Message sending device

Abstract

The purpose of the present disclosure is to provide a message transmission device that transmits a message that is easy to open.　In a message transmission device 100 according to the present disclosure, a reception unit 101 receives smartphone log data of a user terminal 200. A message generation unit 105 performs a process for transmitting a transmission message that is based on the smartphone log data and the opening history of messages in the user terminal 200. The message transmission process includes a process for generating a message that is based on the smartphone log data and the opening history. The message generation unit 105, for example, selects a nudge that is based on the smartphone log data and the opening history, and generates and transmits a message that corresponds to the nudge.

Description

[Technical Field]

[0001] The present invention relates to a message sending device. [Background technology]

[0002] Patent Document 1 describes a message sending device that sends a message according to the psychological state or psychological bias of a user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-55712 Summary of the Invention [Problem to be solved by the invention]

[0004] However, even if an appropriate message is sent according to the user's psychological state, the user may not open the message, and the effect of sending an appropriate message may not be obtained.

[0005] Therefore, an object of the present invention is to provide a message sending device that sends messages that are easy to open. [Means for solving the problem]

[0006] The message sending device of the present invention includes an acquisition unit that acquires terminal log data of a user terminal, and a message sending unit that performs a sending process of a message based on the terminal log data and the message opening history of the user terminal. [Effects of the Invention]

[0007] According to the present invention, it is possible to send a message that is likely to be opened by a user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing a functional configuration of a message transmitting device 100 according to the present disclosure. [Figure 2] FIG. 10 is a diagram showing a specific example of personality factor scores. [Figure 3] 10 is a diagram showing the opening rate of users for each nudge type derived by the opening estimation unit 103. FIG. [Figure 4] FIG. 10 is a diagram showing the opening rate for each nudge type. [Figure 5] FIG. 5(a) is a diagram showing a specific example of the opening DB 104a, and FIG. 5(b) is a diagram showing the distribution status obtained from the opening DB 104a. [Figure 6] FIG. 10 is a diagram showing a specific example of a nudge message DB 103b relating to walking. [Figure 7] FIG. 10 is a schematic diagram showing the learning process of a personality factor score estimation model 102a. [Figure 8] FIG. 10 is a diagram showing a specific example of a personality factor score DB 102c. [Figure 9] FIG. 10 is a schematic diagram showing the learning process of the opening estimation model 103a. [Figure 10] 10 is a flowchart showing the operation of the message transmitting device 100. [Figure 11] 1 is a diagram illustrating an example of a hardware configuration of a message transmitting device 100 according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] The present disclosure will be described with reference to the accompanying drawings. Whenever possible, the same parts are designated by the same reference numerals and redundant description will be omitted.

[0010] 1 is a block diagram showing the functional configuration of a message sending device 100 according to the present disclosure. The message sending device 100 receives smartphone log data from a user terminal 200 and sends a message in response to the received data.

[0011] In the present disclosure, the user terminal 200 has, for example, a healthcare application (hereinafter, the application will be abbreviated as an app) and has a function of counting the number of steps taken by the user and notifying the user of the number of steps and a target number of steps. The user terminal 200 then periodically or at a predetermined timing transmits the target app and smartphone log data to the message sending device 100 as a request to send a message. The notification timing may be determined by taking into consideration the time, place, or people who are with the user at the time that the user is likely to respond.

[0012] The message sending device 100 sends a message relating to the number of steps or the target number of steps, etc., to the user terminal 200 according to the app and smartphone log data. The message sending device 100 generates and sends a message with content that is easy for the user to open, or sends the message at a timing when it is easy for the user to open it.

[0013] In this disclosure, the message sending device 100 is disclosed as transmitting healthcare-related messages such as a target number of steps, but is not limited thereto. It may be any device that transmits a message encouraging a user to take a predetermined action. For example, if the user terminal 200 has a shopping app, the message sending device 100 may send a message encouraging the user to make a purchase.

[0014] The following describes in detail the message sending device 100. The message sending device 100 includes a receiving unit 101, a personality factor score estimation unit 102, an opening estimation unit 103, a weight calculation unit 104, a message generation unit 105, a personality factor score estimation model 102a, an opening estimation model 103a, a nudge message DB 103b, and an opening DB 104a.

