JP2000293540A - Information providing method and device therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide the useful information in response to the estimated activity of a user by extracting the unit activity history information on an information providing object to estimate the activity of this object and sending the information accordant with the estimated activity to the information providing object. SOLUTION: The activity of an information providing object is estimated from the extracted unit activity history information on the object and the information accordant with the estimated activity is sent to the object. In an information providing system 100, for example, the position of a user having a portable terminal device 20 is detected by a communication project device 30. An activity pattern analysis server 33 of the part 30 analyzes the activity pattern of the user from the position information. In this example, the server 33 uses an activity object stored in an activity pattern information storage part 34 in response to the request of a server 41 and estimates the activity of the user having the device 20 with the day of the week and the weather designated by a service provider 40 used as the key information.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an information providing method and an information providing apparatus, and more particularly to an information providing method and an information providing apparatus for predicting the behavior of a user having terminal equipment and providing useful information to the user. It is suitable.

[0002]

2. Description of the Related Art Conventionally, for example, PHS (Personal Handypho
ne System), the location registration signal transmitted from the PHS terminal device is received by the base station in the wireless zone where the PHS terminal is located, and the base station transmits the location registration signal to the PHS service control station from the base station. The station recognizes the position of the PHS terminal device for each wireless zone of the base station.

[0003] A system has been considered which uses the position information of the PHS terminal device to provide the PHS terminal device with various kinds of information according to the position of the PHS terminal device.

[0004]

However, in a system that provides information according to the current position of the terminal device, it is difficult to predict the behavior of the user who has the terminal device and provide the information according to the prediction. It is.

The present invention has been made in view of the above points, and has as its object to propose an information providing method and apparatus for providing useful information in accordance with a user's behavior prediction.

[0006]

According to the present invention, in order to solve the above-mentioned problem, discrete information of an action history of an information providing object is obtained, and based on the obtained discrete information, unit action history information of an information providing object is obtained. Is extracted, and based on the extracted unit action history information, the behavior of the information provision target is predicted, and information corresponding to the predicted behavior is transmitted to the information provision target. Useful information can be provided.

[0007]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below in detail with reference to the drawings.

(1) Principle of Behavior Prediction Method The behavior prediction method according to the present invention uses a discrete system model composed of four elements. The four elements that make up this discrete system model are, first, E
NTITY and, second, an EVENT that represents a cross section of the occurring phenomenon as an event captured without considering time consumption
And third, an AC representing the operation or action performed by ENTITY.
TIVITY, and fourthly, an EVENT (or ACTIV) that focuses on a certain ENTITY and relates to the ENTITY.
PRO representing a phenomenon including a time lapse by a column of (ITY)
And CESS.

If these elements are shown in a concrete example, as shown in FIG. 1, for example, as a behavior of a customer who purchases a ticket at a ticket issuing machine, the customer is referred to as ENTITY as a first element,
Arrival EVENT where the customer joins the queue of waiting customers at the ticketing machine as an EVENT, and the customer finishes the waiting queue and starts buying a ticket at the ticketing machine Start of service
EVENT and customers finish buying tickets at ticketing machines End of
service EVENT. The third factor, ACTIVITY, represents the act of a customer buying a ticket,
The fourth element, PROCESS, is EVE related to customers
This represents the NT column.

As described above, only the events (EVENT) having an important meaning with respect to the operation and action of the ENTITY (customer) are used, and the behavior of the customer is discretely modeled by the sequence (PROCESS). It is used for the behavior prediction.

Therefore, in this discrete system model, the time of an important event (EVENT) is counted, so that the time varies depending on the event that occurs and the time when the event occurs.

Here, with respect to a certain entity (ENTITY), basic modeling can be performed by the operation of the entity (ACTIVITY) and the processing period of the operation (the time when the ACTIVITY starts and ends). That is, as shown in FIG.
A basic model is composed of a column of CTIVITY and an entity (ENTITY).

In this basic model, the action of the subject is regarded as an action of a discrete change, and the time at which the action switches and the switch destination are expressed as a discrete model of the action pattern. The prediction is made.

Next, a method of forming a discrete system model of the behavior pattern used when predicting the behavior of the subject will be described. In the method of generating this discrete system model, EN
The subject that is TITY is, for example, a user, the actual behavior (position and movement) of the user is detected by a predetermined position detection unit, and the stay and movement states of the user are described later using the detected results. M
ove Extract as ACTIVITY.

That is, as shown in FIG. 3, the position of the user is detected by a predetermined position detecting means (described later) at predetermined time intervals (not necessarily at fixed time intervals). The time is stored as EVENT.

Then, ACTIVITY is extracted based on the accumulated EVENT. In this case, of the plurality of accumulated EVENTs, EVENT1 and EVE1
NT2 is at the same position, EVENT3 is EVENT2
Are different positions with respect to EVENT3 to EVE.
It is assumed that NT6 are different from each other. Also, EVE
EVENT7 is a different position from NT6 and EVE
It is assumed that NT7 to EVENT10 are at the same position.

