission/reception control module 2031 controls processing for the processing server 20 to transmit and receive signals from an external device according to a communication protocol.

The blood sugar level acquisition module 2032 acquires the glucose level measured by the measuring device by receiving it from the operation terminal 10 according to a preset sampling cycle. The blood glucose level acquisition module 2032 stores the acquired blood glucose level data of the user in the blood glucose level database.

The activity record module 2033 acquires activity records input by the user, activity records acquired from sensing devices such as wearable terminals, and information on the user's schedule. The activity log module 2033 stores the acquired activity log information in the activity log database. The activity record module 2033 stores the acquired schedule information in the schedule database.

The meal record module 2034 acquires the meal record input by the user and information on the mode of meals suggested to the user from the meal suggestion module 2036, which will be described later. The meal record module 2034 stores information regarding the acquired meal records and suggested dietary aspects in a meal record database.

The index calculation module 2035 calculates an evaluation index and various indices required for calculation of the evaluation index according to a preset calculation cycle. Here, the evaluation index E(s) is set by the following formula (1).
E(s)= _k1 *A+ _k2 *S+ _k3 * _GAve (1)
k ₁ : first coefficient (area coefficient)
A: Area between the blood glucose level curve and the reference line k ₂ : Second coefficient (slope coefficient)
S: slope of blood glucose level curve k ₃ : third coefficient (average value coefficient)
_GAve: average blood sugar level

The area A in Equation (1) is the area of the region surrounded by the blood glucose level curve and the reference line. Specifically, it is a value obtained by integrating the difference between the blood glucose level curve and the reference line over a predetermined evaluation period. Although the predetermined evaluation period can be set arbitrarily, the evaluation period may be, for example, a period from the time of calculation to 6 hours before.
Here, the reference line may be, for example, the value of the average blood sugar level, or may be a straight line at which the blood sugar level is 0. The reference line is not limited to a straight line, and may be defined by a curved line. The reference line can be set arbitrarily.

The slope S in Equation (1) is a value indicating the amount of change in the blood sugar level per unit time. The unit time may be 1 second, or may be a value obtained by differentiating the blood glucose level curve with respect to time by bringing it closer to zero. The slope can adopt the maximum value within a predetermined evaluation period. Although the predetermined evaluation period can be set arbitrarily, the evaluation period may be, for example, a period from the time of calculation to 6 hours before.

The average blood sugar level _GAve in formula (1) is obtained by moving average of the blood sugar level curve in a predetermined evaluation period. It should be noted that it may be obtained according to calculation of other average values that are limited to moving averages. Although the predetermined evaluation period can be set arbitrarily, the evaluation period may be, for example, a period from the time of calculation to 6 hours before.

Each coefficient in Equation (1) is a coefficient that weights each index.
A first factor _k1 weights the area A.
A second factor _k2 weights the slope S.
A third factor _k3 weights the average blood glucose level _GAve .

Each of the coefficients k ₁ to k ₃ constituting the evaluation index E(s) is weighted by referring to how the blood sugar level changes after a certain period of time from the timing of the user's meal intake. Specifically, the first coefficient _k1 is increased so that the change in the area A can be emphasized for a user whose change in the area A is significant, and the change in the slope S for a user whose change in slope is significant is reduced. The value of each coefficient is adjusted so that the second coefficient _k2 becomes large so that it can be emphasized.

The evaluation index E(s) is the weighted and linearly combined values of the area A, the slope S, and the average blood sugar level _GAve . The evaluation index is calculated at each set calculation time. Although it can be set arbitrarily at the time of calculation, for example, a standard meal time may be set and several hours before that time may be used as the initial value at the time of calculation.

The meal suggestion module 2036 compares the user's meal record and the evaluation index E(s) calculated by the index calculation module 2035 with preset proposal criteria, and proposes recommended meal patterns to the user. to decide. The proposal criteria are set based on the knowledge of the content of proposals made to users in the past. Suggestion criteria may be set using, for example, GI values. The GI value will be explained below.

The GI value is an index that indicates the degree of increase in blood sugar level after ingestion of a certain ingredient, and is an index that quantifies how the blood glucose level increases when a standard amount of food is ingested. Specifically, it is described by equation (2).
GI = increase in area of blood glucose level curve when ingesting "sample"/increase in area of blood glucose level curve when ingesting "glucose" x 100 (2)
The GI value of the entire dish is the sum of the GI values of all ingredients that make up the dish.