[0015] Receiving unit 101 is a part that receives, at a predetermined timing or periodically, the application type (or application ID) and smartphone log data that are the subject of the message from user terminal 200. The application type (or application ID) is information for identifying an application such as a healthcare application, and a healthcare application is an application that counts the number of steps taken by the user and notifies the user of the number of steps, etc., or notifies the user of a target number of steps, etc.

[0016] The smartphone log data includes user attribute information, application logs, location information, subscriber information, health care logs, and message opening information at a certain point in time. This smartphone log data is data for a fixed period in the immediate vicinity of the user terminal 200. This smartphone log data is stored in the opening DB 104a to be used as learning data, which will be described later.

[0017] The attribute information includes the user's gender, age, annual income, occupation, hobbies, etc. The application log is a usage log of applications registered in the user terminal 200. For each application and each category, descriptive statistics are shown for the usage time, usage interval, and number of uses. Applications include phone, email, SMS, messaging applications, and SNS.

[0018] The location information indicates the location obtained by the GPS or the like of the user terminal 200. It also includes descriptive statistics regarding the travel distance, travel route, and stay points. For the travel route and stay points, it may include at least one of the similarity, means of transportation, stay time, and at-home rate. The similarity indicates the degree of match compared with the user's past travel route and stay points.

[0019] The contractor information is information about the user who has contracted for the user terminal 200. For example, it includes the fee plan, model upgrade period, contract options, and the like.

[0020] The healthcare log is information indicating the user's health condition, such as BMI, current step count, target step count, average step count, etc. This information is obtained by the user terminal 200 or a wearable terminal linked to it.

[0021] Message opening information is information about the opening of messages sent to the user terminal 200, such as the opening rate, opening time, whether the message was opened previously, etc. The opening time is the time when the user opened the message (in YYYYMMDDhhmm format) and the time from when the message arrived until when the message was opened.

[0022] The personality factor score estimation unit 102 is a part that estimates the user's personality factor scores based on smartphone log data acquired in real time. The personality factor score estimation unit 102 inputs the smartphone log data into a personality factor score estimation model 102a and obtains the personality factor scores that are the output result. These personality factor scores are standardized numerical information that indicate the user's personality or psychological characteristics.

[0023] The personality factor score estimation model 102a is an estimation model trained by known machine learning using learning smartphone log data as explanatory variables and learning personality factor scores as objective variables. In the present disclosure, the personality factor scores include at least one of the BigFive, Health Locus of Control, and time discount rate, but may also include other factors, or may represent psychological characteristics of the user other than these factors.

[0024] Specific examples of these personality factor scores are shown in Figure 2. The Big Five is a theory that personality is composed of five factors. In this disclosure, the five factors are openness, conscientiousness, extroversion, agreeableness, and emotional instability. It is believed that the strength of these five factors affects the personality and behavior of users.

[0025] Health Locus of Control is a concept that classifies whether the cause of health-related evaluations is sought within oneself or others. The tendency to seek causes within oneself is classified as internal locus of control, while the tendency to seek causes within others or the external environment is classified as external locus of control.

[0026] The time discount rate is also called the time preference rate. The time discount rate is the rate at which the future value of a reward (delayed reward) is perceived as lower than the present value (immediate reward), and it also refers to that discount rate.

[0027] The opening estimation unit 103 is a part that acquires a predicted opening rate for each nudge message (nudge type) prepared in advance in the nudge message DB 103b based on the estimated personality factor score, attribute information, and delivery status. In the present disclosure, the opening estimation unit 103 inputs the personality factor score, attribute information, and delivery status into the opening estimation model 103a, and derives a predicted opening rate for each nudge type for the user. Note that the delivery status is not essential.

[0028] The opening estimation model 103a is prepared for each nudge type, and the opening estimation unit 103 inputs the personality factor scores and attribute information into each opening estimation model 103a, and derives a predicted opening rate from each.

[0029] FIG. 3 is a diagram showing the opening rate for each nudge type of user derived by the opening estimation unit 103. Although FIG. 3 shows the opening rate for each nudge type for multiple users, it is sufficient to show the opening rate for one target user. In FIG. 3, the opening rate for user A's nudge messages of the monetary gain type is 7%, and the opening rate for nudge messages of monetary loss is 48%, etc. It can be seen that sending nudge messages of monetary loss is effective for user A.

[0030] Here, we will explain the nudge type. Note that the default is a message that is not a nudge.

