Information provision device, information provision method, and program

Abstract

This information providing device is provided with: a physical information acquisition means that acquires physical information including a user's attribute, physical condition, and goal for said physical condition; a user information acquisition means that acquires information related to the user, which includes schedule information of the user; a determination means that determines, on the basis of the physical information and the schedule information, content of the advice for the goal and a timing for executing said content of the advice; and an output means that outputs the content of the advice and the timing.

Description

[Technical Field]

[0001] This disclosure relates to an information provision device, an information provision method, and a recording medium. [Background technology]

[0002] To encourage users to improve their lifestyle habits, there is technology that provides advice on daily behavior based on users' activity logs.

[0003] For example, Patent Document 1 discloses a health management server that generates appropriate advice messages based on the diet and health status of the person being managed. Patent Document 2 also discloses an information processing device that transmits advice to bring the user's physical condition closer to an ideal body specified by the user, based on the user's lifestyle and preference information. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] International Publication No. 2017 / 022013 [Patent Document 2] International Publication No. 2019 / 116679 [Overview of the project] [Problems that the invention aims to solve]

[0005] However, with the technologies described in Patent Documents 1 and 2, users may not be able to act according to the advice given, depending on their schedule. Therefore, it is necessary to provide advice that the user can actually implement.

[0006] One example of the purpose of this disclosure is to provide an information-providing device that can offer users actionable advice for improving their lifestyle habits. [Means for solving the problem]

[0007] An information providing device in one aspect of this disclosure includes: a physical information acquisition means for acquiring physical information including the user's attributes, physical condition, and goals for said physical condition; a user information acquisition means for acquiring information about the user, including the user's schedule information; a determination means for determining the content of advice toward the goal and the timing for implementing said advice based on the physical information and schedule information; and an output means for outputting the content of the advice and the timing.

[0008] An information provision method in one aspect of this disclosure involves a computer acquiring physical information including the user's attributes, physical condition, and goals for said physical condition; acquiring information about the user, including the user's schedule information; determining the content of advice toward the goals and the timing for implementing said advice based on the physical information and schedule information; and outputting the content of the advice and the timing.

[0009] A recording medium in one aspect of this disclosure stores a program that causes a computer to execute a process to acquire physical information including the user's attributes, physical condition, and goals for said physical condition; acquire information about the user, including the user's schedule information; determine the content of advice toward the goals and the timing for implementing said advice based on the physical information and schedule information; and output the content of the advice and the timing. [Effects of the Invention]

[0010] According to this disclosure, it is possible to provide users with advice on lifestyle improvements that they can implement. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 is a block diagram showing an example configuration of an information providing device in the first embodiment. [Figure 2] Figure 2 shows the hardware configuration in which the information provision device in the first embodiment is realized using a computer device and its peripheral devices. [Figure 3]FIG. 3 is a graph showing the frequency of the places where the user stayed in the first embodiment. [Figure 4] FIG. 4 is an example of a screen for the body information acquisition unit to acquire body information in the first embodiment. [Figure 5] FIG. 5 is an example of a screen for allowing the user to select the content of advice in the first embodiment. [Figure 6] FIG. 6 is an example of outputting the schedule information of the user in the first embodiment. [Figure 7] FIG. 7 is a diagram for explaining a method of determining advice in the first embodiment. [Figure 8] FIG. 8 is a flowchart showing the operation of the information providing apparatus in the first embodiment and a modification example of the first embodiment. [Figure 9] FIG. 9 is a diagram for explaining a method of determining the content of advice based on action information and lifestyle information in another modification example of the first embodiment. [Figure 10] FIG. 10 is a diagram for explaining a method of determining the content of advice based on action information and preference information in another modification example of the first embodiment. [Figure 11] FIG. 11 is a diagram for explaining a method of determining the content of advice based on environmental information in another modification example of the first embodiment. [Figure 12] FIG. 12 is a block diagram showing a configuration example of the information providing apparatus in the second embodiment. [Figure 13] FIG. 13 is a block diagram showing a configuration example of the information providing apparatus in the third embodiment. [Figure 14] FIG. 14 is a flowchart showing the operation of the information providing apparatus in the third embodiment.

MODE FOR CARRYING OUT THE INVENTION

[0012] Embodiments of the information provision device, information provision method, and non-temporary recording medium for storing the program described herein will be explained in detail below with reference to the drawings. These embodiments are not intended to limit the disclosed technology.

[0013] Figure 1 is a block diagram showing an example configuration of an information provision system in the first embodiment. Referring to Figure 1, the information provision device 100 includes a physical information acquisition unit 101, a user information acquisition unit 102, a determination unit 103, and an output unit 104.

[0014] The information provider 100 outputs advice to the user regarding behavioral changes aimed at improving lifestyle habits. The information provider 100 has, for example, the user's attributes, physical condition, and physical information including goals for that physical condition registered in an application program for health management, and outputs advice to the user on the same application program. In this embodiment, the advice refers to actions that the user will take to improve indicators of their health condition, such as their weight and height, waist circumference, blood pressure, blood glucose level, and blood lipids. Actions include exercise and diet. In this way, the information provider 100 promotes, for example, the user taking care of their own healthcare (self-care).