That is, by obtaining a user-specific GI value for each food from the user's blood sugar level curve and storing it as a meal record, it is possible to estimate the subsequent change in the postprandial blood sugar level curve. The meal suggestion module 2036 uses the changes in the blood glucose level curve thus estimated and refers to the evaluation criteria E(s) to determine the suggested meal aspects.

Behavioral evaluation module 2037 evaluates the user's eating behavior. Specifically, behavioral evaluation module 2037 determines whether the user is properly keeping a meal log and whether the user's diet adheres to suggested aspects. The behavior evaluation module 2037 refers to the meal log recorded in the meal record database, evaluates the user's eating behavior pattern, and reflects the results in the meal record database.

<Overview of this embodiment>
Next, an outline of this embodiment will be described. FIG. 7 is a diagram for explaining the outline of this embodiment.
As shown in FIG. 7, the meal suggestion system according to the present embodiment uses blood sugar level data to suggest meals to the user. A few hours before each meal, the user receives suggestions for recommended eating habits based on recommended blood glucose data.
The user selects a meal based on the suggestion, and inputs a record of the meal taken from the operation terminal 10 to the processing server 20 . The processing server 20 determines whether or not the user's eating habits (dietary habits) are appropriate based on the input record of meals, and feeds back the determination result to the user. The user improves his eating habits based on the determination result. This improves health.
Processing of such a meal suggestion system will be described in detail.

<Process flow>
Next, processing of the meal proposal system 1 will be described. FIG. 8 is a flow for explaining the processing of the meal proposal system 1. As shown in FIG. Prior to the processing of this figure, the user concludes a contract for using the meal proposal system 1 and receives a loan of a measuring instrument. A user registers user data, which are various types of personal information, when making a contract for use. When using the meal suggestion system 1, the user wears the provided measuring instrument on a part of the body such as the upper arm, for example, by inserting the probe under the skin.

As shown in FIG. 8, the operation terminal 10 inputs the blood sugar level data acquired from the measuring device by short-range wireless communication to the processing server 20 (step S110). Specifically, the operation terminal 10 repeatedly transmits the acquired blood sugar level data to the processing server 20 at predetermined intervals. This process is always repeated while the blood glucose level data is being measured by the measuring device.

After step S110, the processing server 20 acquires blood sugar level data (step S210). Specifically, the blood sugar level acquisition module 2032 of the processing server 20 stores the blood sugar level data in the storage unit 202 . Further, the blood sugar level data acquisition module creates a blood sugar level curve based on the acquired blood sugar level data, and stores the blood sugar level curve in the storage unit 202 . This updates the blood sugar database.

After step S210, the operation terminal 10 inputs the schedule information to the processing server 20 (step S111). Specifically, the operation terminal 10 extracts information about the schedule that the user has previously input into the application software for schedule management, and transmits the extracted information to the processing server 20 as schedule information. At this time, each field value that has not been clarified as schedule data may be supplemented by prompting the user to input again.

After step S111, the processing server 20 acquires schedule information (step S211). Specifically, the activity recording module 2033 of the processing server 20 receives the schedule information transmitted from the operation terminal 10 and stores it in the storage unit 202 . This updates the schedule database.

After step S211, the operation terminal 10 inputs the activity record to the processing server 20 (step S112). Specifically, the operating terminal transmits, for example, an activity log detected by a sensing device worn by the user to the processing server 20 . Further, the operation terminal 10 may transmit the activity log input by the user to the processing server 20 . The input of the activity record is performed each time the operation terminal 10 acquires the activity record.

After step S112, the processing server 20 acquires the activity record (step S212). Specifically, the activity record module 2033 of the processing server 20 acquires the activity log transmitted from the operation terminal 10 . The activity record module 2033 stores the acquired activity log in the storage unit 202 . This reflects the history database. Acquisition of the activity record is performed each time an activity record is transmitted from the operation terminal 10 .

After step S212, the processing server 20 calculates an evaluation index (step S213). Specifically, the index calculation module 2035 calculates the evaluation index according to Equation (1). The index calculation module 2035 causes the storage unit 202 to store the calculation result. This reflects the evaluation index database.