[0031] Time pressure is a nudge concept that encourages a user to take action by preventing the user from making rational decisions due to a sense of time pressure. In this disclosure, the time remaining until a goal is achieved is displayed to encourage the user to take a predetermined action.

[0032] Monetary gain and monetary loss are nudge concepts that encourage a user to take a certain action by presenting an economic gain or loss.

[0033] Social conformity is a concept of nudging a user to take a specific action, as people tend to conform to the behavior of those around them. In this disclosure, the status (here, the number of steps) of other users is displayed to encourage the user to walk.

[0034] Healthy gain is a nudge concept that encourages a user to take a certain action by presenting a healthy gain or loss.

[0035] Benefit is a nudge concept that encourages users to take a certain action by showing them the positive benefits of a product, action, or health.

[0036] Nudge messages for each nudge type will be described later.

[0037] The weight calculation unit 104 performs weighting processing by multiplying the predicted opening rate calculated by the opening estimation unit 103 by the target user's "message opening rate up to now" (hereinafter referred to as past opening rate). The weight calculation unit 104 refers to the opening DB 104a to calculate the opening rate for each nudge type, and acquires this as the past opening rate.

[0038] Figure 4 shows the open rate for each nudge type. As shown in the figure, the probability that a user will open a message is calculated by multiplying the predicted open rate by the past open rate. For example, in Figure 4, the past open rate for the default message is 7%, and by multiplying this by the predicted open rate of 7%, the probability that the final user will open the message can be calculated.

[0039] The weight calculation unit 104 performs weighting based on the past opening rate, but this is not limited to this. The weight calculation unit 104 may also apply a predetermined weighting coefficient to the predicted opening rate depending on the time, location, or people who are with the user. For example, since it is expected that users are short on time during the morning time period (a predetermined time period), users tend to open or not open depending on the nudge type, or regardless of that. Therefore, a high weighting coefficient is set for a time period when users are more likely to open a message, and conversely, a low weighting coefficient is set for a nudge type during a time period when users are less likely to open a message.

[0040] Here, the weighting coefficients have been described with a focus on time, but the weighting coefficients may be changed depending on the location and the people who are with the user. The location and the people who are with the user may be included in the smartphone log data transmitted from the user terminal 200. The user terminal 200 acquires location information using GPS or the like. Furthermore, the user terminal 200 can identify nearby users (other user terminals) by using short-range wireless communication or the like.

[0041] Alternatively, the opening rate may be calculated from the opening DB 104a according to the time period, location, who was with whom, the personality factor scores of the people with whom, etc., and converted into a weighting factor and multiplied by the predicted opening rate.

[0042] Here, the acquisition of the past open rate will be described. Weight calculation unit 104 accesses open DB 104a to check whether or not a user has opened a message. Open DB 104a stores, for each user, an open history that associates the message nudge type with whether or not the message has been opened.

[0043] 5(a) is a diagram showing a specific example of the opening DB 104a. As shown in the figure, the opening DB 104a stores, for each user, history information such as a message ID, received date and time, opened date and time, received location, opened location, whether or not there were people present when the message was received, whether or not there were people present when the message was opened, nudge type, and whether or not the message was opened, in association with each other. Furthermore, if necessary, personality factor scores of people present may also be stored in association with each other. This information can be tallied for each user to determine the opening rate for each nudge type.

[0044] This opening DB 104a is configured by receiving from the user terminal 200 the opening result, the opening date and time, the opening location, information on whether or not anyone was present when the message was opened, and the like, each time a nudge message is sent. The nudge type is stored when the message sending device 100 sends the message. Furthermore, the information on whether or not anyone was present is determined by an opening DB management device (not shown) that determines whether or not other terminals were present near the user terminal 200 based on the location of the user terminal 200 and the time and registers the determination in the opening DB 104a.

[0045] At that time, the opening DB management device can obtain which user terminal 200 (who) was present and the personality factor scores of the users from the personality factor score DB 102c, and reflect these in the opening DB 104a.

[0046] The weight calculation unit 104 can refer to the opening DB 104a to determine the opening rate for each user and each nudge type. Note that the individual opening rate is the rate at which messages are opened within a predetermined time after receipt, but is not limited to this. Location or whether the user was with someone may also be taken into consideration. That is, in addition to or instead of time or nudge type, the opening rate at a certain location may be determined, or the opening rate when the user was with someone (or not). The opening rate may be determined by appropriately combining the nudge type, time, location, or whether the user was with someone, etc.