[0015] Figure 2 shows an example of a hardware configuration in which the information providing device 100 in the first embodiment of this disclosure is implemented using a computer device 500 including a processor. As shown in Figure 2, the information providing device 100 includes a CPU (Central Processing Unit) 501, memory such as ROM (Read Only Memory) 502 and RAM (Random Access Memory) 503, a storage device 505 such as a hard disk for storing a program 504, a communication interface 508 for network connection, and an input / output interface 509 for input and output of data. In the first embodiment, the information providing device 100 is connected to each component via a bus 510. Furthermore, the information providing device 100 in the first embodiment shown in Figure 1 can also be configured using cloud computing or the like.

[0016] The CPU 501 controls the entire information providing device 100 according to the first embodiment of the present invention by running the operating system. The CPU 501 also reads programs and data into memory from a recording medium 506 mounted on, for example, a drive device 507. Furthermore, the CPU 501 functions as part of the body information acquisition unit 101, user information acquisition unit 102, determination unit 103, and output unit 104 in the first embodiment, and executes processes or instructions in the flowchart shown in Figure 8, which will be described later, based on the program.

[0017] The recording medium 506 is, for example, an optical disc, a flexible disc, a magneto-optical disc, an external hard disk, or a semiconductor memory. The semiconductor memory, which is part of the recording medium, is a non-volatile memory device, and the program is recorded therein. The program may also be downloaded from an external computer (not shown) connected to a communication network.

[0018] As described above, the first embodiment shown in Figure 1 is realized by the computer hardware shown in Figure 2. However, the means for realizing each part of the information providing device 100 in Figure 1 are not limited to the configuration described above. Furthermore, the information providing device 100 may be realized by a single physically coupled device, or by a system consisting of two or more physically separated devices connected by wired or wireless connections.

[0019] The physical information acquisition unit 101 is a means for acquiring physical information including the user's attributes, physical condition, and goals for that physical condition. User attributes include gender and age. Physical condition refers to weight and the most recent measured values ​​of health indicators that indicate health status, such as height, waist circumference, blood pressure, blood glucose level, and blood lipids. Goals are ideal values ​​for the above indicators, and also include the deadline for achieving the goal state. For example, for weight, a goal might be to lose 0.5 kg in one month. In this embodiment, the goal may be a final goal (for example, to lose 5 kg in six months) or a series of short-term goals leading up to the final goal (for example, goals on a weekly basis).

[0020] The physical information acquisition unit 101 acquires, for example, attributes, physical condition, and goals for said physical condition, which are input into an application program. Furthermore, regarding attributes, the physical information acquisition unit 101 may acquire them from the user's terminal if they are registered on the terminal. If the measuring instruments for each health indicator are connected to the network, the physical information acquisition unit 101 may acquire information indicating the physical condition from each measuring instrument via the communication interface 508. Also, if the information management server of a medical institution that holds information indicating physical condition is connected to the network, the physical information acquisition unit 101 may acquire information indicating the physical condition from the information management server via the communication interface 508.

[0021] The physical information acquisition unit 101 may input attributes and physical condition for a goal and acquire a goal for achieving the ideal situation based on a learning model learned by machine learning. This learning model is machine-learned using data from pairs of information consisting of user attributes and the user's physical condition a predetermined X months ago (for example, 3, 6, or 12 months ago) and the user's current physical information for multiple users. It represents a predictive model that predicts the user's physical information X months later when user attributes and the user's current physical condition are input.

[0022] The user information acquisition unit 102 is a means of acquiring information about the user, including the user's schedule information. Schedule information includes the user's plans for work / school, exercise, meals, and time spent at home, particularly those that influence the content of advice for lifestyle improvement. The user information acquisition unit 102 acquires, for example, schedule information stored on the user's terminal.

[0023] Furthermore, the user information acquisition unit 102 may acquire the user's life log information and estimate the user's schedule information based on said life log information. In this embodiment, life log information refers to information about activities such as work, meals, exercise, and sleep, and includes the content of each activity, location information, duration, travel time, and travel route.

[0024] The user information acquisition unit 102 may acquire location information of the user based on life log information in order to estimate the user's schedule information. Location information refers to information about places where the user spends time in their daily life, such as work / school, exercise, meals, and home. In this embodiment, staying means, for example, remaining in the same area (for example, less than 200m) for a predetermined time (for example, 20 minutes or more). The user information acquisition unit 102 identifies location information such as longitude and latitude from the GPS location information of the user's terminal and identifies the name of the place from its correspondence with map information. The user information acquisition unit 102 identifies the place where the user is staying from the pre-set types of location information (work / school, exercise, meals, home). If there is no place that matches the pre-set types of location information, the user information acquisition unit 102 may ask the user about the place where they are staying and have them input it. The user information acquisition unit 102 may also add the input information to the types of place. Furthermore, the user information acquisition unit 102 may acquire information about the user's movements, such as walking, based on information obtained from the acceleration sensor of the user's terminal.