[0017] In the detection result of each such EVENT,
Regarding EVENT1 and EVENT2, it can be seen that the user stays at the same place during this time zone. On the other hand, for EVENT3 to EVENT6, it can be seen that the user is moving during this time zone. Therefore, E
ACT intermediate time T11 between VENT2 and EVENT3
As the time for switching the IVITY, the EVENT1 and EVENT2 sides are set to Stay ACTIVITY indicating stay, and EVENT3 to EVENT6 are set to indicate user movement.
Move ACTIVITY. Also, EVENT7 ~
EVENT10 indicates that the user is staying at the same place during this time zone, and this indicates that
ACT intermediate time T12 between VENT6 and EVENT7
EVENT7 to E
Stay ACTIV indicating the user's stay on the VENT10 side
ITY. By the way, EVENT3 to EVENT6
The starting point in Move Activity is S
tay ACTIVITY is the place of stay, Move ACT
The destination point of IVITY is a stay point of Stay ACTIVITY following the Move ACTIVITY. As described above, the Move ACTIVITY is represented by the start point, the destination point, and the required time (time points T11 to T12).

In this manner, the EVENT column while the user is staying at the same position is collected into one Stay AC
TIVITY, and the EVENT column while the user is moving is collected into one Move ACTIVITY.
And

FIG. 4 shows a processing procedure for extracting the ACTIVITY from the EVENT. That is, in FIG. 4, when the action prediction system enters the processing procedure from step SP10, the access interval for taking in the EVENT which is the position of the user is set to 30 in the subsequent step SP11.
It is determined whether it is less than minutes. Here, EVENT taken at an access interval of 30 minutes or more is ACTIVE
In order to newly generate a TY, the reliability before and after the connection becomes insufficient. Therefore, in this case, the behavior prediction system obtains a negative result in step SP11, and proceeds to step SP12.

In step SP12, the behavior prediction system determines whether or not an existing Stay ACTIVITY corresponding to the EVENT taken at this time, that is, an existing Stay ACTIVITY exists at the same point. If a negative result is obtained here, this means that the EVE captured at this time is
NT indicates that it does not exist in the existing Stay ACTIVITY, and that the access interval is 30 minutes or more. At this time, the behavior prediction system proceeds to step SP1.
Then, the process goes to 3 to store the EVENT data at this time in a temporary data storage area (Tmp Box).

On the other hand, if a positive result is obtained in step SP12, this means that the access interval of the EVENT taken at this time is 30 minutes or more, and the existing
This indicates that a similar EVENT exists in Stay ACTIVITY. At this time, the behavior prediction system proceeds to step SP14, and sets the EVENT taken in at this time as one of the populations of the corresponding existing ACTIVITY. The data storage area (ACTIVITY Box) storing the ACTIVITY data is updated.

As described above, an EVENT or an EVENT column having an access time of 30 minutes or more is taken into a population only when there is an existing Stay ACTIVITY having a similar population.

If a positive result is obtained in step SP11, this means that the access interval of the EVENT taken at this time is within 30 minutes. S
Moving to P15, the ACTENT or EVENT column fetched at this time is determined based on the correspondence relationship (FIG. 3) with the EVENT immediately before and immediately after the EVENT or EVENT column.
It is determined whether it is Y or Move ACTIVITY.

Incidentally, as a judgment in step SP15, the behavior prediction system determines that the data interval between the head and the end of the EVENT column is 20 minutes or more and the same position (EVEN)
T), this action is regarded as a stay at a certain point (area). In addition, the behavior prediction system considers the distance and time interval between
Do not consider it as Move ACTIVITY.

Then, in step SP15, Stay
When a determination result of ACTIVITY is obtained, the behavior prediction system proceeds to step SP16, and the existing Stay ACTIVIT made of a similar population (EVENT) is obtained.
It is determined whether or not Y exists. If an affirmative result is obtained here, this indicates that there is an existing Stay ACTIVITY similar to Stay ACTIVITY by the EVENT or EVENT sequence fetched at this time, and at this time, the action prediction system performs step SP16.
Then, in Step SP15 described above, Stay AC
A data storage area (Stay ACTIVITY Box) storing Stay ACTIVITY data is updated by regarding the EVENT or EVENT sequence determined as TIVITY as one of the populations of the existing ACTIVITY corresponding thereto. As a result, the number of EVENTs (the number of mothers) of the Stay ACTIVITY is increased, and the Stay ACTIVITY is increased.
The probability of occurrence of ITY will increase.