After step S213, the processing server 20 determines the mode of the proposed meal (step S214). Specifically, the meal proposal module 2036 compares the value of the evaluation index with the preset evaluation criteria, and determines the recommended diet for the user, name of nutrients or foods, Decide how much and when to take them. The meal suggestion module 2036 transmits the determined recommended meal aspect to the operation terminal 10 . In addition, the meal record module 2034 stores information on the determined meal pattern in the storage unit 202 . This updates the meal record database.

In addition, in the step of suggesting a mode of eating, a plurality of recipes of dishes that can be selected from the composition of recommended nutrients may be proposed as the contents of the meal. Information on recipes for such dishes can be obtained from the food database 30 .

Also, in the step of proposing a dietary pattern, the acquired schedule information is referred to, and a recommended composition of nutrients is proposed in consideration of the importance of scheduled tasks and the attention level required for the tasks. You may
In this case, high blood sugar is known to cause distraction, so the suggested meal should be included in the meal immediately prior to a task of high importance or a task requiring a high level of attention. Depending on the content, it is possible to reduce the amount of carbohydrates that contribute greatly to the increase in blood sugar level.

In addition, in the step of proposing a dietary pattern, the exercise intensity of the user may be estimated from at least one of the action history and healthcare data, and a recommended dietary pattern may be proposed. That is, processing may be performed such as suggesting a large amount of protein for meals on days when the exercise intensity is high. Also, if the activity record indicates a large amount of exercise, or if the schedule suggests that the amount of exercise will be large, a proposal may be made to encourage intake of carbohydrates so as to avoid hypoglycemia.

After step S214, the operation terminal 10 displays the mode of meal proposed by the processing server 20 to the user (step S113). Specifically, the operation terminal 10 displays to the user the content of meals, the time period for eating, and the order of eating each food item. child

After step S113, the operation terminal 10 inputs the meal record to the processing server 20 (step S114). Specifically, the operation terminal transmits to the processing server 20 the information on the ingested meal input by the user. At this time, image information of meals may be included. Input of the meal record is performed each time the user inputs the meal information.

After step S114, the processing server 20 acquires a meal record (step S215). Specifically, the meal record module 2034 of the processing server 20 acquires the meal record transmitted from the operation terminal 10 and stores it in the storage unit 202 . This updates the meal record database.

After step S215, the processing server 20 evaluates the eating behavior of the user (step S216). Specifically, the behavior evaluation module 2037 of the processing server 20 refers to the meal record database to determine whether the user has properly entered the meal record and whether the content of the meal is in compliance with the proposed mode. determine whether there are At this time, a certain allowable value range may be set in advance for requesting complete matching of meal contents. The behavior evaluation module 2037 causes the storage unit 202 to store the determination result. Good for this, the meal record database is updated. The behavior evaluation process is performed each time the user inputs meal information or after a certain period of time has passed since the recommended meal time slot.

After step S216, the processing server 20 outputs the evaluation result (step S217). Specifically, the action evaluation module 2037 transmits the determination result to the operation terminal. In addition, the behavior evaluation module 2037 gives benefits to users with excellent determination results. A benefit is valuable information such as points that can be used in other services. That is, the behavior evaluation module 2037 gives benefits to the user when the meal record is properly input, and gives the user give perks to. The content of these points can be set arbitrarily.

After step S217, the operation terminal 10 displays the evaluation result to the user (step S115). Specifically, the operation terminal 10 presents to the user the evaluation result of the eating behavior pattern transmitted from the processing server 20 . The presentation of the evaluation results may be performed at the end of the day or at the beginning of the day, for example, for the most recent day, or may be presented for each behavioral pattern determination.
Thus, the processing of the meal proposal system 1 ends. Note that a series of these processes are repeated, for example, for several months, half a year, preferably several years, and are used to contribute to changes in the user's eating habits, ie, eating habits.
Moreover, the order of each process in the meal suggestion system 1 may be changed as appropriate within a range that does not contradict.