[0047] The weight calculation unit 104 may determine the user's status (location, whether they are with someone) based on the user's location registration information (location registration DB, etc.) by a server that manages the user's location information, and then determine what weight to multiply and perform the weighting process.

[0048] Furthermore, the weight calculation unit 104 may use an opening rate calculated according to time, location, who was with whom, etc. The weight calculation unit 104 may calculate the opening rate using the opening DB 104a.

[0049] The message generation unit 105 is a unit that generates a nudge message based on the opening rate calculated by the weight calculation unit 104 and transmits the message to the user terminal 200. For example, the message generation unit 105 selects the nudge type with the highest opening rate and generates a message based on that.

[0050] In the present disclosure, the message generation unit 105 generates a message according to the application of the user terminal 200. If the application is a healthcare application in the user terminal 200, a message related to walking is extracted from the nudge message DB 103b and generated. The receiving unit 101 also receives a target value and a current value (a target number of steps and a current number of steps in the case of walking) from the user terminal 200, and the message generation unit 105 generates a nudge message according to the received value as needed. Note that a target value, etc. is not necessarily required.

[0051] Furthermore, the message generating unit 105 has a function of receiving the opening result and the like from the user terminal 200 within a predetermined time after sending the message, and reflecting the result in the opening DB 104a.

[0052] In the present disclosure, when the user terminal 200 receives a message, the message is displayed as a banner, allowing the user to see part of the message. Therefore, in response to a nudge, the user may open the message to view the entire message.

[0053] 6 is a diagram showing a specific example of the nudge message DB 103b related to walking. As shown in the figure, the nudge message DB 103b stores, for one user action, a default message as well as a nudge type corresponding to the user's personality factor score. In FIG. 6, in addition to the default message encouraging walking, such as "Target walking time is 3910 steps," nudge types such as time pressure and monetary gain are also shown.

[0054] In FIG. 6, the default message simply indicates a target value. This target value is a value determined for each user based on the smartphone log data received by the receiving unit 101. When indicating a target value for the number of steps, the target value is the number of steps taken in that day minus the number of steps taken up to that point. The target value is determined based on the average or median of the user's daily actions, and may be, for example, the average number of steps.

[0055] Next, the learning process of the personality factor score estimation model 102a will be explained. Fig. 7 is a schematic diagram showing a learning device 120 that learns this personality factor score estimation model 102a. As shown in the figure, the learning device 120 has a learning unit 102b, a personality factor score DB 102c, and a smartphone log DB 102d, and uses these to generate the personality factor score estimation model 102a.

[0056] The personality factor score DB 102c is a database that stores personality factor scores for each user. These personality factor scores are learning data stored in the personality factor score DB 102c. This information is acquired in advance for each user through a questionnaire or the like. FIG. 8 is a diagram showing a specific example of the personality factor score DB 102c. As shown in the diagram, scores are assigned to each user and to each subscale of the personality factor score.

[0057] The smartphone log DB 102d also stores smartphone log data for each user. As described above, the smartphone log data indicates the user's attribute information, application log, location information, etc. The smartphone log DB 102d stores data for each predetermined time period.

[0058] The learning unit 102b uses smartphone log data for a predetermined time period as an explanatory variable and personality factor scores as a target variable, and performs learning using known machine learning to generate a personality factor score estimation model 102a.

[0059] These components are included in the learning device 120, which updates the personality factor score estimation model 102a at predetermined times.

[0060] Next, the learning process of the opening estimation model 103a will be described. FIG. 9 is a block diagram of a learning device 130 that performs the learning process of the opening estimation model 103a. As shown in the figure, the learning device 130 includes a learning unit 103c, a personality factor score estimation model 102a, an attribute information DB 103e, and an opening DB 104a, and generates the opening estimation model 103a using these. The learning unit 103c uses the estimated values ​​for each user from the personality factor score estimation model 102a, the attribute information for each user stored in the attribute information DB 103e, and the delivery status (number of deliveries, delivery interval) of messages delivered to each user as explanatory variables, and whether or not the message has been opened as a target variable to generate the opening estimation model 103a using known machine learning. Note that while the personality factor score DB 102c may be used instead of the personality factor score estimation model 102a, using the personality factor score estimation model 102a allows for the use of information from a larger number of users as explanatory variables.