[0025] The user information acquisition unit 102 may also acquire the frequency of the locations where the user has been staying. Figure 3 is a graph showing the frequency of locations where the user has been staying. In the example in Figure 3, the frequency of locations where the user was staying during each time period is shown for each day of the week. The user information acquisition unit 102, for example, infers the location information of the user for each time period on each day of the week based on the information of the user's staying locations and the frequency of staying.

[0026] The decision unit 103 is a means for determining the content of advice toward the goal and the timing of carrying out the advice, based on physical information and schedule information. Specifically, first, the decision unit 103 calculates the calories that the user should reduce in order to approach the target physical state, based on physical information including the target for the user's physical state. Next, the decision unit 103 extracts timings such as days and times when the user can take action to reduce calories, based on the user's schedule information. Then, the decision unit 103 decides to advise the user to take the above action at the extracted timing.

[0027] The advice determined by the decision unit 103 may simply be to increase exercise or reduce calorie intake, or it may provide specific advice on particular exercises or dietary content. The decision unit 103 may also accept user responses in order to determine the content and timing of the advice. For example, it may accept a response indicating whether to increase exercise or reduce calorie intake. Furthermore, if the user chooses to reduce calories through exercise, the decision unit 103 may accept the name of the exercise the user will perform.

[0028] Here, we will use a diagram to explain how to determine the content of the advice provided to the user and the timing of its implementation. Figure 4 is an example of a screen for the physical information acquisition unit 101 to acquire physical information. In the example in Figure 4, the target for the user's physical condition is to lose 1.0 kg in 4 weeks. As shown in Figure 4, the determination unit 103 may calculate information regarding the target calories to be consumed and basal metabolic rate, and the output unit 104 may output this information. In the example in Figure 4, for the user to lose 1.0 kg, they need to reduce their calorie intake by 7000 / 4kcal = 1750kcal per week. That is, they need to reduce their calorie intake by 1750 / 7 = 250kcal per day. Therefore, the determination unit 103 will, for example, advise three exercises to reduce calorie intake by 100kcal each day.

[0029] Figure 5 shows an example of a screen that allows the user to select the content of the advice. In the example in Figure 5, for advice on exercise, on weekdays (Monday to Friday), the user is shown the option to choose from walking, climbing stairs, stretching / strength training, bathroom cleaning, or vacuuming. On weekends (Saturday and Sunday), in addition to the weekday options, the user is also shown the option to choose from sports such as soccer, golf, and tennis. Furthermore, in the example screen in Figure 5, for each exercise, the user is asked to check a box indicating the location where the exercise can be performed (type of location information) from among work / school, exercise, meals, and home.

[0030] In the example screen shown in Figure 5, when the user inputs the name of the exercise and the location where the exercise is planned to be performed, the user information acquisition unit 102 acquires the user's schedule information. The user information acquisition unit 102 may also acquire schedule information inferred based on the user's resting locations and resting frequency information. Figure 6 shows an example of the output of the user's schedule information in this embodiment. Next, the decision unit 103 identifies the time period (timing) when the user can perform the exercise, based on the exercise selected by the user on the screen in Figure 5, the location where the exercise is planned to be performed, and the types of location information (work / school, exercise, meal, home) extracted from the schedule information. Next, the decision unit 103 extracts the days of the week from the user's schedule information that do not contain any types of location information. If there is one or more extracted days of the week, the exercise for the extracted days is distributed to the days other than those days. Specifically, in the example in Figure 6, the type of location information is not included on Friday. In this case, the decision unit 103 decides not to give advice on three exercises on Friday, but to give advice on three exercises for Saturday and three exercises for Friday on Saturday.

[0031] The decision unit 103, for example, uses the acquired user schedule information and the information on the exercise selected by the user in Figure 5 and the planned location where that exercise can be performed (types of location information: work / school, exercise, meal, home) to determine the name of the exercise to advise on and the timing of when to give the advice on which day of the week and during which scheduled time slot. Let's also assume that the user has a plan to stay at home for three consecutive hours during the daytime. In this case, if "home" is selected as the planned location where the exercise can be performed in the example screen in Figure 5, the decision unit 103 may decide to advise on the content of exercises that can be performed at home during the time the user will be staying at home.

[0032] The output unit 104 is a means for outputting the content of the advice and the timing for executing the advice. The output unit 104 outputs the determined content of the advice and the timing for execution on, for example, an application program. The output unit 104 may also send a message to the user based on the information determined by the decision unit 103, which day of the week, at what scheduled time, and which exercise name to advise. Specifically, if the decision unit 103 has determined that the exercise advice for Wednesday in Figure 6 is "30 minutes of walking" and that the advice should be given during the "work / school" time slot, the output unit 104 will send the user the message, "30 minutes of walking is recommended," at that time. The output unit 104 may also function as an output control means that outputs to a display device or voice output device, which is a user terminal owned by the user. For example, the output unit 104 causes the content of the advice and the timing for execution to be displayed on the display device. In this case, the output unit 104 generates a display signal and supplies the generated display signal to the display device. The output unit 104 may perform sound output control in the voice output device so as to output voice guidance or the like to notify the user.