<Screen example>
Next, screen examples of the operation terminal 10 will be described.
FIG. 9 is a diagram showing a screen example of the operation terminal 10. As shown in FIG. On this screen, detailed information can be displayed by clicking each item to be explained.
As shown in FIG. 9, the screen example displays a blood sugar level graph G showing a blood sugar level curve. In the blood sugar level graph G, the current time is clearly indicated by a vertical broken line, and the average blood sugar level is clearly indicated by a horizontal broken line.

In the screen example, the code B displays the content of meal suggestions. In the proposed meal content, the content of the meal that is recommended to be ingested for each meal time period is described.
Code C describes a meal record. In the meal record, "done" indicates that the entry has been completed, and "not" indicates that the entry has not been made. By displaying the details, each item of the meal record data can be displayed and edited.

Code D describes the schedule for the day. By displaying the details, each item of the schedule data can be displayed and edited.
Code E describes the activity record of the day. By displaying the details, each item of the activity record data can be displayed and edited.

<effect>
As described above, according to the meal suggestion system 1 of the present disclosure, the mode of meal recommended from the blood sugar level curve is suggested to the user. Therefore, it is possible for the user to easily grasp what should be ingested specifically.
In addition, since the meal proposal system 1 determines whether or not the user has actually taken the suggested meal, it is possible to evaluate the user's eating habits and make eating habits conducive to health.

In addition, in the meal proposal system 1, the area, slope, and average blood sugar level of the blood sugar level curve are used as parameters, and each coefficient is weighted for each user based on the fluctuation characteristics of the blood sugar level of the user. Appropriate meal suggestions can be made in consideration of characteristics.

In addition, in the meal proposal system 1, there are a wide variety of items to be proposed as aspects of meals, such as menus, food names, nutrients, recipes, names of food items on the market, time periods for ingestion, order of eating, and the like. , Suitable information can be provided to various users such as those who want to cook for themselves and those who do not have time to cook for themselves, and user convenience can be ensured.

In addition, since the meal proposal system 1 proposes meals in consideration of schedule information and activity records, it is possible to propose reasonable meals that match the lifestyle of the user.

Moreover, in the meal proposal system 1, benefits are given to the user who has properly entered the meal record and to the user who has complied with the content of the proposal, so it is easy to maintain the user's motivation to improve their eating habits.

<Second embodiment>
Next, the meal proposal system 1 according to the second embodiment will be described. FIG. 10 is a block diagram showing the functional configuration of the processing server 20 according to the second embodiment. In addition, in this figure, the same reference numerals are given to the same configurations as in the first embodiment, and the description thereof will be omitted.

As shown in FIG. 10, in the meal proposal system 1 according to the second embodiment, the storage unit 202 of the processing server 20B does not have evaluation index data, but has meal proposal models.
Also, the control unit 203 of the processing server 20 does not include the index calculation module 2035 .

The meal proposal model stored in the storage unit 202 is a trained model that estimates the mode of meal recommended for the user who is the person to be evaluated. The meal proposal model learns the user's meal record, the waveform data of the blood sugar level curve, and the mode of meal recommended for the user who showed the blood sugar level curve as teacher data.
The meal proposal model uses the user's meal record and the waveform data of the blood glucose level curve as input data, and uses the mode of meal recommended for the user as output data. FIG. 11 is a diagram showing a user's tendencies, recommended diet patterns, and a graph of the user's blood glucose level curve.

As shown in FIG. 11, it is empirically obtained that advice should be given to users who have various meal records and waveform blood glucose level curves as dietary aspects. By learning, it is possible to make a proposal based on past knowledge.

The meal proposal model according to this embodiment is, for example, a parameterized synthetic function obtained by synthesizing a plurality of functions. A parameterized composite function is defined by a combination of multiple tunable functions and parameters. The meal suggestion model according to the present embodiment may be any parameterized composite function that satisfies the above requirements, but is assumed to be a multi-layered network model (hereinafter referred to as a multi-layered network). A meal suggestion model using a multi-layered network has an input layer, an output layer, and at least one intermediate or hidden layer provided between the input layer and the output layer. The meal suggestion model is envisioned for use as a program module that is part of artificial intelligence software.

As the multi-layered network according to the present embodiment, for example, a deep neural network (DNN), which is a multi-layered neural network targeted for deep learning, can be used. As the DNN, for example, a convolution neural network (CNN) targeting images may be used.