[0061] Furthermore, the learning unit 103c acquires the message delivery status and whether or not the message has been opened for each nudge type to learn for each nudge type. Then, the learning unit 103c performs machine learning for each nudge type using personality factor score information, attribute information, and the delivery status of the nudge message as explanatory variables and whether or not the message has been opened as a target variable, and generates multiple opening estimation models 103a corresponding to the nudge type.

[0062] As shown in Figure 5(b), the message delivery status is determined for each message and indicates the number of messages delivered and the delivery interval immediately prior to the delivered message. The number of messages delivered is the number of messages delivered within the past six months to one year, but this period is an example and is not limited to this. The delivery interval indicates the time interval from the most recently delivered message. If the most recently delivered message was one day ago, it would be written as 1 day, 24 hours, or 86,400 seconds. As long as the concept of time is understood, the notation format is not limited. Note that here, the number of messages delivered and the delivery interval indicate the number of messages delivered up to the most recently delivered message and the time interval between all nudge messages delivered immediately prior, regardless of the nudge type, but they may also apply to messages of the same nudge type.

[0063] This information on the delivery status is acquired based on the opening DB 104a shown in Fig. 5(a). That is, the opening DB 104a stores information on the status from receipt to opening of each message and whether or not the message has been opened, and the delivery status is acquired based on this information.

[0064] The attribute information DB 103e is a database that stores user attribute information.

[0065] The operation of the message sending device 100 configured as above will be described. Fig. 10 is a flowchart showing the operation of the message sending device 100. The receiving unit 101 receives smartphone log data from the user terminal 200 (S101). The personality factor score estimation unit 102 inputs the received smartphone log data into the personality factor score estimation model 102a, and estimates the personality factor scores of the user of the user terminal 200 (S102).

[0066] The opening estimation unit 103 inputs the personality factor scores of the user into the opening estimation model 103a, and estimates the opening rate for each nudge type (S103).

[0067] The weight calculation unit 104 performs weighting processing by multiplying the opening rate for each nudge type by the past opening rate of the user (S104).

[0068] The message generation unit 105 selects one nudge type based on the weighted open rate for each nudge type, generates a message according to that nudge type (S105), and transmits the message to the user terminal 200 (S106).

[0069] In this way, it is possible to send a nudge-type message that is easy for the user to open.

[0070] Next, a modified example will be described. In the above disclosure, the message sending device 100 has the personality factor score estimation model 102a and the opening estimation model 103a, but these may be integrated into one estimation model.

[0071] This estimation model is trained by machine learning using smartphone log data as an explanatory variable and whether or not a message has been opened as a target variable. As training data, a database is prepared for each user, storing smartphone log data and whether or not a message has been opened for each nudge type. This database is periodically uploaded from user terminal 200 or obtained from the open notification in response to the message being sent.

[0072] In the above disclosure, the personality factor scores are first calculated and the opening rate is calculated based on the calculated personality factor scores, but this modified example differs in that the personality factor scores are omitted. As shown in the above disclosure, it is believed that the method of estimating the opening rate after estimating the personality factor scores first provides better accuracy.

[0073] Furthermore, in the above disclosure, the open rate is calculated for each nudge type, and the nudge type with the highest open rate is found, and a message is generated and sent based on that, but this is not limited to this.

[0074] For example, since the open rate varies depending on the location of the user terminal 200, the time of message delivery, and the situation of the user terminal 200 (such as whether the user is with someone), the open rate may be estimated accordingly regardless of whether the message is a nudge message or not.

[0075] In the above disclosure, the opening estimation model 103a is prepared for each nudge type, and the opening rate is output for each nudge type, but this is not limited to this. For example, the opening estimation model 103a may be prepared for each location, time, or situation of the user terminal 200.

[0076] In this case, the learning process is performed for each time the message was delivered to the user, the location of the user terminal 200 at the time of delivery, and the situation of the user terminal 200 at the time of delivery (whether someone was with the user, etc.). The location may be broadly categorized, such as home, workplace, downtown, or other.

[0077] The learning unit 103c extracts information on whether the message has been opened for each of these locations, times, or situations.The learning unit 103c then generates an opening estimation model 103a by known machine learning using the personality factor score information, attribute information, and the delivery status (number of deliveries, delivery intervals) of the messages delivered to each user as explanatory variables, and whether the message has been opened as a target variable.