[0033] In the first embodiment described above, the information providing device 100 has a decision unit 103 that determines the content of advice toward the goal and the timing for implementing the advice based on physical information and schedule information, and an output unit 104 that outputs the content of the advice and the timing for implementation. In this case, for example, if the user has a full schedule all day and does not have time to implement actions toward improving their lifestyle, the device can prompt them to implement the advice for that day on another day. This allows the device to provide lifestyle improvement advice that the user can implement. The user can, for example, refer to the advice and implement actions toward improving their lifestyle. In other words, the information providing device 100 can support the user's decision-making by providing advice toward improving their lifestyle.

[0034] [Modified example of the first embodiment] Next, modifications of the first embodiment of this disclosure will be described in detail with reference to the drawings. To the extent that the description of this embodiment remains clear, any information that overlaps with the above description will be omitted.

[0035] In the first embodiment described above, the decision unit 103 determined the content of the advice toward the goal and the timing for implementing the advice, and the output unit 104 output this content. In contrast, in this modified example, the user information acquisition unit 102 further acquires information on the user's current actions. Then, when the decision unit 103 detects an action for which advice can be given, it determines the content of the advice using the detected action. In this modified example, the advice determined by the decision unit 103 will be advice regarding specific exercises or meals that can be performed within the user's current actions.

[0036] The user information acquisition unit 102 acquires the user's current activity information, for example, based on the user's terminal location using GPS (Global Positioning System), a combination of Wi-Fi and GPS, or Bluetooth Low Energy (registered trademark). The user information acquisition unit 102 may also acquire schedule information for the current time from the schedule information stored on the user's terminal. However, the activity information acquired by the user information acquisition unit 102 is not limited to these pieces of information, as long as it can capture the user's current activities. For example, when the user is moving, the user information acquisition unit 102 acquires the user's current activity information and outputs it to the decision unit 103.

[0037] When the decision unit 103 detects an action for which advice can be given, it determines the content of the advice based on the detected action. For example, if the decision unit 103 detects that the user is about to board an elevator based on the user's location information, it decides to advise the user to use the stairs. Also, if the decision unit 103 detects that the user's current schedule information is soccer and that the user has moved to a soccer field, it decides to advise the user to play soccer for a predetermined amount of time. Furthermore, if the decision unit 103 detects that the user has moved to a convenience store, it decides to advise the user to purchase food with a low calorie intake. The output unit 104 also outputs the content of the determined advice to the user as it occurs, using application programs or messages.

[0038] Figure 7 illustrates the method by which the decision unit 103 determines advice. As shown in Figure 7, keywords related to location, position, and acceleration sensor information are pre-associated with each exercise and dietary advice. The decision unit 103 determines whether the action corresponds to the keyword based on the location, position, and acceleration sensor information obtained from the user's terminal. If the action corresponds to the keyword, the decision unit 103 determines the content of the associated advice. This allows the decision unit 103 to provide advice in a timely manner without contacting the user.

[0039] Figure 8 is a flowchart outlining the operation of the information providing device 100 in the first embodiment and a modified version thereof. Note that the processing shown in this flowchart may be executed based on the program control by the processor described above. In this flowchart, the processing in S101 to S104 corresponds to a part of the first embodiment, and the processing in S105 to S108 corresponds to a part of a modified version thereof.

[0040] As shown in Figure 8, the physical information acquisition unit 101 acquires physical information including the user's attributes, physical condition, and goals for that physical condition (step S101). Next, the user information acquisition unit 102 acquires information about the user, including scheduled information (step S102). Then, the decision unit 103 determines the content of the advice toward the goal and the timing for implementing the advice based on the physical information and scheduled information (step S103). The output unit 104 outputs the determined advice and the timing for implementation (step S104). Note that the processing in S101 to S104 is performed, for example, at the timing for outputting short-term goals (for example, on a weekly basis).

[0041] Next, the user information acquisition unit 102 acquires information about the user's current actions (step S105). If the decision unit 103 detects an action by the user that can be advised (step S106; YES), it determines the content of the advice using the detected action (step S107). Next, the output unit 104 outputs the content of the determined advice (step S108). The processing in S105 to S108 is executed each time the decision unit 103 detects an action by the user that can be advised.

[0042] On the other hand, if the decision unit 103 does not detect any advisable user actions within a predetermined period (for example, until the deadline of the short-term goal) (step S106; NO), it terminates the process. With this, the information providing device 100 terminates the information provision process.

[0043] In the modified version of the first embodiment described above, the information providing device 100, when the decision unit 103 detects an action by the user that can be advised on, determines the content of the advice using the detected action. Then, the output unit 104 outputs the content of the determined advice. In this case, for example, the user can take timely actions toward improving their life in their daily life.