Then, the meal proposal module 2036 inputs the acquired meal record and blood sugar curve data of the user to the meal proposal model, thereby providing the recommended meal for the user indicated by the correct output data from the meal proposal model. Get the aspect. Therefore, the meal proposal system 1 according to the second embodiment does not calculate the evaluation index.

As described above, in the meal proposal system according to the present embodiment, the content of proposals is determined by the meal proposal system. Appropriate proposal contents can be derived from the knowledge. As a result, it is possible to propose various and appropriate dietary patterns in line with individual conditions based on the content that has been proposed based on the experience and knowledge of registered dietitians.

<Other Modifications>

Other modified examples will be described.

In addition, the meal suggestion system 1 may perform a step of proposing sleep hours and sleep timings from the blood glucose level curve. Specifically, it is known that there is a certain correlation between fluctuations in blood sugar levels and sleep duration. Suggestions may be made as to the timing of sleep to balance the curves. As a result, it is possible not only to improve eating habits, but also to acquire sleep habits that contribute to health.

Moreover, the meal proposal system 1 may present menus, foods, recipes, how to eat, etc. that should not be eaten from the blood sugar level curve. For example, information may be presented to a person with high blood pressure to refrain from eating ramen noodles that are high in salt and contain a lot of fat and carbohydrates. As a result, a wide variety of proposals can be made.

In addition, the meal proposal system 1 performs image analysis on images of meals photographed and input before meals as meal records, and even if the order in which the user intends to eat the meals is specified again. good.

Alternatively, a questionnaire may be obtained from the user, and the contents of the proposal may be corrected in comparison with the results of the questionnaire. Specifically, based on the results of the questionnaire, each coefficient of the evaluation index may be reflected, or the meal proposal model may be re-learned.

In addition, the meal proposal system 1 may propose meal content by weighting coefficients of evaluation indices using meal records or by learning a meal proposal model, taking into account the content of meal records. In this case, for example, the following items can be taken into consideration when making a decision.
・Increase in blood sugar levels with or without breakfast ・Trends in how blood sugar levels rise depending on the type of carbohydrate (rice, wheat, etc.)
・Whether you are consuming an appropriate amount of protein (eating protein with carbohydrates makes it difficult for blood sugar levels to rise)
・Whether root vegetables raise blood sugar levels (if root vegetables are low in dietary fiber, they tend to raise blood sugar levels)
・ Blood sugar level when drinking a drink containing liquid sugar ・ Hearing when blood sugar level is rising at the timing of not eating → Possibility of not recording, or other factors (sleep, etc.) There is a possibility that it is rising due to stress) ・If there are two peaks of blood sugar spikes: If insulin is additionally secreted, and if there is a difference in absorption speed (e.g., if you are eating sugary foods) can be considered)
・Whether fruit consumption raises blood sugar levels please ~

Moreover, in the meal proposal system 1, each record of the user in charge of the supervisor terminal 10B of the supervisor who is a registered dietitian is shared, and advice can be received based on the supervisor's knowledge.

The embodiments of the present disclosure have been described above. It is intended to be included within the scope of the invention corresponding to the examples.

Embodiments of the present invention are additionally described below.

(Appendix 1)
computer processor,
continuously receiving blood glucose level data obtained from a measuring device attached to a part of a user's body;
receiving a meal record input from a user;
generating a blood glucose level curve that changes along a time series from the acquired blood glucose level data;
suggesting a recommended dietary pattern for the next meal estimated from the meal record and the blood glucose curve;
determining whether the user was able to comply with the suggested and recommended dietary pattern.

(Appendix 2)
In the step of suggesting a recommended dietary pattern,
Using the meal record, the area between the blood sugar level curve and the reference line, the slope of the blood sugar level curve, and the average blood sugar level, determine the recommended diet mode according to the evaluation criteria set for each user, ( The program described in appendix 1).