On the other hand, if a negative result is obtained in step SP16, this means that the Stay ACTIVI by the EVENT or the EVENT sequence fetched at this time.
This indicates that there is no existing Stay ACTIVITY similar to TY. At this time, the behavior prediction system proceeds to step SP17 and uses the EVENT or EVENT sequence determined to be Stay ACTIVITY in step SP15 described above. Stay ACTIVITY
Create

On the other hand, in the above-mentioned step SP15, the EVENT column captured at this time is Move A
When a determination result of CTIVITY is obtained, the behavior prediction system proceeds to step SP18, and moves to the existing Move ACTIVIT including a similar population (EVENT column).
It is determined whether or not Y exists. If a positive result is obtained here, this indicates that the existing Move A similar to the Move ACTIVITY by the EVENT column captured at this time.
This indicates that CIVITY exists, and at this time, the action prediction system proceeds to step SP20 and replaces the EVENT column determined as Move ACTIVITY in step SP15 with the corresponding existing ACTIVI.
A data storage area storing Move ACTIVITY data as one of the populations of TY (Move ACTIVITY
Box). Thereby, the Move ACTIV
By increasing the number of mothers at the waypoint of ITY (that is, EVENT), the probability of occurrence of the Move ACTIVITY increases.

On the other hand, if a negative result is obtained in step SP18, this means that there is no existing Move ACTIVITY similar to the Move ACTIVITY by the EVENT column fetched at this time. The system proceeds to step SP19
Then, in the above-mentioned step SP15, Move AC
Using the EVENT column determined to be TIVITY, a new
Create Move ACTIVITY.

By the way, in step SP15, Stay
If the result of the determination of ACTIVITY or Move ACTIVITY is not obtained, the behavior prediction system proceeds to step S
Moving to P13, the EVENT data at this time is stored in a temporary data storage area (Tmp Box).

Thus, according to the procedure shown in FIG. 4, when the interval for taking in EVENT is within 30 minutes, the behavior predicting system determines whether it is Stay ACTIVITY or Move A.
The determination of whether or not it is CTIVITY is started.

As described above, two types of ACTIVITY (S
As shown in FIG. 5, the user's behavior pattern divided into (tay ACTIVITY and Move ACTIVITY) is composed of a first tier behavior pattern model consisting of Stay ACTIVITY and a second tier behavior pattern model consisting of Move ACTIVITY. I do.

The action pattern model of the second layer is a Move ACTI having a moving direction from the starting point to the destination.
The first layer Stay ACTIVITY exists at the place where these Move ACTIVITY are replaced.

Each Stay Activity of the first layer and each Move Activity of the second layer constitute an object including various information on the user's action. As shown in FIG. 6, each Stay ACTIVITY is information indicating a point, and a start time at which a stay is started at the point (FIG. 3).
, The end time of the stay at the point (corresponding to the point T11 in FIG. 3), the key information (Key) such as the date, day of the week, and weather when the user's location information was sampled, and the Stay ACTIVITY. ACT before
Before ACTIVITY information that indicates IVITY (there is one or more ACTIVITY),
Represents ACTIVITY following ACTIVITY Next
ACTIVITY information (one or more of each ACTIVI
TY exists), number of mothers (EVENT number and ACT
IVITY), Stay ACTIV
The service used by the user in ITY and the user's preference information represented by the number of times, the Stay ACTI
It has information on the location of VITY (town information and the like) and ENTITY information including a user name and the like.

Here, as shown in FIG. 7, each time a new EVENT or EVENT sequence is generated, the action prediction system determines whether there is an existing ACTIVITY having the EVENT or EVENT sequence as a component as shown in FIG. The determination is made according to the procedure described above. As this criterion, a matter of whether or not the EVENT point is the same is used. If the same ACTIVITY exists, the behavior prediction system sets the newly generated EVENT
Alternatively, an EVENT sequence is added as a component of the existing ACTIVITY, and the number of the ACTIVITY matrix (EVEN
T number) (FIG. 6) is rewritten. In addition, the behavior prediction system determines the Before ACTIVITY of the ACTIVITY rewritten at this time in accordance with the EVENT generated at this time or the connection with the ACTIVITY before and after the EVENT column.
Rewrite the information or Next ACTIVITY information.

As shown in FIG. 8, each Move ACT
IVITY is information indicating a departure point, information indicating a destination point, a required time of the Move ACTIVITY (corresponding to time points T11 to T12 in FIG. 3), a date, a day of the week, and weather when user position information is sampled. Key information (Key), information on waypoints (EVENT) (the number of mothers at each point, and a plurality of patterns exist together with their occurrence probabilities), and the services used by the user in the Move Activity and the number of times. It has user preference information, information on the movement route of the Move ACTIVITY (town information and the like), and ENTITY information including a user name and the like.

Regarding this Move ACTIVITY,
As in the case of Stay ACTIVITY, each time a new EVENT or EVENT sequence is generated, the behavior prediction system determines whether there is an existing ACTIVITY having these EVENT or EVENT sequence as constituent elements. A condition for this determination is that the departure point and the destination point are the same. And the same ACTIVIT
If Y exists, the behavior prediction system replaces the newly generated EVENT or EVENT string with the existing ACTIV
In addition to the ITY components (via points),
The number of mothers (EVENT number) at the waypoint of IVITY (FIG. 8) is rewritten.