[0044] [Modified example 2 of the first embodiment] Next, other modifications of the first embodiment of this disclosure will be described. To the extent that the description of this embodiment does not become unclear, explanations that overlap with the above description will be omitted. In this modification, the decision unit 103 uses, in addition to the user's schedule information, the user's current activity information, as well as either life log information or environmental information, to determine the content of the advice to the user.

[0045] The user information acquisition unit 102 acquires the user's schedule information, current activity information, and life log information. The method for acquiring each piece of information is the same as in the first embodiment or Modification 1 of the first embodiment. In this modification, for example, the decision unit 103 determines the content of the advice based on the activity information, life log information, and the user's lifestyle habits and preferences obtained by analyzing the life log information.

[0046] Lifestyle information refers to information about daily meals and exercise, and is obtained by analyzing the user's behavioral history, such as travel history, travel time, and commuting route, or information about meals, such as food and beverage purchase history, visits to restaurants, what was ordered at restaurants, and images taken while eating. The user information acquisition unit 102 acquires information about exercise, for example, information about daily exercise content, exercise volume, or calories burned, calculated based on the user's behavioral history. The user information acquisition unit 102 also acquires information about meals, for example, information about the meals consumed on a daily basis and calories consumed. The user information acquisition unit 102 may also estimate lifestyle information from life log information entered into the application program.

[0047] Here, we will specifically explain an example of estimating lifestyle information based on life log information entered into an application program. As examples of lifestyle information, we will explain using (1) favorite places and exercise names and (2) favorite restaurants and menu names. (1) Regarding favorite places and exercise names, for example, the user is asked to enter the place and name of the exercise they performed. Based on the results, the combinations of place and exercise names are ranked in descending order of the total number of times they have done so over a two-week period, and the top three combinations of place and exercise names are designated as favorite places and exercise names. (2) Similarly, regarding favorite restaurants (eating establishments), for example, when a user eats out at a restaurant, the user is asked to enter the name of the restaurant and the name of the menu item they ordered. Based on the results, the combinations of restaurant and menu names are ranked in descending order of the total number of times they have done so over a two-week period, and for example, the top three combinations of restaurant and menu name are designated as favorite restaurants and menu names.

[0048] Preference information refers to information about a user's preferences for luxury goods such as tea, coffee, or sweets, or information about a user's hobbies, such as their favorite sports. Preference information can be obtained, for example, by analyzing the camera installed in the user's terminal or their purchase history. Alternatively, the user information acquisition unit 102 may estimate preference information from life log information entered into the application program. In this case, for example, the user is asked to directly input the contents of the food they ate into the application program. The results are then compiled to create a ranking, for example, based on the total number of times consumed over a two-week period, in descending order. For example, the top three items may be estimated as favorite foods.

[0049] Figure 9 is a diagram illustrating how the content of advice is determined based on behavioral information and lifestyle information in this modified example. As shown in Figure 9, lifestyle information, detected behavioral information (location), recommendations, and the content of the advice are linked and stored in the storage device 505. In the example in Figure 9, lifestyle information indicates that the user has a habit of eating melon bread when at a convenience store and a habit of playing soccer when at a park. The decision unit 103 may also decide to provide advice linked to specific behavioral information once it detects a particular behavior. In the example in Figure 9, if the decision unit 103 detects that the user is at a convenience store, it decides to advise the user to switch from melon bread to anpan (sweet bean paste bun), which has fewer calories, and the output unit 104 sends a message saying, "You can reduce your calorie intake by 100 kcal by switching to anpan." Also, if the decision unit 103 detects that the user is at a park, it decides to advise the user to play soccer, and the output unit 104 sends a message saying, for example, "You can burn 600 kcal in 60 minutes of soccer."

[0050] Figure 10 is a diagram illustrating how the content of the advice is determined based on behavioral information and preference information in this modified example. As shown in Figure 10, preference information and the method for determining the advice are linked and stored in the memory device 505. In the example in Figure 10, it is shown that the user prefers anpan (sweet bean paste bun) when it comes to Japanese sweets. When the decision unit 103 records that the user who likes anpan has eaten anpan in the application program, it advises the user to switch from anpan to dango (rice dumplings), which have fewer calories. The output unit 104 sends a message such as, "You can reduce your calorie intake by 100 kcal by switching from anpan to dango."

[0051] As another example, if the decision unit 103 detects that the user has moved to a ramen shop, it may decide to advise the user to eat ramen with fewer calories. Also, suppose the calorie content of ramen increases in the order of salt → soy sauce → miso. In this case, the decision unit 103 may decide to advise, for example, that if the user is going to eat ramen, "Salt is recommended because the calorie content increases in the order of salt → soy sauce → miso." As yet another example, if the decision unit 103 advises a user to exercise on Saturday, for example, if the user's favorite activity is playing soccer, it may decide to advise the user to play soccer for a predetermined amount of time. Furthermore, if the decision unit 103 detects that a user who enjoys playing soccer in a park has moved to a park, it may decide to advise the user to play soccer in the park for a predetermined amount of time.