(Appendix 3)
In the step of suggesting a recommended dietary pattern,
Determine the recommended dietary mode from the evaluation index calculated from the following formula (1),
The program according to (Appendix 2), wherein each coefficient constituting the evaluation index is weighted with reference to a change in blood sugar level after a certain period of time from the timing of the user's meal intake.
E(s)= _k1 *A+ _k2 *S+ _k3 * _GAve (1)
k ₁ : area coefficient A: area between blood glucose level curve and reference line k ₂ : slope coefficient S: slope of blood glucose level curve k ₃ : average value coefficient _GAve: average blood sugar level

(Appendix 4)
In the step of suggesting a recommended dietary pattern,
Determining a recommended dietary pattern by using a trained model that has been trained to use the user's meal record and the waveform data of the blood glucose level curve as input data and the recommended dietary pattern for the user as output data. The program according to (Appendix 1).

(Appendix 5)
In the step of suggesting a recommended dietary pattern,
The program according to any one of (Appendix 1) to (Appendix 4), which proposes a menu or a food name as the content of the meal.

(Appendix 6)
In the step of suggesting a recommended dietary pattern,
The program according to any one of (Appendix 1) to (Appendix 5), which proposes a plurality of cooking recipes that can be selected from the recommended nutrient composition as the content of the meal.

(Appendix 7)
In the step of suggesting a recommended dietary pattern,
The program according to any one of (Appendix 1) to (Appendix 6), which proposes the types and amounts of commercially available foodstuffs as the content of the meal.

(Appendix 8)
In the step of suggesting a recommended dietary pattern,
The program according to any one of (Appendix 1) to (Appendix 7), which proposes a time slot for ingesting the next meal as the meal timing.

(Appendix 9)
In the step of suggesting a recommended dietary pattern,
The program according to any one of (Appendix 1) to (Appendix 8), which proposes the order of eating each food item as the meal timing.

(Appendix 10)
In the step of accepting input of information about meals,
The program according to any one of (Appendix 1) to (Appendix 9), which receives an input of an image of each meal time and meal content.

(Appendix 11)
to the processor,
cause execution of a step of acquiring schedule information indicating the user's schedule;
In the step of suggesting a recommended dietary pattern,
Refer to the acquired schedule information and propose a recommended nutrient composition considering the importance of the scheduled task and the attention level required for the task (Appendix 1) to (Appendix 10) Program as described in any.

(Appendix 12)
to the processor,
executing a step of accepting input of action history from the user or receiving healthcare data acquired by the wearable terminal worn by the user;
In the step of suggesting a recommended dietary pattern,
The program according to any one of (Appendix 1) to (Appendix 11), which estimates a user's exercise intensity from at least one of action history and health care data, and proposes a recommended mode of eating.

(Appendix 13)
to the processor,
12. The program according to any one of (Appendix 1) to (Appendix 12), causing a step of suggesting sleep time and sleep timing from a blood glucose level curve.

(Appendix 14)
to the processor,
In the step of accepting input of a meal record, determining whether the meal record has been properly input;
The program according to any one of (Appendix 1) to (Appendix 13), causing execution of a step of giving a privilege to the user when the meal record is properly input.

(Appendix 15)
to the processor,
The program according to any one of (Appendix 1) to (Appendix 14), causing the execution of a step of giving a privilege to the user when the user is able to comply with the suggested and recommended dietary pattern. .

(Appendix 16)
the computer's processor
continuously receiving blood glucose level data obtained from a measuring device attached to a part of a user's body;
receiving a meal record input from a user;
generating a blood glucose level curve that changes along a time series from the acquired blood glucose level data;
suggesting a recommended dietary pattern for the next meal estimated from the meal record and the blood glucose curve;
determining whether the user was able to comply with the recommended dietary pattern suggested.

(Appendix 17)
comprising a computer having a processor;
computer processor
a first module that continuously receives blood glucose level data obtained from a measuring device attached to a part of a user's body;
a second module for accepting input of a meal record from a user;
a third module that generates a blood sugar level curve that changes over time from the acquired blood sugar level data;
a fourth module that proposes a recommended dietary mode for the next meal estimated from the meal record and the blood glucose level curve;
and a fifth module for determining whether the user was able to comply with the suggested and recommended dietary pattern.