In this manner, Stay ACTIVITY
The object and the Move Activity object are newly generated EVENT or EVENT, respectively.
The column updates information such as the number of mothers. This number of mothers is used as an occurrence probability of ACTIVITY in behavior prediction described later.

Next, the accumulated Stay ACTIVITY
A method of predicting a user's action using an object and a Move ACTIVITY object will be described.

Each of the accumulated Stay Activity objects and Move Activity objects is
An ACTIVITY object having key information (Key) such as a day of the week and weather when each ACTIVITY occurs (FIGS. 6 and 8), and having key information (Key) matching the day of the week and weather to be predicted Is a selection target object.

Then, the behavior prediction system searches for an ACTIVITY object that is a prediction candidate using information included in each ACTIVITY object, such as a time zone to be predicted and a departure point, as a search key. For example,
When the user designates the time zone and the weather from 7:00 am to 5:00 pm on Sunday, the behavior prediction system determines from the stored ACTIVITY objects.
ACTIVI having the time zone and key information (Key)
Search for a TY object.

Then, the behavior prediction system calculates the plurality of retrieved ACTIVITY objects.
Multiple ACTIVIs based on the location information and context
Create a plurality of action patterns connecting TY objects. The context of the ACTIVITY object refers to the Before ACTIVITY information (FIG. 6) and the Next ACT of the Stay ACTIVITY object.
Using the CTIVITY information (FIG. 6), Move AC
In the TIVITY object, its start point and destination point (FIG. 8) are used.

For example, as shown in FIG. 9, when the user designates from 7:00 am to 5:00 pm on a sunny Sunday as a behavior prediction and starts at home, the behavior prediction system
Have the day and weather information as key information (Key)
Stay ACTIVITY object and Move ACT
An ACTIVITY object in a designated time zone is searched as a candidate from among the IVITY objects (FIG. 9A), and the searched candidate object AO1,
From the group N1 of AO2,..., AOn, for example, a Stay Activity object AO1 of "I am at home" is set as a start object (FIG. 9).
(B)).

Then, the behavior prediction system uses the following Activity objects as Move Activity objects AO2 starting from the home,
Specified in the Next Activity information of the Stay Activity object AO1 saying "I am at home"
Predict Move AACTIVITY object AO3 etc. A
Select as CTIVITY object.

Thus, each selected ACTIV
An ACTIVITY object that can follow the ITY object is selected. In this case, the selected ACTI
There may be a plurality of VITY objects, so that a plurality of predicted behavior patterns (PROCESS) are created by connecting the ACTIVITY objects.

As described above, the behavior prediction system uses each ACT.
Information indicating the context of the IVITY object (Before
By creating PROCESS using e-ACTIVITY information, Next-ACTIVITY information) and information indicating the connection (starting point, destination point), if it is an ACTIVITY object included in the time zone to be predicted,
Even if the occurrence times are different, the connection of the ACTIVITY objects having the connection characteristic of the user's action pattern can be obtained as a candidate for action prediction.

Incidentally, the behavior prediction system according to this embodiment defines two rules as rules for forming a row of ACTIVITY objects. The first rule is that the Stay Activity object and the Stay A
Move A between the CTIVITY object
Assume that a CTIVITY object exists. Thereby, it is possible to avoid occurrence of an unnatural behavioral break. And as a second rule, Move ACTIVE
Stay ACTIVITY before and after the TY object
Assume that both the object and the Move ACTIVITY object have the potential to be selected. However,
Successive Move Activity objects must be S
Suppose that it must be surrounded by a tay ACTIVITY object.

As described above, the behavior prediction system shown in FIG.
As shown in (B), a plurality of prediction patterns (PROCE
SS) can be created. By the way, each ACTIV
The ITY object has an occurrence probability depending on the number of EVENTs and the number of waypoints. The probability of this occurrence is
Other activity from the CTIVITY object
It is used as a probability when transitioning to an object, and as a result, the probability of occurrence of each PROCESS
It is obtained by the product of the occurrence probabilities of ITY.

FIG. 10 shows a result of predicting an action pattern including such an occurrence probability. In FIG. 10, ACTIV
The probability of transition from ITYa to ACTIVITYi is AC
The probability of occurrence of TIVITYi is 0.6, and the probability of transition from ACTIVITYi to ACTIVITYf is the probability of occurrence of ACTIVITYf of 0.
55. Therefore, for example, ACTIVITYa-AC
TIVITYi-ACTIVITYf-ACTIVIT
The probability of occurrence of PROCESS consisting of Yc-ACTIVITYk is determined by each of the ACTIVIs that constitute the PROCESS.
It is 0.33 which is the product of the occurrence probabilities of TY.

In this way, the behavior predicting system uses the AC retrieved by the set time zone and key information (Key).
A plurality of PROCESSes can be created from the TIVITY group based on the user's action history.