[0052] Furthermore, in this modified example, the user information acquisition unit 102 may further acquire environmental information about the user's current surroundings, and the decision unit 103 may determine the content of the advice based on the environmental information. Environmental information refers to, for example, the weather, temperature, and humidity around the user's location, and the user information acquisition unit 102 acquires environmental information based on weather forecast information, etc.

[0053] Figure 11 illustrates how the decision unit 103 determines the content of advice based on environmental information. As shown in Figure 11, the storage device 505 stores alternative advice associated with specific weather or temperature conditions, which can be used to modify pre-stored advice. In the example in Figure 11, if the temperature is hotter than a predetermined temperature (for example, 30 degrees Celsius or higher), the decision unit 103 would normally advise a user who frequently eats ramen at their favorite restaurant, or whose favorite food is ramen, "If you're going to eat ramen, salt is recommended because the calorie intake is highest in the order of salt → soy sauce → miso," but instead it would advise, "Cold ramen is recommended. Salt is recommended because the calorie intake is highest in the order of salt → soy sauce → miso." Also, if it's raining, the decision unit 103 will not advise a user who plays soccer or likes soccer on the same day of the week to play soccer. Instead, it will suggest indoor sports or indoor exercises. In this case, it can provide advice that is more actionable for the user.

[0054] [Second Embodiment] Next, a second embodiment of this disclosure will be described in detail with reference to the drawings. To the extent that this description of the embodiment does not become unclear, any information that overlaps with the previous description will be omitted.

[0055] Figure 12 is a block diagram showing an example configuration of the information providing device 110 in the second embodiment. The information providing device 110, like the computer device shown in Figure 2, can implement its functions not only in hardware, but also in a computer device or software based on program control.

[0056] As shown in Figure 12, the information providing device 110 includes a physical information acquisition unit 111, a user information acquisition unit 112, a determination unit 113, a decision unit 114, and an output unit 115. The information providing device 110 differs from the first embodiment in that it includes at least a determination unit 113. The configuration and functions of the physical information acquisition unit 111 and the user information acquisition unit 112 are the same as those of the information providing device 100.

[0057] In this embodiment, the determination unit 113 is a means for determining the optimal behavioral pattern toward the user's goal, based on life log information and using a learning model obtained by machine learning. This model is obtained using information including the user's life log information and behavioral patterns as learning data. The determination unit 113 selects the optimal behavioral pattern of the user from lifestyle information, which is calculated based on the life log information, including daily calorie intake from meals (IN) and calories burned from basal metabolism and exercise (OUT). For example, to reduce calories by approximately 1000 kcal in a week, the value obtained by subtracting OUT from IN (IN-OUT) should be less than -143 kcal (1000 / 7 kcal) per day. To achieve this, one could consider (1) reducing IN, (2) increasing OUT, or (3) reducing IN and increasing OUT.

[0058] The determination unit 113 automatically selects which of the following methods (1) to (3) is most likely to result in a negative IN-OUT value for the user, based on a learning model obtained through machine learning using the daily IN and OUT states of multiple users as training data. This learning model takes life log information, including the user's diet (calorie intake) and exercise (calorie expenditure) for a predetermined period prior to the determination, as input and outputs information on the user's optimal behavioral pattern from among (1) to (3).

[0059] Here, among the above-described actions (1) to (3), the means for determining the optimal action pattern using the learning model will be described. This learning model uses a different learning model for each time j to determine the action pattern to be executed at that time, which is the action pattern for achieving the user's ultimate goal (the goal for the user to reach the ideal situation). The time in this case may be an absolute time or a relative time. In the case of relative time, the time may be called a stage. Also, the time may refer to a point on the time axis or a predetermined period on the time axis. Hereinafter, j is a natural number. For example, time j = 1 indicates the first week, time j = 2 indicates the second week, time j = t indicates the t-th week, and time j = T (T is a natural number greater than t) may indicate the final time, that is, the final week when it can be determined whether the ultimate goal has been achieved or not.

[0060] For example, the j-th learning model D * j takes as input the state X observed for user h at time j. Here, the state includes the record of the user's weight at each time, the record of IN at each time, the record of OUT at each time, the information on the frequency of each meal name for each time, the information on the frequency of each exercise content for each time, etc., observed from time 1 to time j. And the j-th learning model D jh * j determines the action pattern A of user h at time j. The determined action pattern A jh jh is the action pattern that maximizes the total effect ((the value obtained by multiplying (IN - OUT) by minus) that user h can obtain from time j to the final time T). jh is the action pattern that maximizes the total effect ((the value obtained by multiplying (IN - OUT) by minus) that user h can obtain from time j to the final time T).

[0061] The determination of the action pattern by the determination unit 113 is performed forward as time j elapses. For example, if the current time j is t, the state X of user h observed at the current time t th is input to the t-th learning model D * t to obtain the action pattern A of user h at the current time t. thThe determination unit 113 obtains the state X of user h observed at time t+1. (t+1)h By inputting this, the action of user h at time t+1, A (t+1)h This is obtained. In this way, the determination unit 113 successively determines the action pattern to be taken as time progresses. Therefore, the action plan is created dynamically.