1 meal proposal system 10 user terminal 20 processing server 203 control unit 2031 transmission/reception control module 2032 blood sugar level acquisition module 2033 activity record module 2034 meal record module 2035 index calculation module 2036 meal proposal module 2037 behavior evaluation module

Claims (17)

computer processor,
continuously receiving blood glucose level data obtained from a measuring device attached to a part of a user's body;
receiving a meal log input from the user;
generating a blood sugar level curve that changes along a time series from the acquired blood sugar level data;
suggesting a recommended dietary pattern for the next meal estimated from the meal record and the blood glucose curve;
and determining whether the user was able to comply with the suggested dietary pattern recommended. In the step of proposing the recommended dietary pattern,
Using the meal record, the area between the blood sugar level curve and the reference line, the slope of the blood sugar level curve, and the average blood sugar level, the recommended dietary pattern is determined according to evaluation criteria set for each user. 2. The program of claim 1, which determines. In the step of proposing the recommended dietary pattern,
Determine the recommended dietary mode from the evaluation index calculated from the following formula (1),
3. The program according to claim 2, wherein each coefficient constituting said evaluation index is weighted with reference to a state of change in blood sugar level after a certain period of time from the timing of meal intake by the user.
E(s)= _k1 *A+ _k2 *S+ _k3 * _GAve (1)
k ₁ : area coefficient A: area between blood glucose level curve and reference line k ₂ : slope coefficient S: slope of blood glucose level curve k ₃ : average value coefficient _GAve: average blood sugar level In the step of proposing the recommended dietary pattern,
Using a user's meal record and waveform data of a blood glucose level curve as input data, and using a trained model that has been trained so that the recommended meal pattern for the user is output data, the recommended meal pattern is calculated. 2. The program of claim 1, which determines. In the step of proposing the recommended dietary pattern,
5. The program according to any one of claims 1 to 4, which proposes a menu or a food name as the contents of the meal. In the step of proposing the recommended dietary pattern,
6. The program according to any one of claims 1 to 5, which proposes a plurality of cooking recipes that can be selected from recommended nutrient composition as the content of meals. In the step of proposing the recommended dietary pattern,
7. The program according to any one of claims 1 to 6, which suggests the types and amounts of foodstuffs available on the market as the contents of the meal. In the step of proposing the recommended dietary pattern,
8. The program according to any one of claims 1 to 7, which proposes a time slot for ingesting the next meal as the meal timing. In the step of proposing the recommended dietary pattern,
9. The program according to any one of claims 1 to 8, which suggests the order of eating each food as the meal timing. In the step of accepting input of information on meals,
10. The program according to any one of claims 1 to 9, which receives an input of each meal time and an image of the content of the meal. to the processor;
causing a step of acquiring schedule information indicating the user's schedule;
In the step of proposing the recommended dietary pattern,
11. A recommended composition of nutrients is proposed by referring to the acquired schedule information and considering the importance of scheduled tasks and the attention level required for the tasks. The program described in Section. to the processor;
executing a step of accepting an input of an action history from the user or accepting healthcare data acquired by a wearable terminal worn by the user;
In the step of proposing the recommended dietary pattern,
12. The program according to any one of claims 1 to 11, wherein said user's exercise intensity is estimated from at least one of said action history and said healthcare data, and a recommended mode of eating is suggested. to the processor;
13. The program according to any one of claims 1 to 12, causing a step of suggesting sleep time and sleep timing from the blood glucose level curve. to the processor;
determining whether the meal record has been properly entered in the step of accepting input of the meal record;
14. The program according to any one of claims 1 to 13, causing execution of a step of providing a privilege to the user when the meal record is properly entered. to the processor;
15. The method according to any one of claims 1 to 14, causing execution of a step of giving a privilege to the user when the user is able to comply with the suggested recommended dietary pattern. program. the computer's processor
continuously receiving blood glucose level data obtained from a measuring device attached to a part of a user's body;
receiving a meal log input from the user;
generating a blood sugar level curve that changes along a time series from the acquired blood sugar level data;
suggesting a recommended dietary pattern for the next meal estimated from the meal record and the blood glucose curve;
and determining whether the user was able to comply with the suggested dietary pattern. comprising a computer having a processor;
computer processor
a first module that continuously receives blood glucose level data obtained from a measuring device attached to a part of a user's body;
a second module that receives a meal log input from the user;
a third module that generates a blood sugar level curve that changes along a time series from the acquired blood sugar level data;
a fourth module that proposes a recommended dietary pattern for the next meal estimated from the meal record and the blood glucose level curve;
and a fifth module for determining whether the user was able to comply with the suggested dietary pattern.

Images