(2) Configuration of Behavior Prediction System FIG. 11 shows an information providing system 1 using the behavior prediction system.
0, the position of a user who has a portable terminal device 20 such as a PHS as a terminal device is detected by the communication business device unit 30, and the behavior pattern analysis server 33 of the communication business device unit 30 detects the position based on the position information. The user's behavior pattern is analyzed.

That is, as shown in FIG. 12, the portable terminal device 20 carried by the user
A transmission / reception circuit unit (RF) 20E for transmitting / receiving a signal to / from the U20A, the memory 20B, and the base station 21, and an RF (Radio Frequency) signal received by the transmission / reception circuit unit 20E is converted into a baseband signal and transmitted. An MMI (Man Machine Interface) which is an interface with a baseband processing unit 20D that converts a baseband signal into an RF signal, a microphone 20G, and a speaker 20H.
e) The unit 20F, the display unit 20I, and the keyboard 20J are connected to each other.

The CPU 20A executes various operations in accordance with the operation programs stored in the memory 20B, and controls each circuit in accordance with the operations. Various processing contents of the CPU 20A are displayed on a display unit 201 constituted by a liquid crystal display panel or the like as necessary.

When the user inputs a telephone number of a desired destination, the keyboard 20J supplies data representing the telephone number to the CPU 20A. The CPU 20A transmits a connection request to the communication destination represented by the telephone number input by the user via the transmission / reception circuit unit 20E. At this time, the communication line 22 connects the line according to the response of the called party. When the line is connected, the transmission / reception circuit unit 20E supplies the RF signal from the called party received via the antenna to the baseband processing unit 20D, where the RF signal is converted into a baseband signal. By supplying the converted baseband signal to the MMI unit 20F, the baseband processing unit 20D converts the received audio signal from the call destination into the speaker 2
Output as audio from 0H.

When the user inputs a voice through the microphone 20G, the MMI unit 20F turns on the microphone 2G.
The input audio signal supplied from 0G is supplied to the baseband processing unit 20D, where the baseband signal is converted into an RF signal. Then, the baseband processing unit 20D sends the converted RF signal to the communication line 22 via the transmission / reception circuit unit 20E, thereby transmitting the RF signal to a call destination connected to the line.

The CPU 20A transmits various information input by the user operating the keyboard 20J to the communication destination via the baseband processing unit 20D and the transmission / reception circuit unit 20E, and superimposes information from the communication destination. The obtained RF signal is captured via the transmission / reception circuit unit 20E and the baseband processing unit 20D, and displayed on the display unit 20I.

Thus, the user using the portable terminal device 20 can have a conversation with the other party and exchange various information.

Here, the CPU 20A of the portable terminal device 20
Transmits a position registration signal and identification information (ID information including a telephone number) of the mobile terminal device 20 to the base station 21 in the wireless zone where the mobile terminal device 20 is located at a predetermined timing. I have. The base station 21 transmits the position registration signal and the ID information transmitted from the mobile terminal device 20 to the access server 31 of the communication business device unit 30. As a result, the access server 31 sets the mobile terminal device 2
0 can be recognized for each wireless zone of the base station, and the obtained current position information of the portable terminal device 20 is stored together with the time information in the position information storage unit 35 composed of, for example, a plurality of hard disks. I do.

The current position information stored in the position information storage unit 35 is EVENT information (FIG. 3) in which the portable terminal device 20 is ENTITY (FIGS. 1 and 2). Therefore, each time a position registration signal is transmitted from the mobile terminal device 20, the position information storage unit 35 stores current position information indicating the position and time.

Here, the behavior pattern analysis server 3 of the behavior prediction system for creating an ACTIVITY object
Numeral 3 has a CPU and a memory connected to the data bus, and the CPU executes the activation object creation processing procedure shown in FIG. 4 according to a program stored in the memory. Then, the CPU stores the created ACTIVITY object in the action pattern information storage unit 34 including, for example, a plurality of hard disks.

The CP of the behavior pattern analysis server 33
U stores new current position information of the portable terminal device 20 (ie, EVENT or EVENT column) in the position information storage unit 35.
The update processing of the ACTIVITY object described above with reference to FIG. 7 is executed every time is stored or at a predetermined timing. As a result of this update processing, the ACTIVITY object stored in the behavior pattern information storage unit 34 increases the number of its mothers so that
The occurrence probability of the ITY object approaches a value reflecting the behavior pattern of the user, and a more accurate behavior pattern (ACTIVITY) can be obtained.

In the state where the behavior patterns of the user are stored in the behavior pattern information storage unit 34 as the ACTIVITY objects, the behavior pattern analysis server 33 stores the behavior pattern information in response to the request from the service provider 40. AC stored in the unit 34
Using the TIVITY object, the behavior of the user having the mobile terminal device 20 is predicted by the method described above with reference to FIGS.

The server 41 of the service provider 40
As shown in FIG. 13, C connected to the data bus BUS
PU 41A, memory 41B, communication interface 41
C and a database 41D, and the CPU 41A executes various processes in accordance with programs stored in the memory 41B.