[0062] The decision unit 114 determines the content of the advice based on the user's optimal behavior pattern. In addition to the method for determining the content of the advice used by the decision unit 103, it also determines the content of the advice according to one of the behavior patterns (1) to (3). For example, if the user's optimal behavior pattern is (1), the decision unit 114 decides to mainly provide advice on diet. If the user's optimal behavior pattern is (2), the decision unit 114 decides to mainly provide advice on exercise. If the user's optimal behavior pattern is (3), the decision unit 114 decides to provide advice on both diet and exercise. The decision unit 114 may also decide to advise the user to perform the exercise selected by the user on the screen in Figure 5 for the exercise advice in (1) or (2). The output unit 115 outputs the diet and exercise advice determined by the decision unit 114.

[0063] Furthermore, the determination unit 113 may select the user's optimal behavioral pattern based on the calories burned (OUT) during exercise. To achieve this, the behavioral pattern may be defined as how much OUT the user should burn per day through exercise, for example, (A) 100kcal, (B) 200kcal, or (C) 300kcal. In this case, the determination unit 113 uses the user's daily OUT information and, based on a learning model obtained through machine learning, automatically selects which of (A) to (C) will help the user achieve their goal. The goal here is, for example, a weight loss of 2kg in one month, and the goal is to bring the weight loss as close to 2kg as possible (losing significantly more than 2kg will not achieve the goal). This learning model takes life log information, including the user's diet and exercise content for a predetermined period before the determination, as input and outputs information on the user's optimal behavioral pattern from (A) to (C). The decision unit 114 then decides to provide advice on diet and exercise based on the behavioral patterns (A) to (C).

[0064] In the second embodiment described above, the information providing device 110 has a decision unit 114 that determines the content of the advice based on the user's optimal behavior pattern. In this case, it is possible to provide the best advice for achieving the goal.

[0065] [Third Embodiment] Next, a third embodiment of this disclosure will be described in detail with reference to the drawings. To the extent that this description of the embodiment does not become unclear, any information that overlaps with the previous description will be omitted.

[0066] Figure 13 is a block diagram showing an example configuration of the information providing device 120 in the third embodiment. The information providing device 120, like the computer device shown in Figure 2, can implement its functions not only in hardware, but also in a computer device and software based on program control.

[0067] As shown in Figure 13, the information providing device 120 includes a physical information acquisition unit 121, a user information acquisition unit 122, a determination unit 123, an output unit 124, and a verification unit 125. The information providing device 120 differs from the information providing device 100 in that it includes at least a verification unit 125. In this embodiment, the process is the same as in the first embodiment and the first modified example of the first embodiment until the determination unit 123 outputs the determined advice content, and the configuration and functions of the physical information acquisition unit 121 and the user information acquisition unit 122 are also the same as in the information providing device 100.

[0068] The verification unit 125 is a means for verifying whether the user has implemented the advice that has been output. After the advice has been output by the output unit 124 and a predetermined period of time (several hours to several days) has elapsed, the verification unit 125 acquires information on the meal content or exercise content for the day or time period on which the user specified to implement the advice, for example, based on life log information. Then, if the verification unit 125 has information on the meal content or exercise content corresponding to the advice, it determines that the user has implemented the advice. On the other hand, if the verification unit 125 does not have information on the meal content or exercise content corresponding to the advice, it determines that the user has not implemented the advice.

[0069] The decision unit 123 decides to give different advice if the user does not follow the advice it has given. In other words, the decision unit 123 determines the content of the second piece of advice based on whether or not the first piece of advice already given to the user has been followed. The content of the first piece of advice and the second piece of advice are different. If the user does not follow the advice regarding diet, the decision unit 123 decides to advise the user to eat other foods. Similarly, if the user does not follow the advice regarding exercise, the decision unit 123 decides to advise the user to do other exercises. For example, if the decision unit 123 advises the user to change from a melon bun to a sweet bean paste bun to reduce calorie intake from meals, but the user does not follow the advice, the decision unit 123 decides to advise the user to try changing the melon bun to a dango (rice dumpling), which also has fewer calories.

[0070] The decision unit 123 may decide to advise the user on exercise if the user fails to follow the advice given regarding their diet. Conversely, the decision unit 123 may decide to advise the user on diet if the user fails to follow the advice given regarding their exercise. The output unit 124 outputs the content of the decided advice.

[0071] Figure 14 is a flowchart illustrating the overview of the operation of the information providing device 120 in the third embodiment. Note that the processing shown in this flowchart may be executed based on the program control by the processor described above. This flowchart is performed, for example, after the processing at S108 in Figure 8. Furthermore, the processing in this flowchart is performed, for example, at the timing of outputting short-term goals (for example, on a weekly basis).