[0078] In this way, the opening estimation model 103a may be prepared for each distribution time, each location of the user terminal 200, or each situation of the user terminal 200, and the opening rate for each may be calculated.

[0079] Next, the effects of the message sending device 100 of the present disclosure will be described. In the message sending device 100 of the present disclosure, the receiving unit 101 receives smartphone log data of the user terminal 200. The message generating unit 105 performs a sending process of a sending message based on the smartphone log data and the message opening history (opening DB 104a) of the user terminal 200. Here, the sending process of a sending message includes generating an appropriate message or determining an appropriate timing for sending the message.

[0080] This configuration enables the sending process of a message that is easy for the user to open based on the history (whether or not the message has been opened, etc.) stored in the opening DB 104a. As a result, the rate at which the message is opened by the user increases. The opening history includes at least the user ID in the opening DB 104a and whether or not the message has been opened by that user, and other information is not necessarily required.

[0081] This message sending process includes generating a message based on the smartphone log data and the opening history. For example, message generating unit 105 selects a nudge based on the smartphone log data and the opening history (e.g., the highest opening rate), and generates and sends a message according to the nudge.

[0082] This configuration allows for the creation of messages that are easy for users to open. Depending on the message content (nudge type), a user may or may not open the message. By generating messages based on the opening history, it is possible to create messages that are easy for users to open.

[0083] The message sending process also includes a process of sending a message at a timing based on the smartphone log data and the message opening history. For example, the message generation unit 105 determines the timing based on the smartphone log data and the message opening history, and sends a predetermined message at that timing. The timing of opening may differ depending on the smartphone log data and the message opening history in the user terminal 200, and the timing of opening may differ depending on the user, such as a user who tends to open the message in the morning or a user who tends to open the message in the evening.

[0084] The message sending process also includes a process of sending a message at a timing based on the smartphone log data and the opening history, as well as the status of the user terminal 200. For example, the status of the user terminal 200 indicates the location of the user terminal, whether the user terminal is with another user, etc. The message generating unit 105 generates and sends a message at a timing according to the status.

[0085] In addition, the message generation unit 105 performs the message sending process based on an estimation model trained on smartphone log data and message opening history prepared for learning. This configuration enables the message sending process to be easily opened from the smartphone log. This estimation model may be trained only on the smartphone log and opening history, or may take other information into consideration.

[0086] For example, the estimation model includes a personality factor score estimation model 102a generated by machine learning using terminal log data prepared for learning as explanatory variables and the user's personality factor scores prepared for learning as objective variables.

[0087] The estimation model includes an opening estimation model 103a generated by machine learning using the user's personality factors prepared for learning as explanatory variables and the message opening history prepared for learning as a target variable. The personality factors include at least one of the BigFive, Health Locus of Control, and time discount rate.

[0088] The estimation model also outputs the open rate for each nudge type and for each other predetermined condition, and the message generator 105 generates a message to be sent (or sends it at a predetermined timing) based on the open rate from the estimation model. The predetermined conditions include the time of sending, the user's location at the time of sending, the user's situation (whether they are with someone), etc.

[0089] The message sending device 100 further includes a weight calculation unit 104 that acquires a past opening rate in the user terminal 200 and performs weighting processing on the opening rate based on the past opening rate. The message generation unit 105 performs sending processing on the outgoing message based on the weighted opening rate.

[0090] The message sending device of the present disclosure has the following configuration.

[0091] [1] an acquisition unit that acquires terminal log data of a user terminal; a message sending unit that performs a sending process of a message based on the terminal log data and the message opening history of the user terminal; A message sending device comprising:

[0092] [2] The transmission process includes: generating a message based on the terminal log data and the opening history; [1] The message sending device according to [1].

[0093] [3] The transmission process includes: a process of sending a message at a timing based on the terminal log data and the message opening history, The message sending device according to [1] or [2].

[0094] [4] The transmission process includes: Furthermore, the method includes a process of transmitting a message at a timing based on the state of the user terminal. [3] The message sending device according to [3].

[0095] [5] The message sending unit performing a transmission process for the outgoing message based on an estimation model trained on the terminal log data prepared for learning and the message opening history; A message sending device according to any one of [1] to [4].

[0096] [6] The estimation model is Furthermore, based on the user's personality factors, [5] The message sending device according to [5].

[0097] [7] The estimation model is The personality factor estimation model is generated by machine learning using the device log data prepared for learning as explanatory variables and the user's personality factors prepared for learning as objective variables. [6] The message sending device according to [6].