That is, the CPU 41A receives provided information from various subscriber terminals (not shown) via a network connected by the communication interface 41C,
This is stored in the database 41D. Such provided information is particularly useful information for a user who is in a specific area or point, such as a movie theater screening guide or a transportation operation status. Accordingly, the CPU 41A of the server 41 determines the information of the user (that is, the portable terminal owned by the user) predicted to go to the specific area or the specific point based on the time and the weather condition for providing the provided information. The information of the behavior pattern (that is, PROCESS) is requested to the behavior pattern analysis server 33 of the communication business equipment unit 30.

The behavior pattern analysis server 33 responds to the request by storing the A stored in the behavior pattern information storage unit 34.
Using the CTIVITY object, the behavior of the user having the portable terminal device 20 is predicted using the day of the week and the weather designated by the service provider 40 as key information (Key) by the method described above with reference to FIGS.

In the behavior prediction processing, the behavior pattern analysis server 33 generates a predicted behavior pattern (PROCESS), which is a connection between the ACTIVITY objects. In this case, the behavior pattern analysis server 33 generates a plurality of predicted behavior patterns (PROCES) having different occurrence probabilities.
S) is generated.

The behavior pattern analysis server 33 uses the behavior prediction result to generate a relatively high occurrence of a user who is predicted to go to a specific area or point under the day of the week and the weather specified by the server 41 of the service provider 40. An action pattern (PROCESS) consisting of a probability is supplied to the server 41 of the service provider 40 together with information for specifying the user, that is, ID information such as a telephone number of the portable terminal device 20 owned by the user.

As a result, the CPU 41A of the server 41
Based on the behavior pattern supplied from the communication business device unit 30 and the user information thereof (information such as a telephone number specifying the mobile terminal device 20), the provided information read from the database for the mobile terminal device 20 is transmitted to the network FIG.
Parabolic antenna 25, satellite 24 and radio tower 23 shown in FIG.
Mobile terminal device 2 via a network comprising a communication line 22 and a network comprising a communication line 22 and a base station 21).
Send to 0.

In this case, the server 41 transmits the predicted action pattern (PROCES) supplied from the communication business equipment unit 30.
User preference information (services used and number of times) contained in each Stay ACTIVITY object that constitutes S)
Information that is likely to be particularly necessary in the user's predicted behavior based on the information about the user and the location, the user's preference information (services used and number of times) included in each Move ACTIVITY object, and information about the travel route. Read from the database prior to user action,
This is transmitted to the portable terminal device 20.

Incidentally, as a method of selecting information that is highly likely to be required by the user, the server 41 uses the user's preference information (service used and number of times) included in each ACTIVITY object supplied from the communication business equipment unit 30. Among them, a service whose number of times of use is higher than a predetermined threshold value is selected, and each ACTI is used.
Based on the information on the user's movement route and points included in the VITY object, information that is particularly effective on the user's behavior prediction route is selected and transmitted to the mobile terminal device 20.

Thus, the user possessing the portable terminal device 20 can enjoy in advance the information required for each time zone and place according to his / her preference in the action of the user. .

In the information providing system 10 using the behavior prediction system, the communication between the terminal devices (the portable terminal device 20 such as the PHS), the communication business device unit 30, and the behavior pattern analysis server 33 is performed. It uses an encryption that can be decrypted only by both parties. As this encryption method, a secret key (common key) encryption method or a public key encryption method is used.

The secret key cryptosystem is a system in which the transmitting side and the receiving side transmit and receive encrypted data using the same key, and do not disclose the key used for encrypting the data.
Specifically, there are DES (Data Encryption Standard) or triple DES, and these secret key systems have an effect that the encryption / decryption processing is fast.

Further, the public key method is divided into a secret key and a public key.
This is a method of encrypting data using a type of key, specifically, a method such as RSA, RC2 or RC4. In this public key cryptosystem, components (for example, the portable terminal device 2
0, communication business equipment unit 30, behavior pattern analysis server 3
When 3) increases, there is an effect that key management becomes easy.

(3) Operation and Effect of Embodiment In the above configuration, the behavior prediction system does not capture the behavior of the user as a continuous change model, but omits a time zone in which it is determined that the behavior is not important. Captured by the discretized model. The important event is a time at which the action is switched and a destination at which the action is switched, and one action unit such as a user's stay or movement is expressed as ACTIVITY.

By using the model discretized in this way, the calculation time can be reduced as compared with the case of using, for example, an equation of motion as a method of expressing a continuous phenomenon.

ACTIVIT which is such a discretized model
The Y object has a meaning specific to the user. For example, information that characterizes the behavior of the user, such as information about the services used, the number of times, and the location described above with reference to FIGS.
As a result, useful information can be provided to the user based on these pieces of information.