[0072] As shown in Figure 14, first, the output unit 124 outputs the advice content determined by the decision unit 123 (step S121). Next, the verification unit 125 acquires the user's life log information after a predetermined period has elapsed since the advice was output (step S122). Next, the verification unit 125 verifies whether the user has acted on the content of the outputted advice (step S123). If the user has not acted on the advice (S123; NO), the verification unit 125 outputs this information to the decision unit 123, and the decision unit 123 decides to give advice different from the advice (step S124). Next, the output unit 124 outputs the content of the decided different advice (step S125). On the other hand, if the user has acted on the advice (S123; YES), the verification unit 125 terminates the process.

[0073] In the third embodiment described above, if the decision unit 123 of the information providing device 120 does not act on the advice it provided, it will provide advice different from the advice it provided. In this case, the likelihood of the user acting on the advice can be increased.

[0074] The present disclosure has been described above with reference to the embodiments described herein, but the present disclosure is not limited to the embodiments described above. The structure and details of each present disclosure may include embodiments that apply various modifications that can be grasped by those skilled in the art within the scope of the present disclosure. The present disclosure may include embodiments that combine or substitute the matters described herein as appropriate. For example, matters described using a particular embodiment may be applied to other embodiments to the extent that they do not cause a contradiction. For example, although multiple operations are described sequentially in the form of a flowchart, the order in which they are described does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations may be changed to the extent that it does not impair the content. [Explanation of Symbols]

[0075] 100, 110, 120 Information provision device 101, 111, 121 Physical information acquisition department 102, 112, 122 User Information Acquisition Unit 103, 114, 123 Decision Section Output sections 104, 115, 124 113 Judgment section 125 Verification Department 500 computer devices 501 CPU 502 ROM 503 RAM 504 Program 505 Storage device 506 Recording media 507 Drive unit 508 Communication Interface 509 Input / Output Interfaces 510 Bus

Claims

1. A means for acquiring physical information, which acquires physical information including the user's attributes, physical condition, and goals for said physical condition, User information acquisition means for acquiring information about the user, including the user's schedule information and life log information, A determination means that determines the optimal behavioral pattern toward the goal using a model obtained by machine learning based on the aforementioned life log information, A determination means that determines the content of advice toward the goal based on information indicating the user's optimal behavioral pattern, physical information, and scheduled information, and determines the timing of implementing the content of said advice based on the physical information and scheduled information, An information providing device comprising output means for outputting the content of the advice and the timing of the aforementioned advice.

2. The information providing device according to claim 1, wherein the user information acquisition means acquires the user's life log information and acquires the schedule information based on the life log information.

3. The user information acquisition means further acquires the user's current behavior information, The information providing device according to claim 1 or 2, wherein the determination means, when it detects an action for which advice is possible, determines the content of advice using the detected action.

4. The user information acquisition means acquires the user's life log information, The information providing device according to claim 3, wherein the determination means determines the content of the advice based on the behavioral information and the life log information.

5. The information providing device according to claim 4, wherein the determination means analyzes the user's lifestyle habits or preferences based on the life log information, and determines the content of the advice based on the behavioral information and the lifestyle habit information or preference information obtained as a result of the analysis.

6. The user information acquisition means further acquires environmental information regarding the current environment surrounding the user, The information providing device according to claim 1 or 2, wherein the determination means determines the content of the advice based on the environmental information.

7. The system further includes verification means for verifying whether the user has implemented the advice provided, The information providing device according to claim 1 or 2, wherein the decision means determines to give advice different from the advice given if the user does not act on the advice given.

8. A means for acquiring physical information, which acquires physical information including the user's attributes, physical condition, and goals for said physical condition, A user information acquisition means for acquiring information about the user, including the user's schedule information and the user's current activity information. A determination means that performs the following: a process to determine the content of advice toward the goal and the timing of implementing the content of said advice based on the physical information and the schedule information; and a process to determine the content of advice using the detected behavior when an action for which advice is possible is detected based on the user's current behavior information. An information providing device comprising output means for outputting the content of the advice and the timing of the aforementioned advice.

9. Computers The system acquires physical information including the user's attributes, physical condition, and goals for that physical condition. Information about the user, including the user's schedule information and life log information, is acquired. Based on the aforementioned life log information, the optimal behavioral pattern toward the aforementioned goal is determined using a model obtained through machine learning. The system determines the content of advice toward the goal based on information indicating the user's optimal behavioral patterns, the physical information, and the planned information, and determines the timing for implementing the content of said advice based on the physical information and the planned information. A method for providing information that outputs the content of the advice and the timing of the aforementioned advice.

10. The system acquires physical information including the user's attributes, physical condition, and goals for that physical condition. Information about the user, including the user's schedule information and life log information, is acquired. Based on the aforementioned life log information, the optimal behavioral pattern toward the aforementioned goal is determined using a model obtained through machine learning. The system determines the content of advice toward the goal based on information indicating the user's optimal behavioral patterns, the physical information, and the planned information, and determines the timing for implementing the content of said advice based on the physical information and the planned information. A program that causes a computer to execute a process that outputs the content of the advice and the timing of the output.

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