[0098] [8] The estimation model is The model includes a message opening estimation model generated by machine learning using the user's personality factors prepared for learning as explanatory variables and the message opening history prepared for learning as the objective variable. [6] The message sending device according to [6].

[0099] [9] The estimation model outputs an opening rate for each predetermined condition, the message sending unit generates the outgoing message based on the open rate from the estimation model. A message sending device according to any one of [5] to [8].

[0100]

[10] a weight calculation unit that acquires a past opening rate of the user terminal and performs a weighting process on the opening rate based on the past opening rate, the message sending unit performs a sending process of the outgoing message based on the weighted opening rate. [9] The message sending device according to [9].

[0101] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are connected directly or indirectly (for example, by wire, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0102] Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocation, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0103] For example, the message sending device 100 according to an embodiment of the present disclosure may function as a computer that performs processing of the message sending method of the present disclosure. Fig. 11 is a diagram illustrating an example of the hardware configuration of the message sending device 100, learning device 120, and learning device 130 (hereinafter referred to as message sending device 100) according to an embodiment of the present disclosure. The message sending device 100 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0104] In the following description, the term "device" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the message sending device 100 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

[0105] Each function of the message sending device 100 is realized by loading specified software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of reading and writing data in the memory 1002 and storage 1003.

[0106] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the personality factor score estimation unit 102, the opening estimation unit 103, and the weight calculation unit 104 described above may be realized by the processor 1001.

[0107] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the personality factor score estimation unit 102 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be made for other functional blocks. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may also be transmitted from a network via a telecommunications line.

[0108] The memory 1002 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store an executable program (program code), a software module, etc. for implementing a message transmission method according to one embodiment of the present disclosure.

[0109] Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray disc), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0110] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned receiving unit 101 and message generating unit 105 may be realized by the communication device 1004. The communication device 1004 may be implemented with a transmitting unit and a receiving unit that are physically or logically separated.

[0111] The input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (for example, a touch panel).

[0112] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0113] Furthermore, the message transmitting device 100 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0114] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0115] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0116] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0117] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0118] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched depending on the implementation. Furthermore, notification of predetermined information (e.g., notification that "X is true") is not limited to being done explicitly, but may be done implicitly (e.g., by not notifying the predetermined information).

[0119] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0120] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0121] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), these wired and / or wireless technologies are included within the definition of transmission media.

[0122] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0123] Note that terms explained in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0124] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0125] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0126] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," etc. may be used interchangeably.

[0127] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0128] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0129] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0130] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0131] Any reference to an element using a designation such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0132] When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, similar to the term "comprising." Furthermore, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0133] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0134] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different." [Explanation of symbols]

[0135] 100...Message sending device, 200...User terminal, 101...Receiving unit, 102...Personality factor score estimation unit, 103...Opening estimation unit, 104...Weight calculation unit, 105...Message generation unit, 102a...Personality factor score estimation model, 103a...Opening estimation model, 103b...Nudge message DB, 104a...Opening DB.

Claims

1. an acquisition unit that acquires terminal log data of a user terminal; a message sending unit that performs a sending process of a message based on the terminal log data and the message opening history of the user terminal; Equipped with The message sending unit performing a transmission process for the outgoing message based on an estimation model trained on the terminal log data, message opening history, and personality factors of the user prepared for learning; The estimation model is The model includes a message opening estimation model generated by machine learning using the user's personality factors prepared for learning as explanatory variables and the message opening history prepared for learning as the objective variable. Message sending device.

2. The transmission process includes: generating a message based on the terminal log data and the opening history; The message sending device according to claim 1 .

3. The transmission process includes: a process of sending a message at a timing based on the terminal log data and the message opening history, The message sending device according to claim 1 .

4. The transmission process includes: Furthermore, the method includes a process of transmitting a message at a timing based on the state of the user terminal. The message sending device according to claim 3.

5. an acquisition unit that acquires terminal log data of a user terminal; a weight calculation unit that acquires a past opening rate of the user terminal and performs a weighting process on the opening rate output from the estimation model based on the past opening rate; a message sending unit that performs a sending process of a message based on the weighted opening rate; Equipped with the estimation model is trained based on terminal log data and message opening history prepared for training, and outputs the opening rate for each predetermined condition. Message sending device.

Images