Further, ACTI which is such a discretized model is
Represents the user's stay behavior as a VITY object
Stay Activity object is its Before
The connection with the action before and after is characterized by the ACTIVITY information and the Next ACTIVITY information. In addition, the Move Activity object representing the user's movement behavior associates these points with the departure point information and the destination point information.

As described above, the behavior prediction system characterizes the behavior of the user by the connection of the discretized model (ACTIVITY object).
The prediction that the user will go to a specific place is predicted not on the exact time but on a behavior pattern (connection of the ACTIVITY objects) in a time zone having a predetermined width. As a result, it is possible to predict that the user goes to a specific place in daily activities such as taking the same behavior pattern even if the time is slightly shifted.

Therefore, by providing the user with useful information on the predicted behavior pattern of the user based on the prediction in advance, the user can request the service provider 40 to provide the information without having to request the service provider 40 to provide the information. , And desired useful information.

According to the above configuration, a discrete model (ACTIVITY object) characterizing the behavior of the user
By predicting the user's behavior pattern, the user's behavior can be more easily predicted.

By expressing the user's behavior model by objects, it is possible to provide useful information related to such information by giving the objects information such as the user's behavior details and the means of transportation. it can.

Further, as shown in FIG. 14, the behavior pattern analysis server 33 displays the predicted behavior of the user in a specific time zone on the display unit based on the information (point information, time information, and number of mothers) of the ACTIVITY object. Can be displayed in different colors with their occurrence probabilities. As a result, for example, at time t1, the user can easily confirm on the display screen a prediction result with the highest probability of being at the point indicated by P2 and subsequently having a high probability of being at the point indicated by P1.

(4) Other Embodiments In the above-described embodiment, as shown in FIG. 15, the position specifying function (the CPU 20A of the portable terminal device 20,
Base station 21 and access server 31) to mobile terminal device 2
0, the behavior pattern analysis server 33, the position information storage unit 35, and the behavior pattern information storage unit 34 are provided in the communication business device unit 30, and the service providing function (server 41)
Is described in the service provider 40, but the present invention is not limited to this. For example, as shown in FIG. 16, in addition to the location information storage unit 35 of the communication business device unit 30, An information storage unit 35 'may be provided. In this way, it works effectively when implementing a method of acquiring position information frequently. For example, a method is conceivable in which the mobile terminal device 20 accumulates a large amount of position information at certain time intervals and collectively transmits the position information to the communication business device unit 30 at predetermined timings. According to this method, the location information is transmitted to the communication business unit 30.
By reducing the number of times of transmission, the transmission processing can be simplified and the communication cost can be reduced.

As shown in FIG. 17, the communication business equipment unit 30 has a location specifying function 20 ′ and a service providing function (server 41), and the service provider 40 operates the behavior pattern analysis server 33 and the behavior pattern storage unit 3.
4 may be used.
0, at least one of the location specifying function, the behavior analysis server, the location information storage unit, the behavior pattern storage unit, and the service providing function may be provided in any of the communication business device unit 30 and the service provider 40.

FIG. 18 shows the arrangement pattern of each function and the respective effects.

In the above-described embodiment, the detection time for detecting the position of the portable terminal device 20 is 30 minutes as a boundary, and the Stay ACTIVITY or Move ACT is used.
Although the IVITY is determined, the present invention is not limited to this, and various time intervals can be applied.

In the above-described embodiment, a PHS terminal device is used as the portable terminal device 20, and a position for specifying the position of the PHS terminal device based on a position registration signal transmitted from the PHS terminal device to the base station. Although the case of using the specifying means has been described, the present invention is not limited to this. For example, a portable terminal device having a GPS (Global Positioning System) receiving unit for specifying a position based on correlation of signals from a plurality of satellites is used. , Various position specifying means can be applied.

Further, in the above-described embodiment, a case has been described in which various information regarding the user's action area is provided based on the result of the action prediction. However, the present invention is not limited to this, and the result of the action prediction and the current Various kinds of information can be applied as information to be provided, such as comparing the position with the position and notifying the user when the comparison result is different.

In the above-described embodiment, a case has been described in which a PHS terminal device is used as the form terminal device 20, but the present invention is not limited to this, and for example, a mobile phone, a P
Various terminals such as a DA (Personal Digital Assistant), a car navigation system, a personal computer, a portable television, and a portable radio can be applied.

In the above-described embodiment, the case where the service provider connected to the network is used has been described. However, the present invention is not limited to this, and other various information providing means can be used.

Further, in the above-described embodiment, a case has been described where portable terminal device 20 transmits a location registration signal to base station 21 at a predetermined timing. However, the present invention is not limited to this, and the user can use portable terminal device. A position registration signal may be transmitted by a user's intention by inputting a transmission command to the terminal 20.

Further, in the above-described embodiment, a case has been described in which information related to the user's behavior is transmitted to the portable terminal device 20 possessed by the user using the result of the user's behavior prediction. The present invention is not limited to this, and information related to the behavior information may be transmitted to the portable terminal device 20 by using mere past behavior information of the user.

[0093]

As described above, according to the present invention, discrete information of an action history of an information providing object is obtained, and unit action history information of an information providing object is extracted and extracted based on the obtained discrete information. Provide more useful information to the information provision target by predicting the behavior of the information provision target and transmitting information corresponding to the predicted behavior to the information provision target based on the unit action history information thus obtained. can do.

[Brief description of the drawings]

FIG. 1 is a schematic diagram for explaining a discrete system model according to the present invention.

FIG. 2 is a schematic diagram used for describing a method of expressing a discrete system model.

FIG. 3 is a schematic diagram illustrating a method of extracting ACTIVITY from an EVENT column.

FIG. 4 is a flowchart illustrating a method of extracting ACTIVITY from an EVENT column.

FIG. 5: Stay ACTIVITY and Move ACTIV
It is a basic diagram used for description of ITY.

FIG. 6 is a schematic diagram illustrating information assigned to a Stay ACTIVITY object.

FIG. 7 is a block diagram showing a method of updating ACTIVITY.

FIG. 8 is a schematic diagram illustrating information assigned to a Move ACTIVITY object.

FIG. 9 is a schematic diagram used to explain an action prediction process.

FIG. 10 is a schematic diagram illustrating a prediction result of a behavior pattern of a user.

FIG. 11 is a schematic connection diagram illustrating an overall configuration of an information providing system using the behavior prediction system according to the present invention.

FIG. 12 is a block diagram illustrating a configuration of a mobile terminal device.

FIG. 13 is a block diagram illustrating a configuration of a server.

FIG. 14 is a schematic diagram illustrating a display example of an action pattern.

FIG. 15 is a block diagram illustrating an example of the arrangement of components.

FIG. 16 is a block diagram showing an example of arrangement of components according to another embodiment.

FIG. 17 is a block diagram showing an example of the arrangement of components according to another embodiment.

FIG. 18 is a schematic diagram for describing effects of different arrangement examples of components.

[Explanation of symbols]

10 Information providing system, 20 Portable terminal device, 2
0A, 41A ... CPU, 20B, 41B ... memory
21 base station, 22 communication line, 30 communication business equipment unit, 31 access server, 33 action pattern analysis server, 34 action pattern storage unit, 35
Location information storage unit, 40 ... Service provider, 41 ...
…server.

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification symbol FI Theme coat ゛ (Reference) H04B 7/26 MF Term (Reference) 5B075 PP02 PP07 PQ02 PQ05 PQ23 PR03 5B085 BA06 BE07 5B089 GA11 GA25 GB03 HA11 KA18 KC48 KC53 5K067 AA21 BB04 BB21 EE02 EE10 FF02

Claims (9)

[Claims] 1. An information providing method for providing information to an information providing target, comprising: an action information generating step of generating the action information of the information providing target based on the action history of the information providing target; and the generated information. An information transmission step of transmitting information related to the behavior to the information provision target based on the provision target behavior information. 2. The action information generating step includes: an action history obtaining step of obtaining discrete information of the action history of the information providing target; and a unit action history of the information providing target based on the obtained discrete information. The method according to claim 1, further comprising: a unit action history information extracting step of extracting information; and an action prediction step of predicting the action of the information providing target based on the extracted unit action history information. How to provide information. 3. The information providing method according to claim 1, further comprising: a unit action history information accumulating step of accumulating the unit action history information; The information providing method according to claim 2, wherein the behavior is predicted. 4. The information providing method according to claim 1, wherein the information transmitting step transmits information related to the behavior to the prediction target prior to the behavior. 5. An information providing apparatus for providing information to an information providing target, comprising: action information generating means for generating the action information of the information providing target based on the action history of the information providing target; An information providing apparatus, comprising: information transmission means for transmitting information related to the behavior to the information provision target based on the behavior information of the provision target. 6. The action information generating means, the action history obtaining means for obtaining discrete information of the action history of the information providing object, and the unit action history of the information providing object based on the obtained discrete information. The method according to claim 5, further comprising: unit action history information extraction means for extracting information; and action prediction means for predicting the action of the information providing target based on the extracted unit action history information. Information providing device. 7. The action history acquisition means is provided in a terminal device carried by the information providing object, and the unit action history extraction means, the unit action history information storage means, and the action prediction means are provided in the terminal device. The information transmitting means is provided in a service information providing device connected to the communication business device and the terminal device by a predetermined line. The information providing device according to claim 6, wherein 8. The communication business device further comprises a storage unit for storing the action history information obtained by the action history obtaining unit, and the unit action history extracting unit stores the action history information stored in the storage unit. The information providing apparatus according to claim 7, wherein the unit action history is extracted based on the information. 9. The terminal device comprises a storage means for storing the action history information obtained by the action history obtaining means, wherein the unit action history extraction means is based on the action history information stored in the storage means. The information providing apparatus according to claim 7, wherein the unit action history is extracted by extracting the unit action history.